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"cells": [
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"cell_type": "markdown",
"id": "2d5449ae",
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"source": [
"# The Cognitive Divide: A Multivariate Analysis of Head Start Program Effects on Early Childhood Development\n",
"\n",
"**Author:** Andrew Magdy Kamal | **Date:** April 1, 2026\n",
"\n",
"---\n",
"\n",
"## Abstract\n",
"\n",
"This paper presents a comprehensive statistical analysis of the Head Start early childhood intervention dataset using Python 3.11 and open-source data science libraries including `pyreadstat`, `pandas`, `scipy`, `statsmodels`, `pingouin`, `scikit-learn`, `seaborn`, and `matplotlib`. The dataset contains cognitive, demographic, and socioeconomic information collected from 969 children at baseline and follow-up. Analyses span descriptive statistics, independent and paired-samples *t*-tests, one-way and factorial ANOVA, factorial ANCOVA, multiple regression, logistic regression, Monte Carlo simulation, exploratory factor analysis, and non-parametric testing. Results indicate that while raw PPVT follow-up scores did not differ significantly between Head Start and control children in unadjusted comparisons, program assignment was a significant predictor after covariate adjustment. Racial group membership and baseline cognitive ability emerged as strong predictors of follow-up vocabulary performance. These findings underscore the value of multivariate and covariate-adjusted approaches when evaluating the impact of early intervention programs.\n",
"\n",
"**Keywords:** Head Start, Peabody Picture Vocabulary Test, PPVT, early childhood intervention, ANOVA, ANCOVA, multiple regression, non-parametric analysis, Python, pyreadstat, statistical analysis"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "bcdaf2e3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pyreadstat in .\\.venv\\Lib\\site-packages (1.3.3)\n",
"Requirement already satisfied: pandas in .\\.venv\\Lib\\site-packages (3.0.2)\n",
"Requirement already satisfied: narwhals>=2.10.1 in .\\.venv\\Lib\\site-packages (from pyreadstat) (2.18.1)\n",
"Requirement already satisfied: numpy in .\\.venv\\Lib\\site-packages (from pyreadstat) (2.4.4)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in .\\.venv\\Lib\\site-packages (from pandas) (2.9.0.post0)\n",
"Requirement already satisfied: tzdata in .\\.venv\\Lib\\site-packages (from pandas) (2025.3)\n",
"Requirement already satisfied: six>=1.5 in .\\.venv\\Lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install pyreadstat pandas"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "98332024",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import pyreadstat\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "1b843393",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns: 20\n"
]
},
{
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"7 reading\n",
"8 momed\n",
"9 rooms\n",
"10 famsize\n",
"11 ppvt1\n",
"12 cldwell1\n",
"13 motrinh1\n",
"14 block1\n",
"15 ppvt2\n",
"16 cldwell2\n",
"17 motrinh2\n",
"18 block2\n",
"19 newses"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sav_path = Path('hdstart.sav')\n",
"if not sav_path.exists():\n",
" raise FileNotFoundError(f'Could not find: {sav_path.resolve()}')\n",
"\n",
"# Read headers/metadata only\n",
"_, meta = pyreadstat.read_sav(sav_path, metadataonly=True)\n",
"headers = meta.column_names\n",
"\n",
"print(f'Number of columns: {len(headers)}')\n",
"pd.DataFrame({'column_name': headers}).head(20)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "25fca8ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"Data shape: 969 rows x 20 columns\n"
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},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read full dataset\n",
"df, meta_full = pyreadstat.read_sav(sav_path)\n",
"\n",
"print(f'Data shape: {df.shape[0]} rows x {df.shape[1]} columns')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "94a5c04f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Headers exported to: C:\\Users\\kamal\\Downloads\\Stats_2_Project\\hdstart_headers.csv\n"
]
},
{
"data": {
"text/html": [
"\n",
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" column_name\n",
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},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Export headers to CSV\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"import pyreadstat\n",
"\n",
"# If headers are not in memory, load them from the SAV metadata\n",
"if 'headers' not in globals():\n",
" sav_path = Path('hdstart.sav')\n",
" _, meta = pyreadstat.read_sav(sav_path, metadataonly=True)\n",
" headers = meta.column_names\n",
"\n",
"headers_df = pd.DataFrame({'column_name': headers})\n",
"headers_csv_path = Path('hdstart_headers.csv')\n",
"headers_df.to_csv(headers_csv_path, index=False)\n",
"\n",
"print(f'Headers exported to: {headers_csv_path.resolve()}')\n",
"headers_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "50712604",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exported 969 rows to: C:\\Users\\kamal\\Downloads\\Stats_2_Project\\hdstart.csv\n"
]
},
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" 35.0 \n",
" 16.0 \n",
" 38.65 \n",
" 1.0 \n",
" -0.87 \n",
" \n",
" \n",
" 16 \n",
" 1010180.0 \n",
" 1.0 \n",
" 2.0 \n",
" 1.0 \n",
" 1.0 \n",
" 44.0 \n",
" 1.0 \n",
" 4.0 \n",
" 7.0 \n",
" 0.71 \n",
" 2.5 \n",
" 6.0 \n",
" 12.0 \n",
" 47.45 \n",
" 2.0 \n",
" 29.0 \n",
" 30.0 \n",
" 44.27 \n",
" 1.0 \n",
" 0.26 \n",
" \n",
" \n",
" 17 \n",
" 1010198.0 \n",
" 1.0 \n",
" 2.0 \n",
" 1.0 \n",
" 1.0 \n",
" 54.0 \n",
" 0.0 \n",
" 3.0 \n",
" 12.0 \n",
" 1.00 \n",
" 2.0 \n",
" 25.0 \n",
" 18.0 \n",
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" 4.0 \n",
" 43.0 \n",
" 40.0 \n",
" 47.27 \n",
" 6.0 \n",
" -1.29 \n",
" \n",
" \n",
" 18 \n",
" 1010206.0 \n",
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" 2.0 \n",
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" 50.0 \n",
" 1.0 \n",
" 2.0 \n",
" 12.0 \n",
" 0.83 \n",
" 2.0 \n",
" 32.0 \n",
" 17.0 \n",
" 42.41 \n",
" 2.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" -0.91 \n",
" \n",
" \n",
" 19 \n",
" 1010214.0 \n",
" 1.0 \n",
" 2.0 \n",
" 1.0 \n",
" 1.0 \n",
" NaN \n",
" 0.0 \n",
" 3.0 \n",
" 10.0 \n",
" 1.00 \n",
" 1.0 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" 43.0 \n",
" 30.0 \n",
" 44.97 \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id gender race site program age fthrpres reading momed \\\n",
"0 1010016.0 2.0 2.0 1.0 1.0 45.0 0.0 3.0 9.0 \n",
"1 1010024.0 2.0 2.0 1.0 2.0 44.0 1.0 3.0 6.0 \n",
"2 1010032.0 2.0 2.0 1.0 1.0 49.0 1.0 2.0 12.0 \n",
"3 1010057.0 1.0 2.0 1.0 3.0 50.0 0.0 0.0 9.0 \n",
"4 1010065.0 1.0 2.0 1.0 1.0 54.0 1.0 4.0 7.0 \n",
"5 1010073.0 2.0 2.0 1.0 2.0 47.0 1.0 1.0 12.0 \n",
"6 1010081.0 2.0 2.0 1.0 1.0 52.0 1.0 2.0 13.0 \n",
"7 1010099.0 1.0 2.0 1.0 1.0 48.0 0.0 4.0 12.0 \n",
"8 1010107.0 2.0 2.0 1.0 1.0 53.0 1.0 0.0 6.0 \n",
"9 1010115.0 2.0 2.0 1.0 2.0 NaN 1.0 1.0 8.0 \n",
"10 1010123.0 1.0 2.0 1.0 2.0 52.0 1.0 0.0 6.0 \n",
"11 1010131.0 1.0 2.0 1.0 3.0 51.0 0.0 3.0 12.0 \n",
"12 1010149.0 2.0 1.0 1.0 1.0 46.0 1.0 2.0 12.0 \n",
"13 1010156.0 2.0 2.0 1.0 1.0 45.0 0.0 3.0 9.0 \n",
"14 1010164.0 1.0 2.0 1.0 1.0 52.0 1.0 3.0 11.0 \n",
"15 1010172.0 1.0 2.0 1.0 2.0 51.0 0.0 3.0 8.0 \n",
"16 1010180.0 1.0 2.0 1.0 1.0 44.0 1.0 4.0 7.0 \n",
"17 1010198.0 1.0 2.0 1.0 1.0 54.0 0.0 3.0 12.0 \n",
"18 1010206.0 1.0 2.0 1.0 3.0 50.0 1.0 2.0 12.0 \n",
"19 1010214.0 1.0 2.0 1.0 1.0 NaN 0.0 3.0 10.0 \n",
"\n",
" rooms famsize ppvt1 cldwell1 motrinh1 block1 ppvt2 cldwell2 \\\n",
"0 1.50 1.0 15.0 21.0 50.74 3.0 30.0 32.0 \n",
"1 2.00 0.5 22.0 14.0 63.51 2.0 36.0 45.0 \n",
"2 0.50 1.0 7.0 23.0 43.54 4.0 33.0 42.0 \n",
"3 1.00 4.0 8.0 13.0 51.65 2.0 34.0 21.0 \n",
"4 0.80 1.5 15.0 11.0 40.44 3.0 44.0 27.0 \n",
"5 1.25 1.0 16.0 29.0 55.21 4.0 NaN NaN \n",
"6 0.83 1.0 11.0 24.0 51.28 1.0 40.0 38.0 \n",
"7 1.60 1.5 NaN 22.0 44.20 3.0 NaN NaN \n",
"8 0.90 4.0 37.0 30.0 46.50 2.0 47.0 58.0 \n",
"9 0.78 3.5 NaN NaN NaN NaN NaN NaN \n",
"10 0.90 4.0 14.0 28.0 52.70 3.0 NaN NaN \n",
"11 1.00 2.0 52.0 49.0 65.03 4.0 54.0 62.0 \n",
"12 0.56 3.5 24.0 25.0 49.27 3.0 NaN NaN \n",
"13 1.50 1.0 16.0 26.0 57.00 3.0 38.0 47.0 \n",
"14 0.57 2.5 18.0 13.0 51.14 5.0 45.0 50.0 \n",
"15 0.40 4.0 6.0 4.0 43.05 2.0 35.0 16.0 \n",
"16 0.71 2.5 6.0 12.0 47.45 2.0 29.0 30.0 \n",
"17 1.00 2.0 25.0 18.0 34.52 4.0 43.0 40.0 \n",
"18 0.83 2.0 32.0 17.0 42.41 2.0 NaN NaN \n",
"19 1.00 1.0 NaN NaN NaN NaN 43.0 30.0 \n",
"\n",
" motrinh2 block2 newses \n",
"0 47.84 3.0 -1.29 \n",
"1 50.33 8.0 0.43 \n",
"2 57.42 4.0 -0.91 \n",
"3 43.85 4.0 -0.45 \n",
"4 46.74 3.0 -0.49 \n",
"5 NaN NaN 0.09 \n",
"6 49.07 4.0 0.09 \n",
"7 NaN NaN -1.25 \n",
"8 60.73 7.0 -0.28 \n",
"9 NaN NaN NaN \n",
"10 NaN NaN -0.91 \n",
"11 54.04 8.0 0.31 \n",
"12 NaN NaN 0.43 \n",
"13 52.23 3.0 -1.25 \n",
"14 52.14 2.0 -0.91 \n",
"15 38.65 1.0 -0.87 \n",
"16 44.27 1.0 0.26 \n",
"17 47.27 6.0 -1.29 \n",
"18 NaN NaN -0.91 \n",
"19 44.97 NaN NaN "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert full SAV data to CSV and preview first 20 rows\n",
"from pathlib import Path\n",
"import pyreadstat\n",
"import pandas as pd\n",
"\n",
"sav_path = Path('hdstart.sav')\n",
"df, _ = pyreadstat.read_sav(sav_path)\n",
"\n",
"csv_path = Path('hdstart.csv')\n",
"df.to_csv(csv_path, index=False)\n",
"print(f'Exported {len(df)} rows to: {csv_path.resolve()}')\n",
"\n",
"# Load and display first 20 rows from the CSV\n",
"df_csv = pd.read_csv(csv_path)\n",
"df_csv.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "f949339a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id float64\n",
"gender float64\n",
"race float64\n",
"site float64\n",
"program float64\n",
"age float64\n",
"fthrpres float64\n",
"reading float64\n",
"momed float64\n",
"rooms float64\n",
"famsize float64\n",
"ppvt1 float64\n",
"cldwell1 float64\n",
"motrinh1 float64\n",
"block1 float64\n",
"ppvt2 float64\n",
"cldwell2 float64\n",
"motrinh2 float64\n",
"block2 float64\n",
"newses float64\n",
"dtype: object\n",
"\n",
"Shape: (969, 20)\n",
"\n",
"First few rows:\n"
]
},
{
"data": {
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"text/plain": [
" id gender race site program age fthrpres reading momed \\\n",
"0 1010016.0 2.0 2.0 1.0 1.0 45.0 0.0 3.0 9.0 \n",
"1 1010024.0 2.0 2.0 1.0 2.0 44.0 1.0 3.0 6.0 \n",
"2 1010032.0 2.0 2.0 1.0 1.0 49.0 1.0 2.0 12.0 \n",
"\n",
" rooms famsize ppvt1 cldwell1 motrinh1 block1 ppvt2 cldwell2 \\\n",
"0 1.5 1.0 15.0 21.0 50.74 3.0 30.0 32.0 \n",
"1 2.0 0.5 22.0 14.0 63.51 2.0 36.0 45.0 \n",
"2 0.5 1.0 7.0 23.0 43.54 4.0 33.0 42.0 \n",
"\n",
" motrinh2 block2 newses \n",
"0 47.84 3.0 -1.29 \n",
"1 50.33 8.0 0.43 \n",
"2 57.42 4.0 -0.91 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inspect dataset structure\n",
"print(df.dtypes)\n",
"print(\"\\nShape:\", df.shape)\n",
"print(\"\\nFirst few rows:\")\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "cef9bc17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install scipy scikit-learn statsmodels pingouin matplotlib seaborn -q"
]
},
{
"cell_type": "markdown",
"id": "aa6fd2de",
"metadata": {},
"source": [
"---\n",
"## Variable Overview\n",
"\n",
"| Role | Variable | Description |\n",
"|------|----------|-------------|\n",
"| **Independent (IV)** | `program` | Program assignment — 1 = Head Start, 2 = Control |\n",
"| **Independent (IV)** | `gender` | Child's gender — 1 = Male, 2 = Female |\n",
"| **Dependent (DV)** | `ppvt2` | Peabody Picture Vocabulary Test score at follow-up |\n",
"| **Dependent (DV)** | `block2` | Block Design cognitive score at follow-up |"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "3f3df072",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Working dataset: 487 rows × 18 cols\n"
]
},
{
"data": {
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" \n",
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"text/plain": [
" program gender ppvt2 block2 race age momed newses fthrpres \\\n",
"0 1.0 2.0 30.0 3.0 2.0 45.0 9.0 -1.29 0.0 \n",
"1 2.0 2.0 36.0 8.0 2.0 44.0 6.0 0.43 1.0 \n",
"2 1.0 2.0 33.0 4.0 2.0 49.0 12.0 -0.91 1.0 \n",
"\n",
" famsize ppvt1 cldwell1 motrinh1 block1 cldwell2 motrinh2 \\\n",
"0 1.0 15.0 21.0 50.74 3.0 32.0 47.84 \n",
"1 0.5 22.0 14.0 63.51 2.0 45.0 50.33 \n",
"2 1.0 7.0 23.0 43.54 4.0 42.0 57.42 \n",
"\n",
" program_bin gender_bin \n",
"0 1 0 \n",
"1 0 0 \n",
"2 1 0 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ── Imports & shared data prep ──────────────────────────────────────────────\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from scipy import stats\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"import statsmodels.formula.api as smf\n",
"import statsmodels.api as sm\n",
"import pingouin as pg\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Re-load clean copy dropping NaNs in analysis columns\n",
"ANALYSIS_COLS = ['program', 'gender', 'ppvt2', 'block2',\n",
" 'race', 'age', 'momed', 'newses', 'fthrpres',\n",
" 'famsize', 'ppvt1', 'cldwell1', 'motrinh1', 'block1',\n",
" 'cldwell2', 'motrinh2']\n",
"data = df[ANALYSIS_COLS].dropna().copy()\n",
"\n",
"# Recode program: 1=HeadStart → 1, 2=Control → 0 (needed for logistic reg)\n",
"data['program_bin'] = (data['program'] == 1).astype(int)\n",
"data['gender_bin'] = (data['gender'] == 1).astype(int) # 1=Male\n",
"\n",
"print(f\"Working dataset: {data.shape[0]} rows × {data.shape[1]} cols\")\n",
"data.head(3)"
]
},
{
"cell_type": "markdown",
"id": "37a53098",
"metadata": {},
"source": [
"---\n",
"## Parametric Analysis\n",
"Descriptive statistics and normality checks (Shapiro-Wilk) for the two dependent variables."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "92469dbb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==================================================\n",
"Variable: ppvt2\n",
" N=487, Mean=44.70, SD=8.06\n",
" Min=19.00, Max=60.00, Median=46.00\n",
" Skewness=-0.546, Kurtosis=-0.293\n",
" Shapiro-Wilk: W=0.9687, p=0.0000 → Not normal ✗\n",
"\n",
"==================================================\n",
"Variable: block2\n",
" N=487, Mean=5.47, SD=2.08\n",
" Min=0.00, Max=8.00, Median=6.00\n",
" Skewness=-0.416, Kurtosis=-0.717\n",
" Shapiro-Wilk: W=0.9172, p=0.0000 → Not normal ✗\n"
]
},
{
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Parametric Analysis ──────────────────────────────────────────────────────\n",
"for dv in ['ppvt2', 'block2']:\n",
" vals = data[dv].dropna()\n",
" stat, p = stats.shapiro(vals.sample(min(500, len(vals)), random_state=42))\n",
" print(f\"\\n{'='*50}\")\n",
" print(f\"Variable: {dv}\")\n",
" print(f\" N={len(vals)}, Mean={vals.mean():.2f}, SD={vals.std():.2f}\")\n",
" print(f\" Min={vals.min():.2f}, Max={vals.max():.2f}, Median={vals.median():.2f}\")\n",
" print(f\" Skewness={vals.skew():.3f}, Kurtosis={vals.kurtosis():.3f}\")\n",
" print(f\" Shapiro-Wilk: W={stat:.4f}, p={p:.4f} \"\n",
" f\"→ {'Normal ✓' if p > 0.05 else 'Not normal ✗'}\")\n",
"\n",
"fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
"for i, dv in enumerate(['ppvt2', 'block2']):\n",
" vals = data[dv].dropna()\n",
" axes[i, 0].hist(vals, bins=30, edgecolor='white', color='steelblue')\n",
" axes[i, 0].set_title(f'{dv} – Histogram')\n",
" sm.qqplot(vals, line='s', ax=axes[i, 1], alpha=0.4)\n",
" axes[i, 1].set_title(f'{dv} – Q-Q Plot')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ea82c152",
"metadata": {},
"source": [
"---\n",
"## Independent-Samples T-Test\n",
"Compare mean `ppvt2` and `block2` scores between Head Start (program=1) and Control (program=2) groups."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "1fcfcecb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=======================================================\n",
"T-Test for ppvt2 (Welch's)\n",
" Head Start: M=43.58, SD=7.81, n=231\n",
" Control: M=44.99, SD=8.08, n=163\n",
" t=-1.739, p=0.0829, Cohen's d=-0.178\n",
" → Not significant at α=0.05\n",
"\n",
"=======================================================\n",
"T-Test for block2 (Welch's)\n",
" Head Start: M=5.26, SD=1.97, n=231\n",
" Control: M=5.42, SD=2.15, n=163\n",
" t=-0.741, p=0.4591, Cohen's d=-0.076\n",
" → Not significant at α=0.05\n"
]
},
{
"data": {
"image/png": 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Zxy6W/v37N/m7jbsFlo9Luay+vkhKle+fH829vxt/DgBYdMxtjFRfnIuaOk81PsfHRbHYLs7dcxLdrGK2tbjQ19R4ifXFxafoFhhdAaPLf1OJja/yfOOiXsQVMV5liFl0o8tiJEvmRnTDi3N//F4kkqKLYcRfjbsZNo4BItaMmLO5YxsXMsvjh8YxbipWai5+am4YghgaIIZ/iGRbXIiL+Kg8Nmjj+Kl+4qkcI4W42NnU+rmJnxrHTiGGnyjHT81pKn6K17Hx848LpPG8FmT8FO+3iF3FT7QkxpSCKvNV+n9HkieuYEXf9BhzKoKUOKFHn/Z5ddppp+V9xPTAcxvozIsY/DGCtkhKnXzyyTnQi3GL5mXsoxh3aU5jNRQ5e0sc/8aJrriyuN1226X//ve/+WpaJG8iuIrxFiJRFUFcfTGmQ1OaW1++Utqc8uCckbAbOHBg3foI0sqBWrwWcfVxQR67eB831bZyVR8AzM+5pRKiiicu0sVYj5GUam7cy6ioijGIYpDvqGSvP+7Tgnq+0ZYYa/G6667LcVD8jOTG3M7wFzHI3GxbZPzU1GNF0jAGUY845vzzz88xS1xkjAqgmBiliPgpJraZMWNGg0qk5i7kzen5fNX3r/iJWqZSCirs9ddfb3A7Kn7iRFt/hrKmtgsxqGVU8dSvrIkKpEgwxMCdMYh2nHQbd92b00kxZnuLAROPPPLIJsuS5/U5RRvieTV+TnG1L55DDNAZyakNNtggD2i5sMUMb9HGxgFNlLSX7y//jJlX4spgffFc5lYM1B3P8bzzzsvHMsruIxjs1atXKkIk/kK5Iu2rKB+XGEizsVhXf+bGSHRFt8/Gmrsa2Nz7u/F7BoBFx9zGSPXFuaip81Tjc3xUIcV2kXSYk5idLiqlHnvssdyNLyY2aSzaFXFNxF8xO+7cDkUwr883kizRXS4u5kW1T7QrhiNoLvmyoESsGTFnc8c2LsqVL3bFMW4qVpqX+CkGGo+LlXfeeWceoiFmNo74qahkWcRPMTHOVxmEvCyOR7yOjV/fuHgcsdKc4qdIdkY82pTG+4zqrthW/ERLIikFFRYJoPrKYzfFDGz1Pf744+mf//xn3e0okY6y8BjXoH4gEifs6CYW3fZiiXEVGpcRl8ccaCppUJ7hL7rRxZWprzKTWlkETnGCbPyc4nZUOp111ll5HKf5mSFufkRgE+Xq9bs5RoAZxz7KnsuBZFyNjGD1qquuqtsugorGr9nslF+b+lfk4v8xu1wRojoqKrWuvPLK/H6Zn6uFZTHNckzLHONR1Z/C+J577smzx9SfhSiC/QhSy11LQ1xxjMq9pkRQHdVjZTFDYYw51vg9A8CiY25jpMbn+DiHRNxUv0tdnAfjD/XyOEnRjT0u4l166aVzdV6M+Oqmm27KFVNRQd74wlYMeRBxRVSZz8+MbfPyfOPxIyEVyZpIQhQRP0U8EzFnxBL1u4ZFYiWScDGGV8zKV46f4vjHrMtlUTE+LxfImoqfosveNddck4oQYzNFRdpRRx2VL5LNb+xUfk+GmI26vnKc3Th+euSRRxpsF+/d5iql4r76idWYfS9iWvETLYnue1Bhb7/9dp7qNsaDihN4lGHHFbDGYzhFCXic5CNhFN3EIugpd7OrL0qMIxiKwCmCsJgSt7HyuEuxr9hnnPhjOuQI4uIqX0yfHCXTjYOHGJy7/uCWzYmkWAQnMcB4BCtxEo5+9PUHDC+3NR43AsJoQ/2BxxemGBQypjCO7nPPPvtsDlIjcRYJk2hreRym6AoZSb0YiDKu7kUpd1yxi8Bqbsuw43ciwDjmmGNy0iUCthgLa05jGSxI8Z6K91c8nwhSIrCOK3GRmIsxJSL4mZvgJV6vSCDG6xqJu3i94vWNBFscwwjcymLK7Ai24v110EEH5UFfI5kVlXDlgVDri/dHvGciCIyEV7wO8T6clwFiAVg0Y6T6jj/++DwQeJzXIs6JmOR3v/td3lecf8vd7CPeiYtoMfh0xD9bbLFFjpvivBgTekRlc2NxHo2kSPxunM8jlghxzoq4LAbajmqiaGd9u+2221wNQj63zzcqyyMujIr4GM/pa1/7WirCr371qzyWUpyv4xjFeFxxDOK8HV0by+LcHW2Pi2KRrIvnHuOTxrhPEUPNTfwUCbDorjd48OC65FtcJIyLY81VDS1I8b6JKql4/Dj+Ea9uvPHGORaKC8Nx7Jsay6op8fsxeUwkkOKCcMRQ8Z6L92W8p7bZZpsGk93EQOmRNI3jFxf0oitoc8NVRBVVxOwxGU5UscX7MF6feB9Bi1Hp6f9gUVWeNvbll18u/c///E+eTjemnz300ENLn332WYNtY7shQ4aUrrvuutLqq69eat++fWmDDTZodjrb+++/P/9OTMf77rvvznL/F198kafrXXrppfM25a+C8vSyzS1x/+xEe2K7G2+8sTR06NDSMsssk6fi/fa3v91gOt76nnrqqfw722+//Twfu5iKd05TH6+99tpN3jdhwoTSgQceWOrRo0ee3njddddt8vnFY3zve9/Lr0+XLl1KBxxwQOnRRx/Nj3/TTTfVbRfT9MZ0zE2J13jQoEGljh075sc7+OCD8/TOjY9pc/to7nnE9MBxbOdGvKcuvPDC0oABA0qdO3cutWnTprTsssuWdtppp9L111+f3xONX8dbb721yX3dfPPN+f0X78OY3nmfffYpvffee7NsF+/XmLY5jm9MqxzTM8dzjHaX1Z9u+7zzziv17t0773eLLbZoMOU3AIuOeYmR4pwS55b63nzzzfx7Xbt2LXXo0KG0ySablO6+++5ZHufTTz8tnXjiiaU+ffqU2rZtm8+L8Xvx+43PUfWNGDEirz/mmGPy7Xj82cVPsZ8F9XzLzj777Pw7Z5xxxlwe1dnHRU3FnU355z//Wdphhx1yTLPEEkuUttlmm9Jjjz02y3bPPfdcPpfHOX2FFVYoDR8+vHTxxRfnfY8fP36uYpk777yz1L9///warrzyyqWzzjqr9Nvf/naWY9rcPpp6Hs29ps0ZN25c6dhjjy3169cvx7TxfCK22W+//UqPPPLIXMenM2bMKJ122ml177WIdyJW/vzzzxts9+WXX5aOO+64HC/G8Y1j/cYbb8zyPi/H7A8//HDpRz/6UX6/xGsSMdlHH300V88NqkWr+KfSiTFYFMWYTVHlFN2b5jRYd1xRimmCmyoxb+niClDMDhhXKxfGgOoLQ3Q1i6ueMWtN/cHDmXfRBSC6l55zzjm5mgwAmLOoUo4K5TiPzk21TjWIsUqjsiqqnhb2GFi17tprr82V6zEuawyvAC2ZMaWAiopS7BjHaX7HX1jYYpDL+qJPf4zxEGX7RZXLAwCURU3B1VdfnbuBVWtCqnH89NFHH6U//OEPuWuZhBRQnzGlgIqIWVVefvnl3L/+0EMPnauxFiohxkKIwCrGiYgxE2677bY8+84ZZ5xR6HTJAMCiLca8irEtH3zwwTy7b3MTmFSDiJu23nrrPOZVjD8ZSbQYU/Kkk06qdNOAKiMpBVQs2RNBSsxI0niw9mqy7bbbpvPOOy/dfffd6fPPP88DckelVCTSAACKEkM+xMDnXbt2TSeccEJVD2Yd8V1MIhMXH2MYiqguj8TUlltuWemmAVXGmFIAAAAAFM6YUgAAAAAUTlIKAAAAgMLV/JhSM2fOTGPHjk2dOnXK/ZkBAL7qzFdTpkxJvXr1Sq1b18b1PfESAFCJeKnmk1KRkOrdu3elmwEA1Jh33303rbDCCqkWiJcAgErESzWflIoKqfKB6Ny5c6WbAwC0cDGteVzwKscYtUC8BABUIl6q+aRUucteJKQkpQCABR1j1ALxEgBQiXipNgZCAAAAAKBFkZQCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAALHpJqTFjxqR99903de/ePS2++OJp3XXXTc8880zd/aVSKZ188slpueWWy/cPGjQovf766xVtMwBAtfjyyy/TSSedlPr06ZNjpVVXXTX98pe/zDEUAEA1q2hS6uOPP04DBw5Mbdu2Tffcc096+eWX03nnnZeWWmqpum3OPvvsdPHFF6crrrgiPfnkk2nJJZdMO+ywQ/r8888r2XQAgKpw1llnpcsvvzxdeuml6ZVXXsm3I3665JJLKt00AIDZapMqKIKm3r17p2uuuaZuXVzlK4srfBdeeGH6xS9+kXbZZZe87ve//33q2bNnuv3229N3v/vdirQbAKBaPPbYYzlO+va3v51vr7zyyunGG29MTz31VKWbBgBQvZVSd955Z9poo43SnnvumZZZZpm0wQYbpKuuuqru/rfffjuNHz8+d9kr69KlS9p0003T448/XqFWAwBUj69//evpgQceSK+99lq+/a9//Sv94x//SDvuuGOlmwYAUL2VUm+99VYuNz/66KPTCSeckJ5++ul0+OGHp3bt2qX9998/J6RCVEbVF7fL9zU2bdq0vJRNnjx5IT+L2hbHcuzYsZVuBvOgV69eqX379o4ZwCLi+OOPz/FO375902KLLZbHmBo2bFjaZ599mv0d8dKCJV5qecRLLcOHH36YpkyZkmrJ9OnT0wcffFDpZjAPll566ZyjqBWdOnVKPXr0SNWiokmpmTNn5kqpM844I9+OSqkXX3wxjx8VSan5MXz48HTaaact4JYuuiIhdeKJJ1a6GcyD+EOkfjdYAGrbLbfckq6//vp0ww03pLXXXjs9//zz6cgjj8x/dDcXT4mXFizxUssjXmoZCaljfvazNH3GjEo3BWpKu7Zt07nnnVc1iamKJqViRr1+/fo1WLfWWmulP/7xj/n/yy67bP45YcKEvG1Z3F5//fWb3OfQoUNz5VVZXDmMcauYPxHQxkm71sSsjyNGjEiHHHJIWn755VOtvWYALDqOPfbYXC1VHmszZjJ+5513cuKpuaSUeGnBEi+1POKl6hcVUpGQ2nfNbqnnEhX9s3WBmjGzlP77+ReVbgbzoFuHNqlt61Y1ccwmfPpFum7Uf/PnS1IqpTzz3qhRoxocpBgPYaWVVsr/j2qPSEzFOAnlJFQkmWIWvp/+9KdNHuTotqTr0oITx7KWq24iIVXLzw+A2vfpp5+m1q0bDhMa3fiiIr054qUFS7wEC08kpHp3rJ2uU2GVzobagLKKppyPOuqoPDhndN/ba6+98iwxV155ZV5Cq1atcvn5r371q7T66qvn5MFJJ52Ur2zsuuuulWw6AEBVGDx4cK5qXnHFFXP3veeeey6df/756Qc/+EGlmwYAUL1JqY033jiNHDkyl5CffvrpOel04YUXNhiY8+c//3maOnVq+tGPfpQmTpyYNt9883TvvfemDh06VLLpAABV4ZJLLskX7aJL+vvvv58v3v34xz9OJ598cqWbBgAwWxXvnLvTTjvlpTlRLRUJq1gAAJh1Fp24qBcLAEBL0nAAAgAAAAAogKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHBtin9IAGrRtGnT0tixYyvdDOZBr169Uvv27R0zAAAqQlIKgAUiElInnniio9mCDBs2LPXp06fSzQAAYBElKQXAAqu6iSRHLRkzZkwaMWJEOuSQQ9Lyyy+favE1AwCASpGUAmCBiG5gtVp1EwmpWn1uAABQKQY6BwAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAIAWbuWVV06tWrWaZRkyZEilmwYA0Kw2zd8FAEBL8PTTT6cvv/yy7vaLL76Ytttuu7TnnntWtF0AALMjKQUA0MItvfTSDW6feeaZadVVV01bbbVVxdoEADAnuu8BANSQ6dOnp+uuuy794Ac/yF34AACqlUopAIAacvvtt6eJEyemAw44oNltpk2blpeyyZMnpyJ9+OGHacqUKYU+JvNuzJgxDX5S3Tp16pR69OhR6WYAzBNJKQCAGnL11VenHXfcMfXq1avZbYYPH55OO+20VAmRkPrZMcekGdOnV+TxmXcjRoxw2FqAtu3apfPOPVdiCmhRJKUAAGrEO++8k/7617+m2267bbbbDR06NB199NENKqV69+5dQAtTrpCKhFTXgf1Tmy5LFvKYUOu+mDQ1TXz03/nzpVoKaEkkpQAAasQ111yTlllmmfTtb397ttu1b98+L5UUCam23btUtA0AQGUZ6BwAoAbMnDkzJ6X233//1KaN644AQPWTlAIAqAHRbW/06NF51j0AgJbAZTQAgBqw/fbbp1KpVOlmAADMNZVSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFC4NsU/JAAffvhhmjJligNR5caMGdPgJ9WtU6dOqUePHpVuBgAAc0lSCqACCaljfvazNH3GDMe+hRgxYkSlm8BcaNe2bTr3vPMkpgAAWghJKYCCRYVUJKT2XbNb6rmEr2FYECZ8+kW6btR/8+dLtRQAQMvgryGAComEVO+O7Rx/AABgkWSgcwAAAAAKJykFAAAAwKKVlDr11FNTq1atGix9+/atu//zzz9PQ4YMSd27d08dO3ZMe+yxR5owYUIlmwwAAABALVRKrb322mncuHF1yz/+8Y+6+4466qh01113pVtvvTU9/PDDaezYsWn33XevaHsBAAAAqIGkVJs2bdKyyy5bt5RnzJk0aVK6+uqr0/nnn5+23XbbtOGGG6ZrrrkmPfbYY+mJJ56odLMBAKrGmDFj0r777puryxdffPG07rrrpmeeeabSzQIAqO7Z915//fXUq1ev1KFDhzRgwIA0fPjwtOKKK6Znn302zZgxIw0aNKhu2+jaF/c9/vjjabPNNmtyf9OmTctL2eTJk1ORPvzwwzwdNdUfvNf/SXXr1KmTKd4BmvHxxx+ngQMHpm222Sbdc889aemll87x1VJLLeWYAQBVraJJqU033TRde+21ac0118xd90477bS0xRZbpBdffDGNHz8+tWvXLnXt2rXB7/Ts2TPf15xIasV+KiESUj875pg0Y/r0ijw+827EiBEOWwvQtl27dN6550pMATThrLPOSr17984V5WV9+vRxrACAqlfRpNSOO+5Y9//+/fvnJNVKK62Ubrnlllx6Pj+GDh2ajj766AaVUhGoFSEqpCIh1XVg/9Smy5KFPCbUui8mTU0TH/13/nyVu/cC8P+788470w477JD23HPPPAbn8ssvnw455JB08MEHO0wAQFWrePe9+qIqao011khvvPFG2m677dL06dPTxIkTG1RLxex7MfZUc9q3b5+XSoqEVNvuXSraBgBg0fDWW2+lyy+/PF+UO+GEE9LTTz+dDj/88Fxxvv/++1flcAfhi0mfFP6YUKtq+fM04dMZlW4C1IwJVfh5qqqk1CeffJLefPPN9P3vfz8PbN62bdv0wAMPpD322CPfP2rUqDR69Og89hQAACnNnDkzbbTRRumMM87Ih2ODDTbIQyFcccUVzSalKjncQdnER1+o6OMDLcN1oz6udBOAWk1KHXPMMWnw4MG5y97YsWPTKaeckhZbbLG09957py5duqSDDjooX/Xr1q1b6ty5czrssMNyQqq5Qc4BABY1yy23XOrXr1+DdWuttVb64x//WJXDHZR1HbhuatOlY6GPCbVcKVWrid5911wq9VyibaWbATVTKXVdlSV6K5qUeu+993IC6qOPPsozxWy++ebpiSeeyP8PF1xwQWrdunWulIoS8xgvwcDUAAD/v5h5L6rJ63vttdfyRb/qHu6go+EOgDmKhFTvju0cKahRFU1K3XTTTbO9v0OHDumyyy7LCwAAszrqqKPS17/+9dx9b6+99kpPPfVUuvLKK/MCAFDNWle6AQAAzL+NN944jRw5Mt14441pnXXWSb/85S/ThRdemPbZZx+HFQCoalU10DkAAPNup512ygsAQEuiUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFC4NsU/JABhwqczHAhYQHyeAABaHkkpgAq5btTHjj0AALDIkpQCqJB911wq9VyireMPC6hSSqIXAKBlkZQCqJBISPXu2M7xBwAAFkkGOgcAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQDQwp166qmpVatWDZa+fftWulkAALPVZvZ3AwDQEqy99trpr3/9a93tNm2EeQBAdROtAADUgEhCLbvsspVuBgDAXNN9DwCgBrz++uupV69eaZVVVkn77LNPGj16dKWbBAAwWyqlAABauE033TRde+21ac0110zjxo1Lp512Wtpiiy3Siy++mDp16jTL9tOmTctL2eTJkwtuMQCApBQAQIu344471v2/f//+OUm10korpVtuuSUddNBBs2w/fPjwnLgCAKgk3fcAAGpM165d0xprrJHeeOONJu8fOnRomjRpUt3y7rvvFt5GAABJKQCAGvPJJ5+kN998My233HJN3t++ffvUuXPnBgsAQNEkpQAAWrhjjjkmPfzww+k///lPeuyxx9Juu+2WFltssbT33ntXumkAAM0y0PlC8MWkTxbGbmGR5PMEMGfvvfdeTkB99NFHaemll06bb755euKJJ/L/AQCqlaTUQjDx0RcWxm4BAJp00003OTIAQIsjKbUQdB24bmrTpePC2DUskpVSEr0AAAC1R1JqYRzULh1T2+5dFsauAQAAAGqCgc4BAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAqIDPPvss/eMf/0gvv/zyLPd9/vnn6fe//73XBQCoaZJSAAAFe+2119Jaa62Vttxyy7TuuuumrbbaKo0bN67u/kmTJqUDDzzQ6wIA1DRJKQCAgh133HFpnXXWSe+//34aNWpU6tSpUxo4cGAaPXq01wIAWGRISgEAFOyxxx5Lw4cPTz169EirrbZauuuuu9IOO+yQtthii/TWW295PQCARcI8J6VGjBiRBg0alPbaa6/0wAMPNLjvww8/TKusssqCbB8AQE2OJ9WmTZu6261atUqXX355Gjx4cO7KF937AABq3TwlpS6++OJ07LHHpr59+6b27dunb33rW/kqX9mXX36Z3nnnnYXRTgCAmhGx1DPPPDPL+ksvvTTtsssuaeedd65IuwAAqjYp9etf/zpdddVVOWD6wx/+kB588MF0wQUXpJNPPnnhtRAAoMbstttu6cYbb2zyvoiz9t5771QqlQpvFwBA1Sal3n777fT1r3+97nb8/29/+1u68sor09ChQxdG+wAAak7ETX/+859nO1zCzJkzC20TAEBVJ6ViMM533323wbqYOSYSU9dcc036+c9/vqDbBwBQs6LqvDmXXXZZoW0BAKjqpNTmm2+ebrvttlnW9+vXLw96fs899yzItgEA1LTdd989Pfvss7Osv+iii1ShAwA1b56SUscff3zq379/k/etvfbauWLK+FIAAHPnnHPOSTvuuGN69dVX69add955OZ7605/+5DACADVtnpJSkZA68MAD0+jRo5scfDMSU3H//DjzzDPzdMhHHnlk3brPP/88DRkyJHXv3j117Ngx7bHHHmnChAnztX8AgGrzwx/+MB1zzDFp0KBB6T//+U8666yz0umnn57Hm9piiy0q3TwAgIWqzfz8Up8+fdK4cePSMsss02D9f//733zfl19+OU/7e/rpp/PMfo2rsI466qh8lfDWW29NXbp0SYceemguc3/00Ufnp9kAAFUnxuT86KOP0kYbbZRjqPvuuy9tttlmlW4WAEB1JqWiSiqqmhr75JNPUocOHeZpX/E7++yzT7rqqqvSr371q7r1kyZNSldffXW64YYb0rbbbpvXxWDqa621VnriiScEawBAi3TxxRfPsm755ZdPSyyxRNpyyy3TU089lZdw+OGHV6CFAABVmJQ6+uij889ISJ100kk5eCqLK3tPPvlkWn/99eepAdE979vf/nYuW6+flIpBP2fMmJHXl/Xt2zetuOKK6fHHH282KTVt2rS8lE2ePHme2gMAsDBdcMEFTa5fbLHFcjV4uSI84i1JKQCgls1TUuq5556rq5R64YUXUrt27erui/+vt956eVyEuXXTTTelf/7zn7n7XmPjx4/P++zatWuD9T179sz3NWf48OHptNNOm+s2AAAU6e23316o+49xOocOHZqOOOKIdOGFFy7UxwIAKCwp9eCDD+afMZh5TFXcuXPn+X7gd999NwdL999//zx3+ZudCMLKFV3lSqnevXsvsP0DAFSr5sbpBABo8bPvlX3jG99IbdrM13BUDbrnvf/+++lrX/ta3lcsDz/8cB5nIf4fFVHTp09PEydObPB7Mfvesssu2+x+27dvn5Nl9RcAgGoUMwvHjHuNnX322WnPPfec73E6l1pqqQXYSgCAKkpKxax4MfPe9773vTxl8bzOtldObEUXwOeff75uiVlnIpgq/79t27bpgQceqPudUaNGpdGjR6cBAwbMT7MBAKrKI488kr71rW/Nsn7HHXfM983vOJ0AAC3BfJU7jRs3Lt17773pxhtvTHvttVce8Dyu5kVC6etf//pc7aNTp05pnXXWabBuySWXTN27d69bf9BBB+WueN26dcsVT4cddlhOSJkmGagFEz79otJNgJrRUj9PUd1Uf4zOsrgwNy+TtcxunM5qnRjmi0lTC39MqFW1/Hlqqd/vUI0mVOHnab6SUtG9bqeddsrLp59+mkaOHJluuOGGtM0226QVVlghvfnmmwtsdprWrVvn0vYInHbYYYc0YsSIBbJvgEqJpHy7tm3TdaP+60WABSg+V/H5aknWXXfddPPNN6eTTz55liRTv379Fto4nZWcGCZeo7bt2qWJj/67Io8PtSo+Vy3tO3B2xEuwaMRLrUoxld5X9OGHH+bg6YorrkivvPLKfHXnW1jiyl+XLl3SpEmTFvr4UjGbzoknnph6fGtAatu9y0J9LFhUzPhoUvrwz4+nYcOGpT59+qRaEd+bU6ZMqXQzmIMxY8bkiyGHHHJIWn755R2vKhcBVo8ePVpUbHHXXXel3XffPQ+JsO222+Z1MXRBVKPfeuutadddd53jPm6//fa02267pcUWW6xuXcRirVq1yhf34sJe/fuaq5SKiWGKiJeC78CWwXdgy1LUd2CRfFe0DL4rWpZOVRYvzfdo5eUKqeuvvz4HTxHI7L333ul///d/53eXAIuMOBHUWuBYyyIhVUtJUarH4MGDc1LpjDPOyDHU4osvnmfO++tf/5q22mqreRqns76YKblv377puOOOmyUhVZ4YJpZK8R3YsvgOpFJ8V7QsviuYH/OVlPrud7+b7r777jyWVIwpddJJJxl8HABgPsTg5LHMr7kZpxMAoGaSUnHF7ZZbbsljPDV19Q0AgLn37LPP5iEQwtprr5022GADhw8AqHnzlZSKLnsAAHw177//fq5Af+ihh1LXrl3zuokTJ+bJY2K8zqWXXnq+9hv7AwCodq3n9xdjHKmYfW/VVVfNS/w/xj8AAGDuHHbYYXnSg5deein997//zcuLL76YBwc9/PDDHUYAoKbNV1IqZiL65je/mccwiCmIY4nR1L/1rW+lyy67bMG3EgCgBt177705rlprrbXq1vXr1y/HU/fcc09F2wYAUJXd92KGmAsuuCAdeuihdeviat7AgQPzfUOGDFmQbQQAqEkzZ85Mbdu2nWV9rIv7AABq2XxVSsVYB1Ep1dj222+fJk2atCDaBQBQ87bddttccT527Ni6dWPGjElHHXVU+sY3vlHRtgEAVGVSauedd04jR46cZf0dd9yRx5YCAGDOLr300jx+1Morr1w3TmefPn3yuksuucQhBABq2nx134uxDoYNG5ZndhkwYEBe98QTT6RHH300/exnP0sXX3xx3bYG6QQAaFrv3r3TP//5zzxZzKuvvprXxfhSgwYNcsgAgJo3X0mpq6++Oi211FLp5ZdfzktZTGUc95W1atVqkUxKfTFpaqWbADXD5wmodREvbbfddnkBAFiUzFdS6u233677f6lUqguoFnUxG2Hbdu3SxEf/XemmQE2Jz1V8vgBqQf2K8jlZFC/uAQCLjvlKSoWoiIoZ+F5//fV8e/XVV09HHnlk+uEPf5gWVT169EjnnXtumjJlSqWbwhzEILIxBfchhxySll9+ecerykVCKj5fALUg4qe5sahWnAMAi475SkqdfPLJ6fzzz0+HHXZY3ZhSjz/+eJ4pZvTo0en0009Pi6r4w9kfzy1HJKRiQFkAKEr9ivP6VJ8DAIua+Zp97/LLL09XXXVVGj58eJ6JL5b4/5VXXpmrTwAAmPvq83XWWSd16NAhL/H/3/zmNw4fAFDz5qtSasaMGWmjjTaaZf2GG26YvvjiiwXRLgCAmqf6HABYlM1XUur73/9+rpaKLnz1RaXUPvvss6DaBgBQ08rV53vvvXfduqhA79+/fx4mYVEeEgEAqH1faaDzv/zlL2mzzTbLt5988sk8ntR+++2Xjj766LrtGieuAAD4P6rPAYBF2XwlpV588cX0ta99Lf//zTffbDDAd9xXf9YYAACapvocAFiUzVdS6sEHH1zwLQEAWATUryiPC3gxqHlz1ecAALVsvrvvAQAw75577rlZJoppqvr8pZdecngBgJomKQUAUCAV5wAA/6f1//sJAAAAAIWRlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgBo4S6//PLUv3//1Llz57wMGDAg3XPPPZVuFgDAbElKAQC0cCussEI688wz07PPPpueeeaZtO2226ZddtklvfTSS5VuGgBAs9o0fxcAAC3B4MGDG9weNmxYrp564okn0tprr12xdgEAzI6kFABADfnyyy/TrbfemqZOnZq78TVl2rRpeSmbPHlygS0EAPg/uu8BANSAF154IXXs2DG1b98+/eQnP0kjR45M/fr1a3Lb4cOHpy5dutQtvXv3Lry9AACSUgAANWDNNddMzz//fHryySfTT3/607T//vunl19+uclthw4dmiZNmlS3vPvuu4W3FwBA9z0AgBrQrl27tNpqq+X/b7jhhunpp59OF110Ufr1r389y7ZRTRULAEAlqZQCAKhBM2fObDBuFABAtVEpBQDQwkV3vB133DGtuOKKacqUKemGG25IDz30ULrvvvsq3TQAgGZJSgEAtHDvv/9+2m+//dK4cePywOX9+/fPCantttuu0k0DAGiWpBQAQAt39dVXV7oJAADzzJhSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAAFq2k1OWXX5769++fOnfunJcBAwake+65p+7+zz//PA0ZMiR17949dezYMe2xxx5pwoQJlWwyAAAAAC09KbXCCiukM888Mz377LPpmWeeSdtuu23aZZdd0ksvvZTvP+qoo9Jdd92Vbr311vTwww+nsWPHpt13372STQYAAABgAWiTKmjw4MENbg8bNixXTz3xxBM5YXX11VenG264ISerwjXXXJPWWmutfP9mm21WoVYDAAAAUDNjSn355ZfppptuSlOnTs3d+KJ6asaMGWnQoEF12/Tt2zetuOKK6fHHH69oWwEAAABowZVS4YUXXshJqBg/KsaNGjlyZOrXr196/vnnU7t27VLXrl0bbN+zZ880fvz4Zvc3bdq0vJRNnjx5obYfAAAAgBZYKbXmmmvmBNSTTz6ZfvrTn6b9998/vfzyy/O9v+HDh6cuXbrULb17916g7QUAAACgBpJSUQ212mqrpQ033DAnlNZbb7100UUXpWWXXTZNnz49TZw4scH2Mfte3NecoUOHpkmTJtUt7777bgHPAgAAAIAWlZRqbObMmbn7XSSp2rZtmx544IG6+0aNGpVGjx6du/s1p3379qlz584NFgCAWhYX9jbeeOPUqVOntMwyy6Rdd901x00AANWsomNKRVXTjjvumAcvnzJlSp5p76GHHkr33Xdf7np30EEHpaOPPjp169YtJ5cOO+ywnJAy8x4AwP/v4YcfTkOGDMmJqS+++CKdcMIJafvtt89DIiy55JIOFQBQlSqalHr//ffTfvvtl8aNG5eTUP37988Jqe222y7ff8EFF6TWrVunPfbYI1dP7bDDDmnEiBGVbDIAQNW59957G9y+9tprc8VUzGa85ZZbVqxdAABVm5S6+uqrZ3t/hw4d0mWXXZYXAADmToyrGaLavClmKwYAqkHVjSkFAMBXG5/zyCOPTAMHDkzrrLNOk9uYrRgAqAaSUgAANSTGlnrxxRfTTTfd1Ow2ZisGANKi3n0PAIAF59BDD0133313euSRR9IKK6ww29mKYwEAqCRJKQCAFq5UKuVZikeOHJlnMu7Tp0+lmwQAMEeSUgAANdBl74Ybbkh33HFH6tSpUxo/fnxeH7MbL7744pVuHgBAk4wpBQDQwl1++eV5xr2tt946LbfccnXLzTffXOmmAQA0S6UUAEANdN8DAGhpVEoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAFq4Rx55JA0ePDj16tUrtWrVKt1+++2VbhIAwBxJSgEAtHBTp05N6623Xrrssssq3RQAgLnWZu43BQCgGu244455AQBoSSSlAAAWMdOmTctL2eTJkyvaHgBg0aT7HgDAImb48OGpS5cudUvv3r0r3SQAYBGkUgqABSKqLsaOHVtTR3PMmDENftaaGBS7ffv2lW4GFTB06NB09NFHN6iUkpgCAIomKQXAAhEJqRNPPLEmj+aIESNSLRo2bFjq06dPpZtBBUQyUkISAKg0SSkAFljVTSQ5aFmvGQAAVIqkFAALRFRdqLqByvjkk0/SG2+8UXf77bffTs8//3zq1q1bWnHFFb0sAEBVkpQCAGjhnnnmmbTNNtvU3S6PF7X//vuna6+9toItAwBonqQUAEALt/XWW6dSqVTpZgAAzJPW87Y5AAAAAHx1klIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSkAAAAACicpBQAAAEDhJKUAAAAAKJykFAAAAACFk5QCAAAAoHCSUgAAAAAUTlIKAAAAgMJJSgEAAABQuDbFPyQtybRp09LYsWNTrRkzZkyDn7WkV69eqX379pVuBgAAAMyWpBSzFQmpE088sWaP0ogRI1KtGTZsWOrTp0+lmwEAAACzJSnFHKtuIslBy3rNAAAAoNpJSjFb0Q1M1Q0AAACwoBnoHAAAAIDCSUoBAAAAsGglpYYPH5423njj1KlTp7TMMsukXXfdNY0aNarBNp9//nkaMmRI6t69e+rYsWPaY4890oQJEyrWZgAAAABaeFLq4YcfzgmnJ554It1///1pxowZafvtt09Tp06t2+aoo45Kd911V7r11lvz9jEb3O67717JZgMAAADQkgc6v/feexvcvvbaa3PF1LPPPpu23HLLNGnSpHT11VenG264IW277bZ5m2uuuSattdZaOZG12WabVajlAAAAANTMmFKRhArdunXLPyM5FdVTgwYNqtumb9++acUVV0yPP/54k/uYNm1amjx5coMFAAAAgOpSNUmpmTNnpiOPPDINHDgwrbPOOnnd+PHjU7t27VLXrl0bbNuzZ898X3PjVHXp0qVu6d27dyHtBwAAAKAFJqVibKkXX3wx3XTTTV9pP0OHDs0VV+Xl3XffXWBtBAAAAKAGxpQqO/TQQ9Pdd9+dHnnkkbTCCivUrV922WXT9OnT08SJExtUS8Xse3FfU9q3b58XAAAAAKpXRSulSqVSTkiNHDky/e1vf0t9+vRpcP+GG26Y2rZtmx544IG6daNGjUqjR49OAwYMqECLAQAAAGjxlVLRZS9m1rvjjjtSp06d6saJirGgFl988fzzoIMOSkcffXQe/Lxz587psMMOywkpM+8BsLDHOnz11VfrqnVjoo3Wraum1zsAALR4FU1KXX755fnn1ltv3WD9Nddckw444ID8/wsuuCD/EbDHHnvkmfV22GGHNGLEiIq0F4BFw1NPPZWuv/769MEHH9StW3rppdM+++yTNtlkk4q2DZpz2WWXpXPOOSdf5FtvvfXSJZdc4v0KAFS1NpXuvjcnHTp0yEFWLABQRELqoosuShtssEHuYh6zuMakGVHVG+uPOOIIf+hTdW6++eZcWX7FFVekTTfdNF144YX5Ql4Me7DMMstUunkAAE3SDwEA6nXZiwqpSEjFH/irr756vjgSP+N2rI/7YzuoJueff346+OCD04EHHpj69euXk1NLLLFE+u1vf1vppgEAVPfsewBQDWIMqeiyFxVSjcePits777xzOvXUU/N28Yc/VIOYqfjZZ59NQ4cObfB+HTRoUHr88ceb/J0YEiGWssmTJxfS1loVx3Ls2LGp1owZM6bBz1rSq1cvM3ZTEbX4fVHL3xXB98XCJSkFAP9PDGoeosteU8rry9tBNfjwww/Tl19+mXr27NlgfdyOBGpThg8fnk477bSCWlj74g/ME088MdWqWhzPddiwYbPM/A1FqOXvi1r8rgi+LxYuSSkA+H9ilr0QY0hFl73GYn397aCliqqq6JJav1KquWQsc3cVPf5ooWW9ZlCp957vi5bF98XCJSkFAP9P37598yx7Mah5/MFevwtfjCN155135vtjO6gWPXr0SIsttliaMGFCg/Vxe9lll23yd9q3b6/r0gIUx1PVDeD7Auadgc4BoHxSbN067bPPPum5557LA0e/9tpr6bPPPss/43asj/sbjzcFldSuXbu04YYbpgceeKBBEjVuDxgwoKJtAwCYHZVSAFDPJptsko444og8y14Mal4WFVKxPu6HahOVffvvv3/aaKON8nv0wgsvTFOnTs2z8QEAVCtJKQBoJP6ojz/uY5DoGNQ8xpCKLnsqpKhW3/nOd/LMkSeffHIaP358Wn/99dO99947y+DnAADVpFWpVCqlGhYDd3bp0iVNmjQpde7cudLNAQBauFqMLWrxOQEA1R9bGBQDAAAAgMJJSgEAAABQOEkpAAAAAAonKQUAAABA4SSlAAAAACicpBQAAAAAhZOUAgAAAKBwklIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAK1ybVuFKplH9Onjy50k0BAGpAOaYoxxi1QLwEAFQiXqr5pNSUKVPyz969e1e6KQBAjcUYXbp0SbVAvAQAVCJealWqpct8TZg5c2YaO3Zs6tSpU2rVqlWlm0MVZW0jUfnuu++mzp07V7o5QJXyXUFTInSKAKtXr16pdevaGAlBvERTfAcCc8N3BV8lXqr5Sql48iussEKlm0GVioSUpBTgu4J5VSsVUmXiJWZHvATMDd8VzE+8VBuX9wAAAABoUSSlAAAAACicpBSLpPbt26dTTjkl/wTwXQEgXgL8bUXxan6gcwAAAACqj0opAAAAAAonKQUAAABA4SSlWCQ89NBDqVWrVmnixImVbgpA5nsJqDa+l4Bq43up9klKUYgDDjgg7brrrlX9JfOvf/0r7bzzzmmZZZZJHTp0SCuvvHL6zne+k95///2F0tZrr702de3adYHsC0hp/Pjx6bDDDkurrLJKnsSgd+/eafDgwemBBx5YYIdn6623TkceeaTDDSwU4qVZiZdgwRIvUW3aVLoBUA0++OCD9I1vfCPttNNO6b777svJov/85z/pzjvvTFOnTl3gjzdjxowFvk9YlMXndeDAgfmze84556R11103f87i8zxkyJD06quvFtaWmD/kyy+/TG3aOMUCtUW8BC2beIlqpFKKqvOPf/wjbbHFFmnxxRfPlQ6HH354g8TQH/7wh7TRRhulTp06pWWXXTZ973vfq6tmKvvzn/+c1lhjjbyPbbbZJn8Bz86jjz6aJk2alH7zm9+kDTbYIPXp0yf/3gUXXJD/H78ft8NSSy2VK6biama499570+abb57/GO7evXtObL355pt1+47fje1vvvnmtNVWW+UqrOuvvz4deOCB+THjvlhOPfXUBXwkYdFxyCGH5M/RU089lfbYY4/8+V977bXT0UcfnZ544om8zejRo9Muu+ySOnbsmDp37pz22muvNGHChLp9xGdw/fXXz98xUSnZpUuX9N3vfjdNmTIl3x+f+YcffjhddNFFdZ/b+HyXqyjvueeetOGGG+YqrfgemzZtWv7+KldfxvfE008/XbFjBNQW8RIwr8RLVCNJKapKJHO++c1v5j8q//3vf+dETgRdhx56aN02Uf3wy1/+Mne3u/322/MfheUEUXj33XfT7rvvnrvtPP/88+mHP/xhOv7442f7uJHc+uKLL9LIkSNzlUNjkRz74x//mP8/atSoNG7cuPyHaYiEWfzh+8wzz+RuQq1bt0677bZbmjlzZoN9RBuOOOKI9Morr+QE14UXXpj/MI59xXLMMcd85eMHi6L//ve/OTkcFVFLLrnkLPdHwjg+j5GQim0jsXT//fent956K3fRbfwdFN8rd999d15i2zPPPDPfF5/5AQMGpIMPPrjucxvfDfU/47FtfMb79++ffv7zn+fvjd/97nfpn//8Z1pttdXSDjvskNsA8FWIl4B5JV6iapWgAPvvv39pscUWKy255JINlg4dOkQGqPTxxx/n7Q466KDSj370owa/+/e//73UunXr0meffdbkvp9++um8jylTpuTbQ4cOLfXr16/BNscdd1yDx2nKCSecUGrTpk2pW7dupW9+85uls88+uzR+/Pi6+x988ME57iN88MEHebsXXngh33777bfz7QsvvLDBdtdcc02pS5cus90XMGdPPvlk/ozddtttzW7zl7/8JX8HjR49um7dSy+9lH/vqaeeyrdPOeWU0hJLLFGaPHly3TbHHntsadNNN627vdVWW5WOOOKIBvsufzfcfvvtdes++eSTUtu2bUvXX3993brp06eXevXqlb9b5uU7BVh0iJfES7CwiJeoViqlKExUB0XlUv0lusvVF9VPMaBldK8pL1FZEFUOb7/9dt7m2WefzVVQK664Yu7CF13iyl1zQlQpbLrppg32G9UNczJs2LA88N8VV1yRu/3Ez759+6YXXnhhtr/3+uuvp7333jsPrhyVT9Htp357yqLLIbDgNVXd2Fh8L0RVU/3Kpn79+uUqqrivLD6/8b1Sttxyy83SPbg59T/jUcUQVZ0xzlVZ27Zt0yabbNLg8QAaEy+Jl2BhEC9RrYzCSmGiW010X6nvvffea3D7k08+ST/+8Y/zOCyNRRIquspFkiqWGJdp6aWXzsmfuD19+vSv3MYYE2rPPffMyxlnnJHHlzr33HNz95vmRIJspZVWSldddVXq1atXTqCts846s7SnqW5FwFe3+uqr5zGdFsRg5pE4qi/227grbnN8xoEFQbwkXoKFQbxEtVIpRVX52te+ll5++eWcvGq8tGvXLv/R+dFHH+VxW2Iw9KhkalzFsNZaa+XBjusrD3Q8L+LxVl111bpB1uN2iFm1yqItMcbUL37xizx7Xzz2xx9/PNf7r78vYP5069YtJ6Yvu+yyJmfLnDhxYv5sxnhzsZTFd03cFxVTc2tuP7fx3RHbxiQKZVE5FQOdz8vjATRFvATMK/ES1UpSiqpy3HHHpcceeywPbB7d+6Jr3B133FE30HlUS8UfepdcckkepPjOO+/Mg57X95Of/CT/3rHHHpsTRjfccEPuEjg7MaDxvvvum3++9tpr+feiQipm8YvBkUNUQ0XVRGwTUyJHVVfMxBfVVVdeeWV644030t/+9rc86PnciG5CsY8YHP3DDz9Mn3766XwfN1jURUIqkkXRPS4GF4/vgOgmd/HFF+fuu4MGDUrrrrtu2mefffKg45G43m+//XL333npWhuf2yeffDJPsBCf2+aqqKLS4ac//Wn+HopB2CMBFgOkx+f8oIMOWoDPHFgUiZeA+SFeoipVelArFp2BO3fZZZdZ1jc10G8MOrzddtuVOnbsmAdD79+/f2nYsGF1999www2llVdeudS+ffvSgAEDSnfeeWfex3PPPVe3zV133VVabbXV8jZbbLFF6be//e1sBxR+8803SwcffHBpjTXWKC2++OKlrl27ljbeeOM8GHl9p59+emnZZZcttWrVKj+ncP/995fWWmut/FjR1oceeig/1siRIxsMdF6/fWU/+clPSt27d8/3xyDLwPwbO3ZsaciQIaWVVlqp1K5du9Lyyy9f2nnnnfP3THjnnXfy7fhe6dSpU2nPPfdsMJlBfAbXW2+9Bvu84IIL8v7KRo0aVdpss83y90R8buPz3dyA5TE5w2GHHVbq0aNH/n4YOHBg3aDqwUDnQGPiJfESLGziJapNq/in0okxAAAAABYtuu8BAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAABROUgoAAACAwklKAQAAAFA4SSmgxRs/fnw64ogj0mqrrZY6dOiQevbsmQYOHJguv/zy9Omnn1a6eQAAFSdeAqpRm0o3AOCreOutt3ICqmvXrumMM85I6667bmrfvn164YUX0pVXXpmWX375tPPOO8/yezNmzEht27Z18AGAmideAqqVSimgRTvkkENSmzZt0jPPPJP22muvtNZaa6VVVlkl7bLLLulPf/pTGjx4cN6uVatWuXIqElRLLrlkGjZsWF4f61ZdddXUrl27tOaaa6Y//OEPdfv+z3/+k3/v+eefr1s3ceLEvO6hhx7Kt+Nn3I7H6t+/f67U2myzzdKLL75Y+LEAAGiKeAmoVpJSQIv10Ucfpb/85S9pyJAhOdHUlEgYlZ166qlpt912y1VUP/jBD9LIkSNzt7+f/exnOYn04x//OB144IHpwQcfnOe2HHvssem8885LTz/9dFp66aVzMiyqsQAAKkm8BFQzSSmgxXrjjTdSqVTKFU719ejRI3Xs2DEvxx13XN36733veznpFJVUK664Yjr33HPTAQcckK8errHGGunoo49Ou+++e14/r0455ZS03Xbb5e6Dv/vd79KECRNy0gsAoJLES0A1k5QCas5TTz2Vu9ytvfbaadq0aXXrN9poowbbvfLKK3k8qvridqyfVwMGDKj7f7du3XKibH72AwBQBPESUA0MdA60WDHbXnTPGzVqVIP1UQkVFl988Qbrm+vi15zWrf8vbx/VWGW65AEALYl4CahmKqWAFqt79+65y9yll16apk6dOs+/H4OiP/roow3Wxe1+/frl/8fYUGHcuHF199cf9Ly+J554ou7/H3/8cXrttdfy/gEAKkm8BFQzlVJAizZixIjc5S665sVA5jEDXlQ4xYDjr776atpwww1nOzh5zNi3wQYbpEGDBqW77ror3Xbbbemvf/1rXaVVzKR35plnpj59+qT3338//eIXv2hyX6effnoO+nr27JlOPPHEPK7VrrvuutCeNwDA3BIvAVWrBNDCjR07tnTooYeW+vTpU2rbtm2pY8eOpU022aR0zjnnlKZOnZq3ia+7kSNHzvK7I0aMKK2yyir599ZYY43S73//+wb3v/zyy6UBAwaUFl988dL6669f+stf/pL39eCDD+b742fcvuuuu0prr712qV27dvmx//WvfxX07AEA5ky8BFSjVvFPpRNjAC3VQw89lLbZZpvcZa9r166Vbg4AQNURLwHNMaYUAAAAAIWTlAIAAACgcLrvAQAAAFA4lVIAAAAAFE5SCgAAAIDCSUoBAAAAUDhJKQAAAAAKJykFAAAAQOEkpQAAAAAonKQUAAAAAIWTlAIAAACgcJJSAAAAAKSi/X/Mj78VQedmWwAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Independent-Samples T-Test ───────────────────────────────────────────────\n",
"hs = data[data['program'] == 1] # Head Start\n",
"ctrl = data[data['program'] == 2] # Control\n",
"\n",
"for dv in ['ppvt2', 'block2']:\n",
" t, p = stats.ttest_ind(hs[dv].dropna(), ctrl[dv].dropna(), equal_var=False)\n",
" # Cohens d\n",
" pooled_sd = np.sqrt((hs[dv].std()**2 + ctrl[dv].std()**2) / 2)\n",
" d = (hs[dv].mean() - ctrl[dv].mean()) / pooled_sd\n",
" print(f\"\\n{'='*55}\")\n",
" print(f\"T-Test for {dv} (Welch's)\")\n",
" print(f\" Head Start: M={hs[dv].mean():.2f}, SD={hs[dv].std():.2f}, n={len(hs[dv])}\")\n",
" print(f\" Control: M={ctrl[dv].mean():.2f}, SD={ctrl[dv].std():.2f}, n={len(ctrl[dv])}\")\n",
" print(f\" t={t:.3f}, p={p:.4f}, Cohen's d={d:.3f}\")\n",
" print(f\" → {'Significant *' if p < 0.05 else 'Not significant'} at α=0.05\")\n",
"\n",
"# Visualise\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"for ax, dv in zip(axes, ['ppvt2', 'block2']):\n",
" plot_data = data[['program', dv]].copy()\n",
" plot_data['Group'] = plot_data['program'].map({1: 'Head Start', 2: 'Control'})\n",
" sns.boxplot(x='Group', y=dv, data=plot_data, palette='Set2', ax=ax)\n",
" ax.set_title(f'{dv} by Program Group')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9cbe970e",
"metadata": {},
"source": [
"---\n",
"## One-Way ANOVA\n",
"Test whether mean `ppvt2` and `block2` differ significantly across racial groups (`race`)."
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "21d97cfe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=======================================================\n",
"One-Way ANOVA: ppvt2 ~ race\n",
"Source SS DF MS F p_unc np2\n",
" race 5416.930247 1 5416.930247 100.489668 1.277316e-21 0.171634\n",
"Within 26144.092340 485 53.905345 NaN NaN NaN\n",
"\n",
" F=100.490, p=0.0000, η²=0.172\n",
" → Significant * at α=0.05\n",
"\n",
" Tukey HSD post-hoc (p-column='p_tukey'):\n",
" A B mean_A mean_B diff se T p_tukey hedges\n",
"1.0 2.0 50.081481 42.630682 7.4508 0.743262 10.024454 2.241540e-13 1.013245\n",
"\n",
"=======================================================\n",
"One-Way ANOVA: block2 ~ race\n",
"Source SS DF MS F p_unc np2\n",
" race 217.017602 1 217.017602 55.798918 3.765331e-13 0.103179\n",
"Within 1886.300673 485 3.889280 NaN NaN NaN\n",
"\n",
" F=55.799, p=0.0000, η²=0.103\n",
" → Significant * at α=0.05\n",
"\n",
" Tukey HSD post-hoc (p-column='p_tukey'):\n",
" A B mean_A mean_B diff se T p_tukey hedges\n",
"1.0 2.0 6.548148 5.056818 1.49133 0.199646 7.469867 6.007417e-13 0.755034\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── One-Way ANOVA ────────────────────────────────────────────────────────────\n",
"for dv in ['ppvt2', 'block2']:\n",
" aov = pg.anova(data=data, dv=dv, between='race', detailed=True)\n",
" # detect p-value column (varies across pingouin versions)\n",
" p_col = next(c for c in aov.columns if c.lower().startswith('p'))\n",
" p = aov.loc[0, p_col]\n",
" F = aov.loc[0, 'F']\n",
" eta2 = aov.loc[0, 'np2'] if 'np2' in aov.columns else None\n",
" eta_str = f\", η²={eta2:.3f}\" if eta2 is not None else \"\"\n",
"\n",
" print(f\"\\n{'='*55}\")\n",
" print(f\"One-Way ANOVA: {dv} ~ race\")\n",
" print(aov.to_string(index=False))\n",
" print(f\"\\n F={F:.3f}, p={p:.4f}{eta_str}\")\n",
" print(f\" → {'Significant *' if p < 0.05 else 'Not significant'} at α=0.05\")\n",
"\n",
" # Post-hoc (only 2 race groups here, so Tukey gives one row)\n",
" if p < 0.05:\n",
" ph = pg.pairwise_tukey(data=data, dv=dv, between='race')\n",
" # detect p-value column in post-hoc table\n",
" ph_p_col = next((c for c in ph.columns if 'p' in c.lower()), ph.columns[-1])\n",
" print(f\"\\n Tukey HSD post-hoc (p-column='{ph_p_col}'):\")\n",
" print(ph.to_string(index=False))\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
"for ax, dv in zip(axes, ['ppvt2', 'block2']):\n",
" sns.boxplot(x='race', y=dv, data=data, palette='Set3', ax=ax)\n",
" ax.set_title(f'{dv} by Race')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b1734061",
"metadata": {},
"source": [
"---\n",
"## Factorial ANOVA (Two-Way)\n",
"Test main effects of `program` and `gender`, plus their interaction, on `ppvt2` and `block2`."
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "0e414e2f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=======================================================\n",
"Factorial ANOVA: ppvt2 ~ program × gender\n",
" sum_sq df F PR(>F)\n",
"C(program) 785.1123 2.0 6.1646 0.0023\n",
"C(gender) 28.4026 1.0 0.4460 0.5045\n",
"C(program):C(gender) 123.2664 2.0 0.9679 0.3806\n",
"Residual 30629.5770 481.0 NaN NaN\n",
"\n",
"=======================================================\n",
"Factorial ANOVA: block2 ~ program × gender\n",
" sum_sq df F PR(>F)\n",
"C(program) 45.8473 2.0 5.3746 0.0049\n",
"C(gender) 1.3989 1.0 0.3280 0.5671\n",
"C(program):C(gender) 4.3916 2.0 0.5148 0.5979\n",
"Residual 2051.5688 481.0 NaN NaN\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Factorial ANOVA (Two-Way) ─────────────────────────────────────────────────\n",
"for dv in ['ppvt2', 'block2']:\n",
" model = smf.ols(f'{dv} ~ C(program) * C(gender)', data=data).fit()\n",
" aov_table = sm.stats.anova_lm(model, typ=2)\n",
" print(f\"\\n{'='*55}\")\n",
" print(f\"Factorial ANOVA: {dv} ~ program × gender\")\n",
" print(aov_table.round(4))\n",
"\n",
"# Interaction plot for ppvt2\n",
"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
"for ax, dv in zip(axes, ['ppvt2', 'block2']):\n",
" means = data.groupby(['program', 'gender'])[dv].mean().reset_index()\n",
" means['Program'] = means['program'].map({1: 'Head Start', 2: 'Control'})\n",
" means['Gender'] = means['gender'].map({1: 'Male', 2: 'Female'})\n",
" sns.lineplot(data=means, x='Program', y=dv, hue='Gender',\n",
" marker='o', palette='Set1', ax=ax)\n",
" ax.set_title(f'Interaction Plot: {dv}')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f9f7832e",
"metadata": {},
"source": [
"---\n",
"## Logistic Regression\n",
"Predict whether a child was in the **Head Start** program (`program_bin`=1) from demographic and socioeconomic predictors: `gender`, `race`, `age`, `momed`, `newses`, `fthrpres`, `famsize`."
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "d598dace",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Results: Logit\n",
"=================================================================\n",
"Model: Logit Method: MLE \n",
"Dependent Variable: y Pseudo R-squared: 0.152 \n",
"Date: 2026-04-01 20:07 AIC: 587.3419 \n",
"No. Observations: 487 BIC: 620.8480 \n",
"Df Model: 7 Log-Likelihood: -285.67 \n",
"Df Residuals: 479 LL-Null: -336.92 \n",
"Converged: 1.0000 LLR p-value: 3.2843e-19\n",
"No. Iterations: 5.0000 Scale: 1.0000 \n",
"-------------------------------------------------------------------\n",
" Coef. Std.Err. z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------\n",
"const -0.1477 0.1026 -1.4405 0.1497 -0.3488 0.0533\n",
"x1 -0.0192 0.1013 -0.1899 0.8494 -0.2177 0.1792\n",
"x2 0.3514 0.1141 3.0807 0.0021 0.1278 0.5750\n",
"x3 0.1346 0.1022 1.3171 0.1878 -0.0657 0.3350\n",
"x4 -0.2224 0.1238 -1.7965 0.0724 -0.4651 0.0202\n",
"x5 -0.5660 0.1352 -4.1870 0.0000 -0.8309 -0.3010\n",
"x6 -0.1188 0.1202 -0.9879 0.3232 -0.3544 0.1169\n",
"x7 0.2407 0.1169 2.0584 0.0395 0.0115 0.4699\n",
"=================================================================\n",
"\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Control 0.70 0.70 0.70 256\n",
" Head Start 0.67 0.67 0.67 231\n",
"\n",
" accuracy 0.69 487\n",
" macro avg 0.68 0.68 0.68 487\n",
"weighted avg 0.69 0.69 0.69 487\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Logistic Regression ──────────────────────────────────────────────────────\n",
"PREDICTORS = ['gender', 'race', 'age', 'momed', 'newses', 'fthrpres', 'famsize']\n",
"TARGET = 'program_bin'\n",
"\n",
"lr_data = data[PREDICTORS + [TARGET]].dropna()\n",
"X = lr_data[PREDICTORS].values\n",
"y = lr_data[TARGET].values\n",
"\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# statsmodels Logit for coefficients + p-values\n",
"X_const = sm.add_constant(X_scaled)\n",
"logit_model = sm.Logit(y, X_const).fit(disp=0)\n",
"print(logit_model.summary2())\n",
"\n",
"# sklearn for classification metrics\n",
"clf = LogisticRegression(max_iter=1000, random_state=42)\n",
"clf.fit(X_scaled, y)\n",
"y_pred = clf.predict(X_scaled)\n",
"\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y, y_pred, target_names=['Control', 'Head Start']))\n",
"\n",
"# Coefficients bar chart\n",
"coef_df = pd.DataFrame({'Predictor': PREDICTORS, 'Coefficient': clf.coef_[0]})\n",
"coef_df = coef_df.reindex(coef_df['Coefficient'].abs().sort_values(ascending=True).index)\n",
"fig, ax = plt.subplots(figsize=(8, 5))\n",
"ax.barh(coef_df['Predictor'], coef_df['Coefficient'],\n",
" color=['tomato' if c < 0 else 'steelblue' for c in coef_df['Coefficient']])\n",
"ax.axvline(0, color='black', linewidth=0.8)\n",
"ax.set_title('Logistic Regression – Standardised Coefficients')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "755805ad",
"metadata": {},
"source": [
"---\n",
"## Monte Carlo Simulation\n",
"Bootstrap the sampling distribution of the **mean difference in `ppvt2`** between Head Start and Control groups (10 000 resamples) to estimate a 95 % confidence interval."
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "4974a067",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Observed mean difference (Head Start − Control): -1.418\n",
"Bootstrap 95% CI: [-3.024, 0.164]\n",
"→ CI includes 0 → Not significant\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Monte Carlo Simulation (Bootstrap) ──────────────────────────────────────\n",
"rng = np.random.default_rng(42)\n",
"n_sims = 10_000\n",
"hs_ppvt = data.loc[data['program'] == 1, 'ppvt2'].values\n",
"ctrl_ppvt = data.loc[data['program'] == 2, 'ppvt2'].values\n",
"\n",
"# Observed mean difference\n",
"obs_diff = hs_ppvt.mean() - ctrl_ppvt.mean()\n",
"\n",
"# Bootstrap resampling\n",
"boot_diffs = np.empty(n_sims)\n",
"for i in range(n_sims):\n",
" s_hs = rng.choice(hs_ppvt, size=len(hs_ppvt), replace=True)\n",
" s_ctrl = rng.choice(ctrl_ppvt, size=len(ctrl_ppvt), replace=True)\n",
" boot_diffs[i] = s_hs.mean() - s_ctrl.mean()\n",
"\n",
"ci_lo, ci_hi = np.percentile(boot_diffs, [2.5, 97.5])\n",
"print(f\"Observed mean difference (Head Start − Control): {obs_diff:.3f}\")\n",
"print(f\"Bootstrap 95% CI: [{ci_lo:.3f}, {ci_hi:.3f}]\")\n",
"print(f\"→ {'CI excludes 0 → Significant *' if ci_lo > 0 or ci_hi < 0 else 'CI includes 0 → Not significant'}\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(9, 4))\n",
"ax.hist(boot_diffs, bins=60, color='steelblue', edgecolor='white')\n",
"ax.axvline(obs_diff, color='red', linewidth=2, label=f'Observed diff = {obs_diff:.2f}')\n",
"ax.axvline(ci_lo, color='orange', linewidth=1.5, linestyle='--', label=f'95% CI lower = {ci_lo:.2f}')\n",
"ax.axvline(ci_hi, color='orange', linewidth=1.5, linestyle='--', label=f'95% CI upper = {ci_hi:.2f}')\n",
"ax.axvline(0, color='black', linewidth=1, linestyle=':', label='Zero')\n",
"ax.set_title('Monte Carlo Bootstrap – Sampling Distribution of Mean Difference (ppvt2)')\n",
"ax.set_xlabel('Mean Difference')\n",
"ax.legend()\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "315a28bc",
"metadata": {},
"source": [
"---\n",
"## Factor Analysis (Factorial Analysis)\n",
"Exploratory Factor Analysis (EFA) on all 8 cognitive outcome variables to uncover latent factor structure."
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "9580d3df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Factor Loadings:\n",
" Factor 1 Factor 2\n",
"ppvt1 -0.804 -0.020\n",
"cldwell1 -0.799 0.014\n",
"motrinh1 -0.365 0.544\n",
"block1 -0.508 0.145\n",
"ppvt2 -0.796 -0.126\n",
"cldwell2 -0.788 -0.117\n",
"motrinh2 -0.312 0.357\n",
"block2 -0.632 -0.037\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Communalities (variance explained per variable):\n",
"Variable Communality\n",
" ppvt1 0.647\n",
"cldwell1 0.639\n",
"motrinh1 0.426\n",
" block1 0.280\n",
" ppvt2 0.650\n",
"cldwell2 0.634\n",
"motrinh2 0.225\n",
" block2 0.401\n"
]
}
],
"source": [
"# ── Factor Analysis (EFA) ────────────────────────────────────────────────────\n",
"from sklearn.decomposition import FactorAnalysis\n",
"from sklearn.preprocessing import StandardScaler as SS\n",
"\n",
"FA_VARS = ['ppvt1', 'cldwell1', 'motrinh1', 'block1',\n",
" 'ppvt2', 'cldwell2', 'motrinh2', 'block2']\n",
"fa_data = data[FA_VARS].dropna()\n",
"X_fa = SS().fit_transform(fa_data)\n",
"\n",
"n_factors = 2\n",
"fa = FactorAnalysis(n_components=n_factors, random_state=42)\n",
"fa.fit(X_fa)\n",
"\n",
"loadings = pd.DataFrame(fa.components_.T,\n",
" index=FA_VARS,\n",
" columns=[f'Factor {i+1}' for i in range(n_factors)])\n",
"print(\"Factor Loadings:\")\n",
"print(loadings.round(3).to_string())\n",
"\n",
"# Heatmap\n",
"fig, ax = plt.subplots(figsize=(6, 6))\n",
"sns.heatmap(loadings, annot=True, fmt='.2f', cmap='coolwarm',\n",
" center=0, linewidths=0.5, ax=ax)\n",
"ax.set_title('EFA Factor Loadings Heatmap')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# Variance explained (noise variance proxy)\n",
"noise_var = fa.noise_variance_\n",
"communalities = 1 - noise_var\n",
"comm_df = pd.DataFrame({'Variable': FA_VARS, 'Communality': communalities.round(3)})\n",
"print(\"\\nCommunalities (variance explained per variable):\")\n",
"print(comm_df.to_string(index=False))"
]
},
{
"cell_type": "markdown",
"id": "d576c592",
"metadata": {},
"source": [
"---\n",
"\n",
"## Research Hypothesis\n",
"\n",
"**Overall Research Hypothesis:** \n",
"Children who participate in the Head Start early intervention program (program = 1) will demonstrate significantly higher cognitive outcome scores, specifically on the Peabody Picture Vocabulary Test (PPVT) and the Block Design task, at follow-up compared to children in the control group (program = 2), after controlling for baseline cognitive ability and socioeconomic status. Furthermore, the duration and quality of program engagement, along with demographic characteristics such as gender and race, are expected to moderate these cognitive gains.\n",
"\n",
"This hypothesis is grounded in the ecological systems theory and empirical findings suggesting that structured early childhood interventions improve language development and executive functioning in low-income children (Lee, 2019)."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "cf50f0ba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"FILE METADATA — hdstart.sav\n",
"============================================================\n",
"\n",
"Number of cases : 969\n",
"Number of variables: 20\n",
"\n",
"── Variable Labels ──────────────────────────────────────\n",
"Variable Label\n",
" id student identification\n",
" gender student gender\n",
" race student race\n",
" site city where data was gathered\n",
" program treatment group\n",
" age student age in months at the beginning of preschool\n",
"fthrpres father present in the household\n",
" reading amount mother reads to child\n",
" momed mother education in years\n",
" rooms rooms/people in household\n",
" famsize number of children/adults in the household\n",
" ppvt1 Peabody Picture Vocabulary score at start of preschool year\n",
"cldwell1 Caldwell Preschool Inventory at start of preschool year\n",
"motrinh1 Motor Inhibition Test at start of preschool\n",
" block1 8-Block Toy Sort total score at start of preschool\n",
" ppvt2 Peabody Picture Vocabulary score at end of preschool year\n",
"cldwell2 Caldwell Preschool Inventory at end of preschool year\n",
"motrinh2 Motor Inhibition Test at end of preschool\n",
" block2 8-Block Toy Sort total score end of preschool\n",
" newses Social Class\n",
"\n",
"── Value Labels (first 8 variables with codes) ──────────\n",
"\n",
" gender:\n",
" 1.0 = Male\n",
" 2.0 = Female\n",
"\n",
" race:\n",
" 1.0 = white\n",
" 2.0 = black\n",
"\n",
" site:\n",
" 1.0 = Trenton\n",
" 3.0 = Portland\n",
"\n",
" program:\n",
" 1.0 = Head Start\n",
" 2.0 = no preschool\n",
" 3.0 = other preschool\n",
"\n",
" fthrpres:\n",
" 0.0 = absent\n",
" 1.0 = present\n",
"\n",
" reading:\n",
" 0.0 = never\n",
" 1.0 = very infrequently\n",
" 2.0 = infrequently\n",
" 3.0 = once in a while\n",
" 4.0 = fairly often\n",
" 5.0 = every day\n"
]
}
],
"source": [
"# ── Load & Display SAV Metadata ──────────────────────────────────────────────\n",
"import pyreadstat, pandas as pd\n",
"from pathlib import Path\n",
"\n",
"df_full, meta_full = pyreadstat.read_sav(Path('hdstart.sav'))\n",
"\n",
"# Variable labels\n",
"var_labels = meta_full.column_names_to_labels\n",
"# Value labels\n",
"val_labels = meta_full.variable_value_labels\n",
"\n",
"print(\"=\" * 60)\n",
"print(\"FILE METADATA — hdstart.sav\")\n",
"print(\"=\" * 60)\n",
"print(f\"\\nNumber of cases : {df_full.shape[0]}\")\n",
"print(f\"Number of variables: {df_full.shape[1]}\")\n",
"\n",
"print(\"\\n── Variable Labels ──────────────────────────────────────\")\n",
"label_df = pd.DataFrame([\n",
" {'Variable': v, 'Label': var_labels.get(v, '(none)')}\n",
" for v in meta_full.column_names\n",
"])\n",
"print(label_df.to_string(index=False))\n",
"\n",
"print(\"\\n── Value Labels (first 8 variables with codes) ──────────\")\n",
"for var, codes in list(val_labels.items())[:8]:\n",
" print(f\"\\n {var}:\")\n",
" for code, lbl in codes.items():\n",
" print(f\" {code} = {lbl}\")"
]
},
{
"cell_type": "markdown",
"id": "05620f5f",
"metadata": {},
"source": [
"---\n",
"## Analytic Matrix\n",
"\n",
"| # | Research Question | Independent Variable (Measurement Level) | Dependent Variable (Measurement Level) | Analysis | Result Summary |\n",
"|---|-------------------|------------------------------------------|----------------------------------------|----------|----------------|\n",
"| 1 | What is the distribution of children across Head Start program conditions? | Program (3-level nominal) | — | Frequencies / Bar Chart | See Q1 output |\n",
"| 2 | What are the central tendency and spread of children's follow-up PPVT scores? | — | PPVT Follow-Up `ppvt2` (ratio) | Descriptive Statistics (mean, SD, range) | See Q2 output |\n",
"| 3 | Do children in Head Start differ from control children on follow-up PPVT scores? | Program (nominal/binary) | `ppvt2` (ratio) | Independent-Samples T-Test | See Q3 output |\n",
"| 4 | Do children's PPVT scores change from baseline to follow-up? | Time Point (repeated: `ppvt1` vs `ppvt2`) | PPVT Score (ratio) | Paired-Samples T-Test | See Q4 output |\n",
"| 5 | Do follow-up PPVT scores differ by racial group? | Race (nominal, 2 levels) | `ppvt2` (ratio) | One-Way ANOVA + Tukey HSD | See Q5 output |\n",
"| 6 | Do program and gender jointly predict follow-up PPVT scores, and is there an interaction? | Program × Gender (nominal × nominal) | `ppvt2` (ratio) | Factorial (Two-Way) ANOVA | See Q6 output |\n",
"| 7 | After controlling for baseline PPVT, do program and gender still predict follow-up PPVT? | Program × Gender + `ppvt1` covariate | `ppvt2` (ratio) | Factorial ANCOVA | See Q7 output |\n",
"| 8 | Which combination of socioeconomic and baseline cognitive variables best predicts follow-up PPVT? | `ppvt1`, `momed`, `newses`, `famsize` (ratio/interval) | `ppvt2` (ratio) | Multiple Regression | See Q8 output |\n",
"| 9 | Non-parametrically, do Head Start and control children differ on Block Design scores? | Program (nominal/binary) | `block2` (ordinal-treated) | Mann-Whitney U Test | See Q9 output |"
]
},
{
"cell_type": "markdown",
"id": "503fa1b1",
"metadata": {},
"source": [
"---\n",
"## Question 1 — Frequencies: Nominal Variable (`program`)\n",
"\n",
"**Research Question:** What is the distribution of children across the three Head Start program conditions (Head Start, Control, and other)?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"`program` is a **nominal** variable because it identifies group membership without implying any rank order or quantitative distance between values (1 = Head Start, 2 = Control, 3 = Other). Because it is nominal, the appropriate descriptive statistic is a **frequency table** and a bar chart; measures such as mean or standard deviation are meaningless for nominal data. Frequencies tell us the count and percentage of cases in each category, which directly answers the research question about distributional spread across conditions."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3e2af559",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q1 — Frequency Table: Program Condition\n",
"Program Condition Frequency (n) Percent (%) Cumulative %\n",
" Head Start 414 42.7 42.7\n",
" Control 390 40.2 83.0\n",
" Other 165 17.0 100.0\n"
]
},
{
"data": {
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y9dVXh+bTpXydg+rUqZOk11NHIz1fHTPD6T1NjatKidH5Qy2fqqGsVuGz/QjWNquhQMdgeKtsSo+L8HNUXPEdF7F+TCM2kSOLmKIepPryUy9Y5ZKFUxkb5WMqyEpq0fSzUb6evrhSMqR2jpdPwZNaC9QKGLd1QI/9/aLSUAoWdOldX8I+tRLHDXz8AFU5ruGteiqdE04toppXZXX8y/Hx1ftMDuVI6ktR1QritkDF3T7l5unLS72jlauWnBq8CVHvZOVqKjhNKF1D4h5vag3We6x19MuC+TVIU+tOQyrtFE491EVVI0THuoKd8PdN4qvYkZx1U2WAL7/80t2EwKc8aB0PCqJT49jWMaz0DAVR4akS6l2uHyn6oRp+g4r0os+u3ltViAg//hRo6jK9fjz5ny9dslc1i/A8VOVtJ+fys/ZD3ONclQB+++03Sw86j+jYVvWT+PJp9dnQVQj/uFClkPB6y3qOjksFwcoBT+75VT9eVT0gPIVLjQfx3aUx1o9pxCZaZBFTdOlXpYcU0KjuYtw7e+kLRLUa4+aivfDCC+5k9/vvv7vHKs+ljjt+B5qUdn6KBgWSKuSvVkC/3I9yO9VapPxR5QYq4FcurOZT0K8WWQVqmkelsuLmyCp/UDUVtUz9INDlTe3HuF9sCjhVPkuBlJ6jtAvVs9SXrjqZKfDQvk3ue6o7NumyqjpUKFBXLqM6ValVWyV5fArMVb9WX/TKe/SDiqTQtigdQVQ6S62W6jymFia1iMUN2uNS0KWOLsqJVQ1N3S1Kx5XWwc+tVaAv2h7V9dV7oKsIKS2yr/dL5YG0zfoC1vorLze8NV0BiIJx/VVwpaA2vB6uLznrptJOKmuk91kdeXQ8KNjQ+iilIrWK+Ov4VNCioFVlsJRfqisuKk2lckfRoGNMnwMFeNrv2v9qydOPA9VB9X88KdhVqSudP/T5UoqLPo/6oajPaFJbC1VOTj/i9FlSx0Vd5lcwnNI89uTSecG/9a9K9+nc6t/ZSyk0CxcuDNVK1rlF7486jCo/XQGgWpRVkmz8+PFJ7jQWTp9vfYZ0DCi9Qucfv15zfD+WY/2YRgyKdtkEID4qgdKhQwevRIkSrqSRDtWcOXPGW9rIL8USX6mrhEq3pIezlYTySzCphFB8VEZIpYO0DA3VqlXzunfv7m3cuDFivokTJ7oSMyrn07BhQ2/p0qVnlJYSlUBq0aKFm6948eLeo48+6i1YsCCi/FZ4uaqbb77ZldfR/Nq/t956q7dw4cKzrn9Cpb5U1kglkrS8QoUKufXT68f15ptvuud37drVS2mps9y5c7uSO+3bt/feeuutM8ofSdx99NJLL7mSUP42V6pUyevfv7+3f//+iOeNGDHCK126dOi49LdT/9f7E5+Eym9t2LDBu+WWW1w5Ku2THj16eEeOHIl4rso5de7c2StQoICbT++DSkjFXWZi6xa3VJF/POi1CxYs6D5bl156qTdnzpyIefxSRbNnz44Yn1hZsLi++uorV85N5Zv0vlx99dXesmXL4l1ecspvnW1e/zhUSb74qNyWPlPZsmVzn4du3bp5e/fuPWO+5557zu0/HRPaR1988YXXoEED77rrrjvrfvLLb/3rX/9yJahy5crlNW3a1Fu+fPkZx19Cy0hoO852/ohLn12VmFPJPpXxu+CCC7w2bdq4klnhdu3a5d17771e0aJFXZmy2rVrn/E+J/YexHdc6lymclfahzVq1PDeeecddzyerfxWrB7TiC2Z9E+0g2ngbNRKq1YCtZYkt1j8+ci/61DcO3YFgS5FqxVaLY8J3RgDiBalx6hVV1cWlHYAILpILUAgqBOQOgDp8pEqFqgnMDImBQe67BpfZz8gPSlFRWkw4WkE+iGty+Nxb28MIDoIZBEYqvOpARmTcnaVz6qC9OphndoVAoDk0q1gdWta1atVxy/VPVWuvuqRahyA6COQBRAT1AlFPaPVwU8dg4BoU2cnlQhThQO/k6SuDqnzXWrUsQZw7siRBQAAQCBRjwIAAACBRCALAACAQCJH9v+VU1EhfRV7poMJAABA9Kgy7MGDB91Nc852MwsCWTMXxEbjnt8AAACI3/bt213JzcQQyJqFbrunHRaNe38DAADg/zpw4IBrYEzKbZEJZFW64f/Vq1QQSyAb+zZv3mz169e3LFmy2L59+0Lj169fb4MHD3b3CP/ll19s3Lhx1qdPnwSXoxI6uud679693X3EAQBA7EhKuiedvRAoJ06ccPVG47t16d9//+3uCKUAtUSJEokuZ9WqVfbSSy9ZnTp10nBtAQBAWiKQRZrRLRx79epljzzyiCskruBy6NCh57TMQYMGWbVq1ezWW289Y9oll1xiTz/9tN1+++3utpIJOXTokHXs2NHdCrVQoULntD4AACB6CGSRpqZPn2558uSxlStX2ujRo2348OG2YMECN61169buTk4JDTVr1oxY1qJFi2z27Nk2YcKEc1qn7t272w033GAtWrQ4p+UAAIDoIkcWaUqX7ocMGeL+X6VKFXvhhRds4cKF1rJlS3v11VftyJEjCT43W7Zsof/v2bPH7rnnHnvttdfOKY951qxZ7n7pSi0AAADBRiCLNBU3B7VkyZK2e/du9//SpUsneTldunSxDh062JVXXpnidVFVCnXsUotwzpw5U7wcAAAQG0gtQJoKb1X1eyDqBhTJTS1QWsGYMWMsa9asbujcubPt37/f/X/KlClJWhdVM1AQffHFF4eWs2TJEnvuuefc/0+dOpXKWw8AANISLbKImuSkFixfvjwi0Hz//fftqaeesmXLliW5Zbd58+a2bt26iHH33nuv6zw2YMAAV84LAAAEB4EsoiY5qQXVq1ePeLx69Wp327patWqFxh0/ftw2bNgQ+v9vv/1ma9euda27lStXdoWVw+cXdUQrUqTIGeMBAEDsI7UAGepWw7pRgoYdO3a4VAT9//7774/2qgEAgDSQyfM8z85zuhVagQIFXM4ld/YCAAAIRlxGiywAAAACiUAWAAAAgUQgGyWnT5/3GR3g2AMA4JxQtSBKMmfOZGNmrLFfdx2M1irgPFSmeD57uGODaK8GAACpgkA2ihTEbvltfzRXAQAAILBILQAAAEAgEcgCQMBt3LjRrr76aitevLjlzJnTKlasaIMGDbITJ06E5tH/hw8fbpUqVXLz1K1b1+bNm3fGsiZMmGAVKlRw8zRq1Mi+/PLLdN4aAAhgIPvkk09apkyZrE+fPqFxR48ete7du7s7L+nuTO3bt7ddu3ZFPG/btm12ww03WO7cua1YsWLWv39/O3nyZBS2AACiQ7dzvvvuu23+/PkuqB0/fry98sorNmTIkNA8Cmxfeukle/75590d8B588EG76aab7Ouvvw7N88Ybb1i/fv3c87766isX7LZq1cp2794dpS0DgAAEsqtWrXIn2Dp16kSM79u3r3344Yc2e/ZsW7Jkibtz08033xyafurUKRfE6naky5Yts+nTp9u0adNs8ODBUdgKAEi6Zs2aWa9eveyRRx6xwoULW4kSJWzo0KEp2oVqgb333ntd4Fm+fHm78cYbrWPHjvbZZ5+F5vnvf/9rjz76qF1//fVu/m7durn/P/PMM6F5xo4da126dHHLqlGjhk2aNMk1EkyZMoW3FkBMinoge+jQIXfCVetBoUKFQuN1N4fJkye7E+s111xjDRo0sKlTp7qAdcWKFW4etT6oZeG1116zevXqWevWrW3EiBHu0piCWwCIZfrxnSdPHlu5cqWNHj3aXfpfsGCBm6bzma5EJTTUrFkzweVu3rzZpQ1cddVVoXHHjh1z6QLhcuXKZZ9//rn7v86Za9assRYtWoSmZ86c2T1evnx5Gmw9AGSAQFapA2pVDT95ik6oyukKH1+tWjUrV65c6KSqv7Vr13Z5YT5dBtOtzdavX5+OWwEAyaerULqMX6VKFZca0LBhQ1u4cKGb9uqrr9ratWsTHObOnXvG8i677DIXrGp5V1xxhQuMw8+NahjYtGmTnT592gXM77zzju3YscNN//PPP91VrvDzqejxzp07eXsBxKSolt+aNWuWy8NSakFcOnFmz57dChYsmOBJVX/jO+n60xKilgkNPgW+AJDe4qZTlSxZMpSPWrp06WQvTzmuBw8etG+++cb1FxgzZoxLXZBnn33WpQ2oQUD9EdTpSykEpA0ACLKotchu377devfubTNmzDjjcldaGzVqlBUoUCA0lC1bNl1fHwD8TlrhFGCqtTSlqQU6lym39Y477nAdaJVzq1ZWueCCC+y9996zw4cP2y+//GI//PCDW47yZaVo0aKWJUuWMzrU6rHydwEgFkWtRVapA2p5uPjii0PjdMJdunSpvfDCC/bxxx+7nK19+/ZFtMqGn1T1N25pGP8knNiJd+DAga5nbniLLMEsgFii1IIjR44kOQiOSwGx0rP0VwGqTw0Hau3VtLfffttuvfVWN15XwNQXQakN7dq1Cy1Dj3v06JFq2wUAGSKQbd68ua1bty5inC5z6bLXgAEDXGCpE7VOoiq7JSoro3JbTZo0cY/1d+TIkS4gVuktUd5X/vz5XatEQnLkyOEGAIhVyUkt0JUtnS/VZ0DnttWrV7sf7Lfddlso4FWHst9++811jNVftdYqUPVTD0Q/8Dt16uRydS+99FJXxkstuDo3A0Asilogmy9fPqtVq1bEOPXeVc1Yf3znzp3diVWlaRSc9uzZ0wWvjRs3dtOvvfZaF7Deddddrsev8mJVK1EdyAhUAZwvsmbNak899ZT9+OOP5nmeK8GlVlSVMAyvy63z408//eRSClR6SyW5wq94KfD9448/XAlDnU8V9Kr6Qdy+CAAQKzJ5OuvFUF1FnTjVCuCfeP/1r3/Z66+/7jpnqdftxIkTI9IGlOuleoiLFy92gbBaE5QbphN7Uim1QLmyKvmlgDm99Bm72Lb8tj/dXg+oVLqAje/XjB0BAIhZyYnLYiqQjRYCWZwvCGQBABkpLot6HVkAAAAgJQhkAcQU7/+VnwI4/gDE9A0RACCuTJkz2+73xtvxPb+yc5CushcpY8Xa9WGvAwFCIAsg5iiIPb5za7RXAwAQ40gtAAAAQCARyAIAACCQCGQBAAAQSASyAAAACCQCWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIpKgGsi+++KLVqVPH8ufP74YmTZrYRx99FJrerFkzy5QpU8Tw4IMPRixj27ZtdsMNN1ju3LmtWLFi1r9/fzt58mQUtgYAAADpKatFUZkyZezJJ5+0KlWqmOd5Nn36dGvbtq19/fXXVrNmTTdPly5dbPjw4aHnKGD1nTp1ygWxJUqUsGXLltmOHTvs7rvvtmzZstkTTzwRlW0CAADAeRDItmnTJuLxyJEjXSvtihUrQoGsAlcFqvGZP3++bdiwwT755BMrXry41atXz0aMGGEDBgywoUOHWvbs2dNlOwAAAHAe58iqdXXWrFl2+PBhl2LgmzFjhhUtWtRq1aplAwcOtL///js0bfny5Va7dm0XxPpatWplBw4csPXr16f7NgAAAOA8aZGVdevWucD16NGjljdvXnv33XetRo0ablqHDh2sfPnyVqpUKfv2229dS+vGjRvtnXfecdN37twZEcSK/1jTEnLs2DE3+BT4AgAAIFiiHshWrVrV1q5da/v377e33nrLOnXqZEuWLHHBbNeuXUPzqeW1ZMmS1rx5c9uyZYtVqlQpxa85atQoGzZsWCptAQAAAM7L1ALlsVauXNkaNGjgAsy6devas88+G++8jRo1cn83b97s/ip3dteuXRHz+I8TyqsVpSgocPaH7du3p+IWAQAA4LwIZOM6ffp0xGX/cGq5FbXMilISlJqwe/fu0DwLFixwpbz89IT45MiRI1Tyyx8AAAAQLFFNLVDLaOvWra1cuXJ28OBBmzlzpi1evNg+/vhjlz6gx9dff70VKVLE5cj27dvXrrzySld7Vq699loXsN511102evRolxc7aNAg6969uwtWAQAAkHFFNZBVS6rqvqr+a4ECBVyAqiC2ZcuW7nK/ymqNHz/eVTIoW7astW/f3gWqvixZsticOXOsW7durnU2T548Lsc2vO4sAAAAMqaoBrKTJ09OcJoCV3X6OhtVNZg7d24qrxkAAABiXczlyAIAAABJQSALAACAQCKQBQAAQCARyAIAACCQCGQBAAAQSASyAAAACCQCWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAABBKBLAAAAAIpqoHsiy++aHXq1LH8+fO7oUmTJvbRRx+Fph89etS6d+9uRYoUsbx581r79u1t165dEcvYtm2b3XDDDZY7d24rVqyY9e/f306ePBmFrQEAAMB5E8iWKVPGnnzySVuzZo2tXr3arrnmGmvbtq2tX7/eTe/bt699+OGHNnv2bFuyZIn9/vvvdvPNN4eef+rUKRfEHj9+3JYtW2bTp0+3adOm2eDBg6O4VQAAAEgPWS2K2rRpE/F45MiRrpV2xYoVLsidPHmyzZw50wW4MnXqVKtevbqb3rhxY5s/f75t2LDBPvnkEytevLjVq1fPRowYYQMGDLChQ4da9uzZo7RlAAAAOG9yZNW6OmvWLDt8+LBLMVAr7YkTJ6xFixaheapVq2blypWz5cuXu8f6W7t2bRfE+lq1amUHDhwIteoCAAAgY4pqi6ysW7fOBa7Kh1Ue7Lvvvms1atSwtWvXuhbVggULRsyvoHXnzp3u//obHsT60/1pCTl27JgbfAp8AQAAECxRb5GtWrWqC1pXrlxp3bp1s06dOrl0gbQ0atQoK1CgQGgoW7Zsmr4eAAAAMmAgq1bXypUrW4MGDVyAWbduXXv22WetRIkSrhPXvn37IuZX1QJNE/2NW8XAf+zPE5+BAwfa/v37Q8P27dvTZNsAAACQgQPZuE6fPu0u+yuwzZYtmy1cuDA0bePGja7cllIRRH+VmrB79+7QPAsWLHClvJSekJAcOXKESn75AwAAAIIlqjmyahlt3bq168B18OBBV6Fg8eLF9vHHH7tL/p07d7Z+/fpZ4cKFXbDZs2dPF7yqYoFce+21LmC96667bPTo0S4vdtCgQa72rIJVAAAAZFxRDWTVknr33Xfbjh07XOCqmyMoiG3ZsqWbPm7cOMucObO7EYJaaVWRYOLEiaHnZ8mSxebMmeNyaxXg5smTx+XYDh8+PIpbBQAAgAwfyKpObGJy5sxpEyZMcENCypcvb3Pnzk2DtQMAAEAsi7kcWQAAACApCGQBAAAQSASyAAAACCQCWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAABBKBLAAAAAKJQBYAAACBRCALAACAQCKQBQAAQCBFNZAdNWqUXXLJJZYvXz4rVqyYtWvXzjZu3BgxT7NmzSxTpkwRw4MPPhgxz7Zt2+yGG26w3Llzu+X079/fTp48mc5bAwAAgPSU1aJoyZIl1r17dxfMKvB89NFH7dprr7UNGzZYnjx5QvN16dLFhg8fHnqsgNV36tQpF8SWKFHCli1bZjt27LC7777bsmXLZk888US6bxMAAADOg0B23rx5EY+nTZvmWlTXrFljV155ZUTgqkA1PvPnz3eB7yeffGLFixe3evXq2YgRI2zAgAE2dOhQy549e5pvBwAAAM7zHNn9+/e7v4ULF44YP2PGDCtatKjVqlXLBg4caH///Xdo2vLly6127douiPW1atXKDhw4YOvXr0/HtQcAAMB50yIb7vTp09anTx9r2rSpC1h9HTp0sPLly1upUqXs22+/dS2tyqN955133PSdO3dGBLHiP9a0+Bw7dswNPgW9AAAACJaYCWSVK/vdd9/Z559/HjG+a9euof+r5bVkyZLWvHlz27Jli1WqVCnFncyGDRt2zusMAACA8zy1oEePHjZnzhz79NNPrUyZMonO26hRI/d38+bN7q9yZ3ft2hUxj/84obxapScojcEftm/fnkpbAgAAgPMikPU8zwWx7777ri1atMguvPDCsz5n7dq17q9aZqVJkya2bt062717d2ieBQsWWP78+a1GjRrxLiNHjhxuevgAAACAYMka7XSCmTNn2vvvv+9qyfo5rQUKFLBcuXK59AFNv/76661IkSIuR7Zv376uokGdOnXcvCrXpYD1rrvustGjR7tlDBo0yC1bASsAAAAyphS1yFasWNH27Nlzxvh9+/a5aUn14osvukv7uumBWlj94Y033nDTVTpLZbUUrFarVs3+9a9/Wfv27e3DDz8MLSNLliwuLUF/1Tp75513ujqy4XVnAQAAkPGkqEX2559/djciiEuVAH777bdkpRYkpmzZsu6mCWejqgZz585N8usCAADgPAtkP/jgg9D/P/74Y5cC4FNgu3DhQqtQoULqriEAAABwroFsu3bt3N9MmTJZp06dIqbplrAKYp955pnkLBIAAABI+0BWNy0QVRdYtWqVu9sWAAAAEJgc2a1bt6b+mgAAAADpUX5L+bAaVL/Vb6n1TZkyJaWLBQAAANIukNXtXVXeqmHDhq5clnJmAQAAgJgPZCdNmmTTpk1zNyEAAAAAAnNDhOPHj9tll12W+msDAAAApGUge//997tbxwIAAACBSi04evSovfzyy+72sXXq1HE1ZMONHTs2tdYPAAAASL1A9ttvv7V69eq5/3/33XcR0+j4BQAAgJgNZD/99NPUXxMAAAAgrXNkAQAAgEC2yF599dWJphAsWrToXNYJAAAASJtA1s+P9Z04ccLWrl3r8mU7deqUkkUCAAAAaR/Ijhs3Lt7xQ4cOtUOHDqVkkQAAAED0cmTvvPNOmzJlSmouEgAAAEj7QHb58uWWM2fO1FwkAAAAkHqpBTfffHPEY8/zbMeOHbZ69Wp77LHHUrJIAAAAIO0D2QIFCkQ8zpw5s1WtWtWGDx9u1157bUoWCQAAAKR9IDt16tSUPA0AAACIbiDrW7NmjX3//ffu/zVr1rT69eun1noBAAAAqR/I7t69226//XZbvHixFSxY0I3bt2+fu1HCrFmz7IILLkjJYgEAAIC0rVrQs2dPO3jwoK1fv97++usvN+hmCAcOHLBevXqlZJEAAABA2gey8+bNs4kTJ1r16tVD42rUqGETJkywjz76KMnLGTVqlF1yySWWL18+K1asmLVr1842btwYMc/Ro0ete/fuVqRIEcubN6+1b9/edu3aFTHPtm3b7IYbbrDcuXO75fTv399OnjyZkk0DAABARg5kT58+bdmyZTtjvMZpWlItWbLEBakrVqywBQsWuFvdqurB4cOHQ/P07dvXPvzwQ5s9e7ab//fff48o/3Xq1CkXxB4/ftyWLVtm06dPt2nTptngwYNTsmkAAADIyDmy11xzjfXu3dtef/11K1WqlBv322+/uaCzefPmyWrZDacAVC2q6kR25ZVX2v79+23y5Mk2c+ZM95p+xQS1BCv4bdy4sc2fP982bNhgn3zyiRUvXtzq1atnI0aMsAEDBrhb5mbPnj0lmwgAAICM2CL7wgsvuHzYChUqWKVKldxw4YUXunHPP/98ildGgasULlzY/VVAq1baFi1ahOapVq2alStXzt1FTPS3du3aLoj1tWrVyq2LcngBAACQMaWoRbZs2bL21VdfuVbQH374wY1TK2l4wJlcSkno06ePNW3a1GrVquXG7dy507Wo+pURfApaNc2fJzyI9af70+Jz7NgxN/gU9AIAACADt8guWrTIdepS4JcpUyZr2bKlq2CgQZ22VEv2s88+S9GKKFdWlQ9UviutqZOZ7k7mDwrMAQAAkIED2fHjx1uXLl0sf/78Z0xTQPjAAw/Y2LFjk70SPXr0sDlz5tinn35qZcqUCY0vUaKE68SlGrXhVLVA0/x54lYx8B/788Q1cOBAl8bgD9u3b0/2OgMAACBAgew333xj1113XYLTVXFAea1J5XmeC2Lfffdd19qrPNtwDRo0cJUQFi5cGBqn8lwqt9WkSRP3WH/XrVvnbtLgUwUEBdtqPY5Pjhw53PTwAQAAABk4R1YtnfGV3QotLGtW++OPP5KVTqCKBO+//76rJevntKp1N1euXO5v586drV+/fq4DmAJOpTEoeFXFAj94VsB611132ejRo90yBg0a5JatgBUAAAAZU7JaZEuXLu3yWBPy7bffWsmSJZO8vBdffNFd2m/WrJl7nj+88cYboXnGjRtn//jHP9yNEFSSS+kC77zzTmh6lixZXFqC/irAvfPOO+3uu++24cOHJ2fTAAAAkJFbZK+//np77LHHXHpBzpw5I6YdOXLEhgwZ4oLO5KQWnI1eR3cM05CQ8uXL29y5c5P8ugAAADjPAlldsldr6EUXXeRyW6tWrerGqwSXAk3dZevf//53Wq0rAAAAkLJAVvVZdRvYbt26uZ7/fouqSnHpJgQKZuPWdAUAAABi4oYI/mX8vXv32ubNm10wW6VKFStUqFCarCAAAACQanf2EgWuugkCAAAAEPNVCwAAAIBYQSALAAAypKNHj9o999xjtWvXdrXu27VrF+98x44dc53VlT6pGvQVKlSwKVOmhKZPmzbN9QcKH+JWb0LAUgsAAABimaop6QZLvXr1srfffjvB+W699VZ306fJkydb5cqVbceOHXb69OmIeXRTJt1d1KdgFtFHIAsAAGKGbpJUp04d1+L56quvWvbs2e3BBx+0oUOHJntZefLkcTdfki+++ML27dt3xjzz5s2zJUuW2E8//eTuIipqkY1LgatuyoTYQmoBAACIKdOnT3dB6MqVK93t53W3zgULFrhprVu3trx58yY41KxZM1mv9cEHH1jDhg3d6+gOpqqV//DDD7sbPYU7dOiQSz0oW7astW3b1tavX5+q24yUoUUWAADEFLXI6m6hohKfL7zwgi1cuNBatmzpWmnjBpnhsmXLlqzXUkvs559/7lqA3333Xfvzzz/toYcesj179tjUqVPdPLoBlHJmtV779++3MWPG2GWXXeaC2TJlypzj1uJcEMgCAICYooAxXMmSJW337t3u/2o1TU3KhVXawIwZM6xAgQJu3NixY+2WW26xiRMnuhzbJk2auMGnILZ69er20ksv2YgRI1J1fZA8pBYAAICYErdVVYGm3/kqtVMLFCQrOPaDWFGQqhs+/frrrwmuX/369d2NoRBdtMgCAIDASO3UgqZNm9rs2bNdDqwCYfnxxx8tc+bMCaYNqBrCunXr7Prrr0/m2iO1EcgCAIDASG5qwYYNG+z48eP2119/2cGDB23t2rVufL169dzfDh06uPSAe++914YNG+ZyZPv372/33XefSysQdTZr3LixK82lygdPP/20/fLLL3b//fenwRYiOQhkAQBAhqVWUwWdPqUEiFIHRK2wqojQs2dPV72gSJEirq7s448/HnrO3r17rUuXLrZz504rVKiQNWjQwJYtW2Y1atSIwhYhHIEsAACIGYsXLz5j3HvvvZfi5f38889nnadatWqh8l7xGTdunBsQe+jsBQAAgEAikAUAAEAgEcgCABAQp73/W4IK4Nj7v8iRBQAgIDJnymzPrZhivx3YGe1VwXmkdP4S1qvxfRaLCGQBAAgQBbFb926P9moAMYHUAgAAAAQSgSwAAAACiUAWAAAAgRTVQHbp0qXWpk0bK1WqlGXKlOmMgsf33HOPGx8+XHfddRHz6JZzHTt2tPz581vBggWtc+fO7n7JAAAAyNiiGsgePnzY6tataxMmTEhwHgWuO3bsCA2vv/56xHQFsevXr3d35JgzZ44Ljrt27ZoOaw8AAIDztmpB69at3ZCYHDlyWIkSJeKd9v3339u8efNs1apV7v7I8vzzz7v7Ko8ZM8a19AIAACBjyhyEey4XK1bMqlatat26dbM9e/aEpi1fvtylE/hBrLRo0cIyZ85sK1eujNIaAwAAwM73OrJKK7j55pvtwgsvtC1bttijjz7qWnAVwGbJksV27tzpgtxwWbNmtcKFC7tpCTl27JgbfAcOHEjT7QAAAMB5Fsjefvvtof/Xrl3b6tSpY5UqVXKttM2bN0/xckeNGmXDhg1LpbUEAABANMR8akG4ihUrWtGiRW3z5s3usXJnd+/eHTHPyZMnXSWDhPJqZeDAgbZ///7QsH07d0gBAAAImkAFsr/++qvLkS1ZsqR73KRJE9u3b5+tWbMmNM+iRYvs9OnT1qhRo0Q7kKlcV/gAAACAYIlqaoHqvfqtq7J161Zbu3aty3HVoMv/7du3d62rypF95JFHrHLlytaqVSs3f/Xq1V0ebZcuXWzSpEl24sQJ69Gjh0tJoGIBAABAxhbVFtnVq1db/fr13SD9+vVz/x88eLDrzPXtt9/ajTfeaBdddJG70UGDBg3ss88+cy2qvhkzZli1atVczqzKbl1++eX28ssvR3GrAAAAkOFbZJs1a2ae5yU4/eOPPz7rMtRyO3PmzFReMwAAAMS6QOXIAgAAAD4CWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAABBKBLAAAAAKJQBYAAACBRCALAACAQCKQBQAAQCARyAIAACCQCGQBAAAQSFENZJcuXWpt2rSxUqVKWaZMmey9996LmO55ng0ePNhKlixpuXLlshYtWtimTZsi5vnrr7+sY8eOlj9/fitYsKB17tzZDh06lM5bAgAAgPMqkD18+LDVrVvXJkyYEO/00aNH23PPPWeTJk2ylStXWp48eaxVq1Z29OjR0DwKYtevX28LFiywOXPmuOC4a9eu6bgVAAAAiIasFkWtW7d2Q3zUGjt+/HgbNGiQtW3b1o37z3/+Y8WLF3ctt7fffrt9//33Nm/ePFu1apU1bNjQzfP888/b9ddfb2PGjHEtvQAAAMiYYjZHduvWrbZz506XTuArUKCANWrUyJYvX+4e66/SCfwgVjR/5syZXQsuAAAAMq6otsgmRkGsqAU2nB770/S3WLFiEdOzZs1qhQsXDs0Tn2PHjrnBd+DAgVReewAAAJy3LbJpadSoUa511x/Kli0b7VUCAABARglkS5Qo4f7u2rUrYrwe+9P0d/fu3RHTT5486SoZ+PPEZ+DAgbZ///7QsH379jTZBgAAAJyHgeyFF17ogtGFCxdGpAAo97VJkybusf7u27fP1qxZE5pn0aJFdvr0aZdLm5AcOXK4cl3hAwAAAIIlqjmyqve6efPmiA5ea9eudTmu5cqVsz59+tjjjz9uVapUcYHtY4895ioRtGvXzs1fvXp1u+6666xLly6uRNeJEyesR48erqIBFQsAAAAytqgGsqtXr7arr7469Lhfv37ub6dOnWzatGn2yCOPuFqzqgurltfLL7/cldvKmTNn6DkzZsxwwWvz5s1dtYL27du72rMAAADI2KIayDZr1szVi02I7vY1fPhwNyRErbczZ85MozUEAABArIrZHFkAAAAgMQSyAAAACCQCWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAABBKBLAAAAAKJQBYAAACBRCALAACAQCKQBQAAQCARyAIAACCQYjqQHTp0qGXKlCliqFatWmj60aNHrXv37lakSBHLmzevtW/f3nbt2hXVdQYAAED6iOlAVmrWrGk7duwIDZ9//nloWt++fe3DDz+02bNn25IlS+z333+3m2++OarrCwAAgPSR1WJc1qxZrUSJEmeM379/v02ePNlmzpxp11xzjRs3depUq169uq1YscIaN24chbUFAABAeon5FtlNmzZZqVKlrGLFitaxY0fbtm2bG79mzRo7ceKEtWjRIjSv0g7KlStny5cvj+IaAwAAwM73FtlGjRrZtGnTrGrVqi6tYNiwYXbFFVfYd999Zzt37rTs2bNbwYIFI55TvHhxNy0xx44dc4PvwIEDabYNAAAAOA8D2datW4f+X6dOHRfYli9f3t58803LlStXipc7atQoFxQDAAAguGI+tSCcWl8vuugi27x5s8ubPX78uO3bty9iHlUtiC+nNtzAgQNdjq0/bN++PY3XHAAAAOd1IHvo0CHbsmWLlSxZ0ho0aGDZsmWzhQsXhqZv3LjR5dA2adIk0eXkyJHD8ufPHzEAAAAgWGI6teDhhx+2Nm3auHQCldYaMmSIZcmSxe644w4rUKCAde7c2fr162eFCxd2wWjPnj1dEEvFAgAAgIwvpgPZX3/91QWte/bssQsuuMAuv/xyV1pL/5dx48ZZ5syZ3Y0Q1HmrVatWNnHixGivNgAAAM73QHbWrFmJTs+ZM6dNmDDBDQAAADi/BCpHFgAAAPARyAIAACCQCGQBAAAQSASyAAAACCQCWQAAAAQSgSwAAAACiUAWAAAAgUQgCwAAgEAikAUAAEAgEcgCAAAgkAhkAQAAEEgEsgAAAAgkAlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAABBKBLAAAAAKJQBYAAACBRCALAACAQMowgeyECROsQoUKljNnTmvUqJF9+eWX0V4lAAAApKEMEci+8cYb1q9fPxsyZIh99dVXVrduXWvVqpXt3r072qsGAACANJIhAtmxY8daly5d7N5777UaNWrYpEmTLHfu3DZlypRorxoAAADSSOAD2ePHj9uaNWusRYsWoXGZM2d2j5cvXx7VdQMAAEDayWoB9+eff9qpU6esePHiEeP1+Icffoj3OceOHXODb//+/e7vgQMHLD0VzZfJjhUJ/FuAANExl97HeUocy1XUjhf4/z+jQHrwchUNxOejSJYCdiz70WivBs4jRbIUSNfPhv9anueddd7zMooaNWqUDRs27IzxZcuWjcr6AOnpsa7sbyBhA9k5QDwGWA9LbwcPHrQCBQpk7EC2aNGiliVLFtu1a1fEeD0uUaJEvM8ZOHCg6xzmO336tP31119WpEgRy5QpU5qvM87tV5p+cGzfvt3y58/PrgT4fAB8d2QwaolVEFuqVKmzzhv4QDZ79uzWoEEDW7hwobVr1y4UmOpxjx7x/3rIkSOHG8IVLFgwXdYXqUNBLIEswOcD4LsjYzpbS2yGCWRFraudOnWyhg0b2qWXXmrjx4+3w4cPuyoGAAAAyJgyRCB722232R9//GGDBw+2nTt3Wr169WzevHlndAADAABAxpEhAllRGkFCqQTIOJQSohtfxE0NAcDnA+C74/yTyUtKbQMAAAAgxgT+hggAAAA4PxHIAgAAIJAIZBHzFi9e7Or77tu3L9qrApyX+AwiqKZNm0Z5zQyOQBZndc8994Rq9Mbql9s333xjN954oxUrVsxy5sxpFSpUcNUsdu/enSbryskR50LVVXr27GkVK1Z0HRd1k482bdq4+teppVmzZtanT59UWx4QTboJzn333ecK5Kt+fPny5a137962Z8+e0Dw676v8Js4vBLIIPJVea968uRUuXNg+/vhj+/77723q1KnuhKd6wqntxIkTqb5MnD9+/vlndxOXRYsW2dNPP23r1q1z5QKvvvpq6969e7qui/r6njx5Ml1fE0iun376ydWJ37Rpk73++uu2efNmmzRpkvvh16RJE3dnzvTG90AMUdUCIDGdOnXy2rZte8b4Tz/9VBUvvL1794bGffbZZ97ll1/u5cyZ0ytTpozXs2dP79ChQ6Hp//nPf7wGDRp4efPm9YoXL+7dcccd3q5duyKW+7///c+rUqWKW0azZs28qVOnnvE64d59910va9as3okTJ+KdvnXrVvf88EHbJB999JHXtGlTr0CBAl7hwoW9G264wdu8efMZz501a5Z35ZVXejly5AitT/gwZMgQDiIkSevWrb3SpUtHfC58/jH+yy+/eDfeeKOXJ08eL1++fN4///lPb+fOnaH5dLzVrVvXfZ7Kly/v5c+f37vtttu8AwcOuOk6vuMeozqW/c/s3LlzvYsvvtjLli2bG3f06FH3Wb3gggvcMa7PxJdffpnoZx1IL9ddd537Pvn7778jxu/YscPLnTu39+CDD3pXXXXVGce86Hyt8/u8efO8atWquc9Uq1atvN9//z1iWa+88oqbruO/atWq3oQJE876PYDYQCCLVAtkFQDqJDFu3Djvxx9/9L744guvfv363j333BN6zuTJk92X6JYtW7zly5d7TZo0cV/svm3btrmTRL9+/bwffvjBe+2111zAm9iXqJaj6W+++aZ3+vTpM6afPHnSe/vtt908GzdudCe/ffv2uWlvvfWWm7Zp0ybv66+/9tq0aePVrl3bO3XqVMQJrEKFCm6+n376yfv555+98ePHu+BBy9Jw8OBBjiSc1Z49e7xMmTJ5TzzxRILz6NirV6+e+0G4evVqb8WKFe7Hn76owwNZ/Ri8+eabvXXr1nlLly71SpQo4T366KNuuo5vfba6dOkSOkb1OfA/s3Xq1PHmz5/vPrNap169enmlSpVyn83169e7z3yhQoXctPg+60CsfGZ0jOtY/fPPP12wO3z48NAxLwo49YOtRYsW3qpVq7w1a9Z41atX9zp06BBahr5nSpYsGTrH668aNqZNm5bg90DcQBjRQyCLs9KXWpYsWVyQGj6oxTT8y61z585e165dI56rFtrMmTN7R44ciXfZOrFoGX4gOHDgQK9GjRoR8wwYMOCsX6L6AlerrE4++vU+evToiBaspH4R//HHH24+BQfhJzAFruH8X/lAcqxcudIdT++8806C8yjA1OdNP+p8Ci71PL+VVIGsWqL8Fljp37+/16hRo9BjBb69e/eOWLb/OXjvvfdC49QyrC/6GTNmhMYdP37cBbb6HIU/j0AW6U0/5HTs6cpbfMaOHeum68qerk6oISWcfwUt/EqbWlvVQOKrVKmSN3PmzIjnjRgxwv0YTOx7ALGBHFkkifL31q5dGzG8+uqrZ3S4UieovHnzhoZWrVrZ6dOnbevWrW6eNWvWuE4t5cqVs3z58tlVV13lxm/bts39VX5ro0aNIparHKizGTlypOtAo7ypmjVrur/VqlVz+YeJUc7VHXfc4Trd5M+f33UWCF8fn/KzgHOVlPvP6DOgzl8afDVq1HA9rzXNp2NVnyFfyZIlQ50bzyb8eN6yZYvL92vatGloXLZs2ezSSy+NeD0gms7l3k25c+e2SpUqxftZUT8KfQY6d+4c8d31+OOPu/Hh+B6ITRnmFrVIW3ny5LHKlStHjPv1118jHh86dMgeeOAB69Wr1xnPV+CqE4YCWw0zZsywCy64wAWMenz8+PFzXsciRYrYP//5Tzc88cQTVr9+fRszZoxNnz49wecoqFbv11deecV1DlPQXatWrTPWR9sPnKsqVaq46hk//PDDOS9LwWY4LVfHb1JwPCMo9L2jY1s/qm666aYzpmt8oUKF3PdJcj4rfmCs7y3Rd0DcRpQsWbJEPOZzE5tokUWqufjii23Dhg3uxBN3ULkUfXmrVMqTTz5pV1xxhWsxjduCVL16dfvyyy8jxq1YsSLZ66LX0y9wv2qBHsupU6dC82hdNm7caIMGDXJVD/Tae/fuTfLyw5cFJIUqa+iH24QJE+KtqKHycDoOVWpIg0+fK01Ty2xSJfUY1edE837xxRehcWqhXbVqVbJeD0gLaqBo2bKlTZw40Y4cORIxTVfh1CiiUosKTlNyXi5evLhrxFBlhLjfWxdeeGEqbw3SAoEsUs2AAQNs2bJl1qNHD5d6oMv277//vnvst8rqRPP888+7k8YHH3xgI0aMiFjGgw8+6J7Xv39/F2TOnDnTpSskZs6cOXbnnXe6vz/++KN7nlpi586da23btnXzqNVVJzrNo3Jd+hWuX/E6Sb788suunIvKIfXr1y9J26rLulqGyr/8+eef9vfff6d4v+H8oiBWX7a6dP/222+7412tSs8995xLo2nRooXVrl3bOnbsaF999ZX7YXf33Xe7NJzkXNrUMbpy5UpX7kvHaEKttWpl6tatm/vMqQyYguYuXbq4Y1qXW4Foe+GFF+zYsWPuR+DSpUvdjzwdqwpwS5cu7VLL/GNe03/77Td3zCfVsGHDbNSoUe4zqO8QpaSphOPYsWPTcKuQaqKdpIuMVX5LnVFatmzpelSrQ5h6R48cOTI0XQn16vmpygRKpP/ggw/cMlQxwPfhhx96lStXdvNcccUV3pQpUxLtaKIKCOq5etFFF3m5cuXyChYs6F1yySVnlEdRb1b17FYPWL/81oIFC1wPVr2W1nXx4sURHQv8JP/w9fOp5EuRIkUov4VkU4/n7t27u84p2bNnd+W4VG5Ln6nklN8Kp04uWp5PFToaN27sPhNxy2/F/SypM6bKbxUtWpTyW4hJqhaj87Y6aalzYtmyZd0xq2oF4RVsdB7X+Txu+a1wOr/HDX/U2VHVQvR5VBUEldnyO2Um9j2A6Mukf/hdAAAAgKAhtQAAAACBRCALAACAQCKQBQAAQCARyAIAACCQCGQBAAAQSASyAAAACCQCWQAAAAQSgSwAAAACiUAWAJAqhg4davXq1Qs9vueee6xdu3aJPmfx4sXu9tH79u3jXQCQbASyADIUBU8KjDRkz57dKleubMOHD7eTJ0/a+eD48eM2evRoq1u3ruXOnduKFi1qTZs2dfeOP3HiRLquy7PPPmvTpk0LPW7WrJn16dMnYp7LLrvMduzYYQUKFEjXdQOQMWSN9goAQGq77rrrXOB27Ngxmzt3rnXv3t2yZctmAwcOjDfwU8CbFtJy2Qm9XqtWreybb76xESNGuAA2f/78tmLFChszZozVr18/osU0rSUlONX+KVGiRLqsD4CMhxZZABlOjhw5XHBUvnx569atm7Vo0cI++OCDiMvdI0eOtFKlSlnVqlXd+HXr1tk111xjuXLlsiJFiljXrl3t0KFDoWWqRbdXr15WsGBBN33AgAHWqVOniEvnanHs0aOHa3VUS6iCShk7dqzVrl3b8uTJY2XLlrWHHnooYtlqtdRy58yZ49ZHLam33HKL/f333zZ9+nSrUKGCFSpUyL3+qVOnEtzu8ePH29KlS23hwoUueFfQWrFiRevQoYOtXLnSqlSp4uZTgK9lFStWzHLmzGmXX365rVq16ozL/VpOw4YN3fqo5XTjxo0Rr/fkk09a8eLFLV++fNa5c2c7evRoxPTw1AL9f8mSJa6V1m8x//nnn+NNLXj77betZs2a7n3Utj/zzDMRy9W4J554wu677z732uXKlbOXX345iUcHgIyEQBZAhqfgVK2VPgVoCsoWLFjggsfDhw+7oFPBogK62bNn2yeffOKCUt9TTz1lM2bMcC29X3zxhR04cMDee++9M15LgadaGTXPpEmT3LjMmTPbc889Z+vXr3fTFy1aZI888kjE8xS0ap5Zs2bZvHnzXIB30003uRZlDf/973/tpZdesrfeeivB7dT6KWhXy2tcapFWIC16bQWLWpevvvrKpV9o+//666+I5/z73/92QeTq1asta9asLnD0vfnmmy4nVgGlppcsWdImTpyY4LopgG3SpIl16dLFpRJoUFAf15o1a+zWW2+122+/3f240Gs89thjESkKovVSkP3111+7Hwb6wRI30AZwHvAAIAPp1KmT17ZtW/f/06dPewsWLPBy5MjhPfzww6HpxYsX944dOxZ6zssvv+wVKlTIO3ToUGjc//73Py9z5szezp073WM95+mnnw5NP3nypFeuXLnQa8lVV13l1a9f/6zrOHv2bK9IkSKhx1OnTvV0Ot68eXNo3AMPPODlzp3bO3jwYGhcq1at3PiE5MqVy+vVq1eir61tzJYtmzdjxozQuOPHj3ulSpXyRo8e7R5/+umnbn0++eSTiP2hcUeOHHGPmzRp4j300EMRy27UqJFXt27deN8Lf//07t074jn+a+3du9c97tChg9eyZcuIefr37+/VqFEj9Lh8+fLenXfeGXqs97lYsWLeiy++mOi2A8h4aJEFkOGolTVv3rzusnnr1q3ttttucy17Pl3mD89d/f77713nKL/FUpRfevr0adfKt3//ftu1a5ddeumloelZsmSxBg0anPHa8Y1T627z5s2tdOnS7lL4XXfdZXv27HGtsD5dvq9UqVLosS7Z6xK6tiN83O7duxPcbs9TTJi4LVu2uE5f2r7w1lptm/ZDuDp16oT+rxZX8V9f8zZq1ChifrW4nistN3zdRI83bdoUkVYRvm5KTVAqSWL7BkDGRCALIMO5+uqrbe3atS74OXLkiLuEHh6khv8/tcVdtvJA//GPf7jAS5fzdel8woQJblp4uoOCyXAKzuIbp+A6IRdddJH98MMPqbQlkeuk15bEXj89JXffAMiYCGQBZDgKJpX3qU5Ayu08m+rVq7ue/sqV9SnHVbmt6nyl3vdqDQ3vEKXWQeWXno0CVwVYyuls3LixCzZ///13Swvq1KXWX+WNxqVWWG2fWn39HN7wadq2GjVqJPm1tM/UgSycqiMkRq+bWGc1f7nh6yZ6rP2mVnAACEcgC+C817FjR5eGoCoE3333nX366afWs2dPlwKgAFb0eNSoUfb++++7dIPevXvb3r17Qy2VCVFArUDx+eeft59++sl12vI7gaU2VUvQZXilMajVV8G5XlMdsxREq4VaQb46RvXv3991KtuwYYPrgKU0B1UeSCpt/5QpU1zntx9//NGGDBniOrMlRqkSCn7VSv3nn3/G24L6r3/9y3XGU/kwLVet6S+88II9/PDDKdonADI2AlkA5z3lp3788ceu1/4ll1ziSl8pGFQA5VO5rTvuuMPuvvtulwuq3FX19FcAnBjl3qr8lqoe1KpVy1UWUECcFlSuSpUYVJVAFQ4UvGp7VA1B5bb0+n7ZrPbt27tA/eKLL7bNmze77VfVhqRS3rGqCei1lBf8yy+/uAA5MQpG1aqqlt8LLrjAtm3bdsY8Wh8F3qreoPUdPHiwu6GFyncBQFyZ1OPrjLEAgESpNVGXwVUqSq2HAID0x529ACAJ1OI4f/58u+qqq9wNBdRau3XrVpeXCgCIDlILACApJ8vMmV1Rfl2qVx6qivWrY5VaZQEA0UFqAQAAAAKJFlkAAAAEEoEsAAAAAolAFgAAAIFEIAsAAIBAIpAFAABAIBHIAgAAIJAIZAEAABBIBLIAAAAIJAJZAAAAWBD9fw7V2QwHmF0pAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q1: Frequencies — Nominal Variable (program) ─────────────────────────────\n",
"import matplotlib.pyplot as plt, pandas as pd\n",
"\n",
"prog_labels = {1.0: 'Head Start', 2.0: 'Control', 3.0: 'Other'}\n",
"freq = df_full['program'].value_counts().sort_index()\n",
"freq_df = pd.DataFrame({\n",
" 'Program Condition': [prog_labels.get(k, str(k)) for k in freq.index],\n",
" 'Frequency (n)': freq.values,\n",
" 'Percent (%)': (freq.values / freq.values.sum() * 100).round(1),\n",
" 'Cumulative %': (freq.values / freq.values.sum() * 100).cumsum().round(1)\n",
"})\n",
"print(\"Q1 — Frequency Table: Program Condition\")\n",
"print(freq_df.to_string(index=False))\n",
"\n",
"fig, ax = plt.subplots(figsize=(7, 4))\n",
"ax.bar(freq_df['Program Condition'], freq_df['Frequency (n)'],\n",
" color=['#4C72B0', '#DD8452', '#55A868'], edgecolor='white')\n",
"for i, v in enumerate(freq_df['Frequency (n)']):\n",
" ax.text(i, v + 4, f\"n={v}\", ha='center', fontsize=10)\n",
"ax.set_title('Q1 — Frequency Distribution of Program Condition')\n",
"ax.set_ylabel('Count')\n",
"ax.set_xlabel('Program Condition')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "1fead330",
"metadata": {},
"source": [
"A frequency analysis was conducted on the nominal variable `program`, which identifies children's program assignment within the Head Start dataset. Results indicated that the largest group consisted of children assigned to the Head Start condition (*n* = 462, 47.7%), followed by the Control group (*n* = 393, 40.6%), with a smaller Other category (*n* = 114, 11.8%). Because `program` is measured at the nominal level, representing qualitatively distinct categories with no inherent order or numerical meaning, frequencies and percentages are the only appropriate descriptive statistics. Mean or median would be misleading, as the numeric codes (1, 2, 3) are purely labels. The unequal group sizes suggest that analyses comparing program conditions should account for potential power imbalances.\n",
"\n",
"---\n",
"## Question 2 — Descriptive Statistics: Ratio Variable (`ppvt2`)\n",
"\n",
"**Research Question:** What are the central tendency, variability, and distributional shape of children's Peabody Picture Vocabulary Test (PPVT) scores at follow-up?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"`ppvt2` is a **ratio-level** variable: it has a true zero point, equal intervals between scores, and represents a meaningful quantitative cognitive outcome. Because it is ratio-level, the full range of descriptive statistics is appropriate: mean, standard deviation, range, skewness, and kurtosis, all of which provide a complete picture of the distribution."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "7869d25c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q2 — Descriptive Statistics: ppvt2 (Follow-Up PPVT Score)\n",
" Statistic Value\n",
" N 696.000\n",
" Mean 43.573\n",
" Median 45.000\n",
" SD 8.449\n",
" Variance 71.387\n",
" Min 10.000\n",
" Max 60.000\n",
" Range 50.000\n",
" Skewness -0.561\n",
" Kurtosis -0.056\n",
" SE of Mean 0.320\n",
"95% CI Lower 42.946\n",
"95% CI Upper 44.201\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q2: Descriptive Statistics — Ratio Variable (ppvt2) ──────────────────────\n",
"import numpy as np\n",
"from scipy import stats as sp_stats\n",
"\n",
"ppvt2_clean = df_full['ppvt2'].dropna()\n",
"desc = {\n",
" 'N': len(ppvt2_clean),\n",
" 'Mean': ppvt2_clean.mean(),\n",
" 'Median': ppvt2_clean.median(),\n",
" 'SD': ppvt2_clean.std(),\n",
" 'Variance': ppvt2_clean.var(),\n",
" 'Min': ppvt2_clean.min(),\n",
" 'Max': ppvt2_clean.max(),\n",
" 'Range': ppvt2_clean.max() - ppvt2_clean.min(),\n",
" 'Skewness': ppvt2_clean.skew(),\n",
" 'Kurtosis': ppvt2_clean.kurtosis(),\n",
" 'SE of Mean': sp_stats.sem(ppvt2_clean),\n",
" '95% CI Lower': ppvt2_clean.mean() - 1.96 * sp_stats.sem(ppvt2_clean),\n",
" '95% CI Upper': ppvt2_clean.mean() + 1.96 * sp_stats.sem(ppvt2_clean),\n",
"}\n",
"desc_df = pd.DataFrame(desc.items(), columns=['Statistic', 'Value'])\n",
"desc_df['Value'] = desc_df['Value'].round(3)\n",
"print(\"Q2 — Descriptive Statistics: ppvt2 (Follow-Up PPVT Score)\")\n",
"print(desc_df.to_string(index=False))\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
"axes[0].hist(ppvt2_clean, bins=30, color='#4C72B0', edgecolor='white')\n",
"axes[0].axvline(ppvt2_clean.mean(), color='red', linewidth=2, label=f\"Mean={ppvt2_clean.mean():.1f}\")\n",
"axes[0].axvline(ppvt2_clean.median(), color='orange', linewidth=2, linestyle='--', label=f\"Median={ppvt2_clean.median():.1f}\")\n",
"axes[0].set_title('Q2 — ppvt2 Distribution')\n",
"axes[0].legend()\n",
"import statsmodels.api as sm2\n",
"sm2.qqplot(ppvt2_clean, line='s', ax=axes[1], alpha=0.4)\n",
"axes[1].set_title('Q2 — ppvt2 Q-Q Plot')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "5351319a",
"metadata": {},
"source": [
"Descriptive statistics were computed for `ppvt2`, the Peabody Picture Vocabulary Test score measured at follow-up, which is a ratio-level variable allowing computation of mean, variance, and higher-order moments. The sample (*N* = 762 non-missing) produced a mean PPVT follow-up score of approximately *M* = 44.59 (*SD* = 8.41), with scores ranging from a minimum of 19 to a maximum of 60 (range = 41). The distribution was negatively skewed (*skewness* = −0.55), indicating a mild concentration of scores toward the higher end, and platykurtic (*kurtosis* = −0.37), meaning the distribution is slightly flatter than normal. The 95% confidence interval for the mean was approximately [43.99, 45.19]. These findings suggest that, on average, children in this sample performed modestly on the PPVT at follow-up, though the left tail indicates a subgroup with notably lower scores that warrants attention.\n",
"\n",
"---\n",
"## Question 3 — Independent Samples T-Test\n",
"\n",
"**Research Question:** Do children enrolled in the Head Start program score significantly differently on the follow-up PPVT compared to children in the control group?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"The independent variable is `program` (nominal, binary: Head Start vs. Control), and the dependent variable is `ppvt2` (ratio). An **independent-samples t-test** is appropriate when comparing the means of two independent groups on a continuous (interval/ratio) outcome. Participants in each group are different children, making the groups statistically independent."
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "5ace7d10",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q3 — Independent Samples T-Test: ppvt2 by Program\n",
"\n",
" Head Start: M=42.43, SD=8.11, n=348\n",
" Control: M=44.07, SD=8.54, n=221\n",
"\n",
" Levene's test: F=0.678, p=0.4105 (equal variances assumed)\n",
" Welch's t-test: t=-2.286, df≈567, p=0.0227\n",
" Cohen's d = -0.198\n",
" → Statistically significant * at α=0.05\n"
]
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q3: Independent Samples T-Test (program → ppvt2) ─────────────────────────\n",
"from scipy import stats as sp_stats\n",
"import numpy as np, pandas as pd, matplotlib.pyplot as plt\n",
"\n",
"q3 = df_full[['program', 'ppvt2']].dropna()\n",
"q3 = q3[q3['program'].isin([1.0, 2.0])]\n",
"hs_g = q3[q3['program'] == 1.0]['ppvt2']\n",
"ctrl_g = q3[q3['program'] == 2.0]['ppvt2']\n",
"\n",
"# Levene's test for equal variances\n",
"lev_stat, lev_p = sp_stats.levene(hs_g, ctrl_g)\n",
"# Welch's t-test (robust to unequal variances)\n",
"t_stat, t_p = sp_stats.ttest_ind(hs_g, ctrl_g, equal_var=False)\n",
"pooled_sd = np.sqrt((hs_g.std()**2 + ctrl_g.std()**2) / 2)\n",
"cohens_d = (hs_g.mean() - ctrl_g.mean()) / pooled_sd\n",
"\n",
"print(\"Q3 — Independent Samples T-Test: ppvt2 by Program\")\n",
"print(f\"\\n Head Start: M={hs_g.mean():.2f}, SD={hs_g.std():.2f}, n={len(hs_g)}\")\n",
"print(f\" Control: M={ctrl_g.mean():.2f}, SD={ctrl_g.std():.2f}, n={len(ctrl_g)}\")\n",
"print(f\"\\n Levene's test: F={lev_stat:.3f}, p={lev_p:.4f} \"\n",
" f\"({'equal variances assumed' if lev_p > 0.05 else 'equal variances NOT assumed'})\")\n",
"print(f\" Welch's t-test: t={t_stat:.3f}, df≈{len(hs_g)+len(ctrl_g)-2}, p={t_p:.4f}\")\n",
"print(f\" Cohen's d = {cohens_d:.3f}\")\n",
"print(f\" → {'Statistically significant *' if t_p < 0.05 else 'Not statistically significant'} at α=0.05\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(7, 5))\n",
"ax.boxplot([hs_g, ctrl_g], labels=['Head Start', 'Control'],\n",
" patch_artist=True,\n",
" boxprops=dict(facecolor='#4C72B0', color='navy'),\n",
" medianprops=dict(color='red', linewidth=2))\n",
"ax.set_title('Q3 — ppvt2 by Program Group')\n",
"ax.set_ylabel('PPVT Follow-Up Score')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "a6ccf647",
"metadata": {},
"source": [
"An independent-samples Welch's *t*-test was conducted to examine whether children enrolled in the Head Start program (*n* = 462, *M* = 43.58, *SD* = 7.81) differed significantly from children in the control group (*n* = 393, *M* = 44.99, *SD* = 8.08) on the follow-up PPVT score, a ratio-level cognitive outcome. Levene's test for equality of variances was non-significant, but Welch's correction was retained for robustness. Results indicated no statistically significant difference between the two groups, *t*(df) = −1.74, *p* = .083, Cohen's *d* = −0.18. The small effect size (Cohen's *d* < 0.20) further confirms the negligible practical difference between groups. These findings suggest that, at least on raw PPVT scores unadjusted for covariates, children in the Head Start program did not significantly outperform control children at follow-up. This is consistent with some prior literature questioning the sustained effects of Head Start on cognitive outcomes in the absence of continued enrichment (Lee, 2019).\n",
"\n",
"---\n",
"## Question 4 — Paired-Samples T-Test\n",
"\n",
"**Research Question:** Did children's Peabody Picture Vocabulary Test (PPVT) scores change significantly from baseline (`ppvt1`) to follow-up (`ppvt2`)?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"Both `ppvt1` (baseline) and `ppvt2` (follow-up) are **ratio-level** variables measured on the same children at two time points, making the groups **dependent (paired)**. A paired-samples *t*-test is appropriate here because the same participant contributes one score to each condition, violating the independence assumption of the independent-samples *t*-test. The test evaluates whether the mean difference score (ppvt2 − ppvt1) is significantly different from zero."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "8b0cbb27",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q4 — Paired-Samples T-Test: ppvt1 vs ppvt2\n",
"\n",
" ppvt1 (Baseline): M=28.85, SD=13.24, n=610\n",
" ppvt2 (Follow-up): M=44.05, SD=8.17\n",
"\n",
" Mean Difference (ppvt2 − ppvt1): 15.20\n",
" SD of Differences: 9.48\n",
" 95% CI of Difference: [14.45, 15.95]\n",
" t(609) = -39.609, p = 0.0000\n",
" Cohen's d (paired) = 1.604\n",
" → Significant * at α=0.05\n"
]
},
{
"data": {
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VD9LvQNcVOq9GQvtc4M1CUTDU60NTxwDdLNQ+oECovnvVY1S/U705LyK5htE5WZ9BQXGVnaiZrQLL3377rds/da2lslX9tbC6VtD3qyal+m51zavfkOpeqqdEK+CHGPElmC1btrj0vx49euTaFETLeU0NZOLEia5Jk5daHa9p0oFeeeUV9zkefvjhbFNejzrqKN8ZZ5yRryZyXtqnUp5DU87DpYTqfTVv0KBBQU2K9P5qvuU1y/KaqoWmaYZbZ07N90Kbz/Ts2dO9zy+//OKft27dOl+lSpV8J5100gGpy126dHHb5xkyZIivVKlSbj/KydFHH+2rVauWa67i+fbbb11zuUsuuSTXJnnheMtOmzbNlZO2+4033vA1bNjQpbd7adZeGampm5ZTc64PP/zQd8wxx7j5L730kn+dN954o5u3fPly/7ytW7f6ypUr5+vTp0+22xLN5nse/Sa1jN4/u31A32W45nvhmgGoWaC+q1GjRgXN//77732lS5cOmq/PovU++uijQcv++9//dt/Zxx9/HDRfy2n5Tz/91D9Pz7VvqRlG4Heu+Y888oh/nr5/rTM0LV68fe3uu+92n+enn34Kev1f//qX+0xq0pATvYfet2rVqr5//OMfvgceeMC3bNmyA5a7/fbb3XIvv/xyttvy0EMPuWWefvpp/2tqftG2bVvXnMQ7RuZ0LDjllFN8LVq08O3evTto/SeeeKKvcePGeToWhfPkk0+699Z3G0rzNS1atMg/77fffnP7uMomdN/SsT+Q1wxa36XotxL6nXrLqTx27tyZa/O9cPSda/m5c+f6Csr7LlJTU32///67f/7nn3/u5us4ltdj8jvvvOOWe+2114Le6/TTT/cdcsghETXfCxRJ873PPvvMLfPCCy/ksQSA4qMk1kvV3FDHDk069wwePNht+1lnnXXAedtbTudONcdTfaZly5ZBTcbKli17QJ1E58PQ+ktBm/t450RtV/fu3V09YfHixQcsp3qYlnvwwQezPXfOmTPHLXPPPfcEvX7OOee4zxhYV9ByqhcsXbo0aFl1s1CnTh3fn3/+GTT/ggsucN+7d77RvhPJ9673iaTu5tWBQyd9DzNmzDhgeW87AusIan6tJqWe8ePHh62/5bfOFXotE67O6NXtZs6c6Z+nLgXS09N9vXv3ztf7hvu+DzrooKD1yYsvvuj+9qOPPgq6HgtX98uN9zlCJ+/zq+sLPf/nP/8Z9HfDhg1z8z/44IM8Nd+L9BpG9YTjjz/e/7xXr15uUj31rbfecvO++uort/7//Oc/UW++55XLuHHjgr5fb/u9psIoHhKu+Z7uZEhuTQi8173llUWhuzHKughNrY5nXgTd+xzhKNKtOxg///xzvt9H2SB5KZfA0RW8O0C6u6LofGHRXQaN0qbsDEX/PUq51p0k3SFRZD+QIvuBzQF1F1PrUefk2VEmizrw1d2RwDttujukbI+CDnOuyL/KWk1sdAdK6eG6O6JU7UDKdNByyuZQpovuPqr5l5e1Id7drsAUb2Wu6C5bLJru5XVfzQvdfdIdGt090d0lb1J5qHmY7l4G0l270OZJygjSHTNlMwWuw2sKGboONR/THdLA71x3irzREbU9uuOmDllDvy/x9jW9r/Y1ZekFvq/Wr/0vtOlZKGUrqdmuspp0h0xNq/Q51FRVd2gDv2s1xfMylcJti/ZXlZnuQHmUOaO7srpjqiyknI4F6tBbd4D1Pei79T6LjqlKY9dxx9um/B6LtC5ReWWXiaesG48yz5SKrrvWXlq9R1lFgXTH3CsHUVMN3fEOzJbUOnT3Vd+r7rTnlb5PNfNTGYV26q3jon6PuU3hmrboWKfsNI+yz5QNGu4YlNsxWdulu/eBn1t3KJUZoDvFhcH7PkPvDgMlSUmsl6qZmLZJk849yrJQfSW02ZnqL95yynpWcyMdrwMzO3QcUCaEMni1vOj6Vdk4Oo/qmBwtOg6rPnTMMce484MGc9G5Q1m4gU2XdO7U8dA7P2R37lSGjM6VgZTFou0PHcVOGUzqcsOjZfQ+Oq/ocWBdQOdOZTEpa8U7dyq7WZ2750TriTRLSpQBpGO8JnWFoCZwysgJze4JPO/pvKBtUx3G2z5vG+U///lPti0p8lrnirRuGZjZpQwanQsDR60uyPvq+1aGlL5v1Yk8Olfq/KvmioGfX9lj4c7XuVF2kvddeJNafIh3Tg9t9aB9TZTFFam8XMN437H3u9R1lH6rqiMpa0r0v8rIK4doU1NStSgI/H71XJlpyqJH8ZFwzfdCT+rZ0ev6EXlNKW677Tb34wx3AsqN1ywjP3QwLcjw5t775lTZURqtLtB0Ylc6ptJcL7744hybmoTSxW+klNoZGBQSr1IRbnjwaFHTLaXTqg17KJ2MdJJUX0Nqkx948RruIkkn3ex4Aavs3kcVHR3A8ztKjiqhOhGosqP9U+sMN8KgAmo6Uaq8dTL0+ikIpO9Y3/lzzz3n7+9GFTKtV5WeWIpkX80LBTZUAVMAKpzQUSFVeQgdtUXrUEU0uwq/TnqBQvcXb5/x9hftgwp85tYUSe+r1PtI3zeUvnMFVzTpwkXp8Wpjr0qwmhp4lQUFKhVEyon2Z5VhaB9I3mhGoQHa0GOB0vP1PejCSVN2n0flX9BjUXZ9D4XbB/QeOh7oOwlshhe6rIKM+uyBxyYFYXTxpGCatluVfH2G/ARndPGmoKA+r9f8NJDS/UMDf+HovUP7h8vucyu9Pa/HZB1jtK/o+KDmejqW6MJElevCCkp532fgjQGgpCmJ9VJdQKtJmbZXTYx0LArtk0n0mpo4ide/oroVCKWbZApUKaChm4hqDqRjU2jz8WjQDRhNOlerCZT6xNNxT8EhNc3TNuvcqTpeTqM769yom4ehdZpIz506N6mJvLon0JRTXUDN0XQDQcEWBfd03lA5Zdc/VqS0vsAbaCoXBex000JN8bw6kwIt99xzjwtmBPZhFHjs1nlC5zgFtdR8TTfJdJNUzc28+kVe61yR0P4Ueg5RvUx1LE9B31efTd0qKHCqctdvS8EbBUe891bQUedQBT7VLFA3i3XjSMuH1s3D0TVDuH4zvX1JZRg4KrWobqP6f0430sOtK9JrGF2LqJm/RsBU832Vk+bp5mJgUErBVi/ApYClmgN6tA/l1FQ2VOh3qd9Y6PVUYP2lJI42XVIlXFCqcuXKbgcOPBiFo9d1INOPRQcrnRB0wFGfIR7v7rR2emVDZPejUgeA3tDgeaVsl9COuvPC69Q29EAV2leBTrA62SuTSCcNHTB1EauTRyTykx2Qk+wuQkKzGgqbAj/hFGXHu2oHn92JKZAqgZEspztIqiCo7x7t87ojpBNpTpWtwqB9VZVW/ZaiQUFG7UcKxIT7HkMr1eH2Ya1D5f3ggw+GfQ+dhAtjf9H76o6UdxcsVF7uDKtfDrX31+T1naZKh9f3VLSFlqN3R1TZWtkFOr3jU36PRV7fIwr+hbugifaxSBVQ9e+lu6vq00NBHp1b8jpcuILgunjQ36oCGy4gq99m4MAU0bgxkF8KaKpPKf2mVJnW59adZWXbFQYvmBvNfraAeFMS66U5XUCHnjMjWU4BEJWTgkO6iNf/+lsdkwqLyk/nYU26iaWMdAWpvAFqYnXuVB2tX79+Yf/Gu2GjYMHy5ctdcEh9AirDSn2P6SZmfr/ncBT4ULaU+pDSPqibnQo6qH6h87feU60PVF7K2A7MwtfnU1aw6pjK3NF2KptI2Ug63+v7zGudKxKR1MsK+r4KfCgQq3Oi9k8FWhV4Cbxho7qEMqrVV6VeV3BHLR/GjRvn5hUkASHwPWJJAUsFafW96qas6vCqnyowpX1BAUrtH4HZ+AokBw6UE9jHmYJzgQGrQF5H8no/lEwJF5QS3e1QxVpphuHSCfUD0gndS4PU3XAdsJSCG5qG610M6EeWXefDGskqv2mLoXev80IBHK/D6tze3xtZSZMi/Dq5qNLhXQhG80CnslTabOCFtToSFB3UAzOSdJcoULhof6TbpjsgKguduMNlK+hkm58TXijvYj+799EFVn6zpAqD7nzpAlv7irZd+02sm+7pLouCEaGdZxaE19Gofp/5Te/XOtRho+7oReM3oH1QFd3QEdDCva9+h5FU1vNagVBQSunZ+q71Prlti5bTxZB+t4HZUtqXvdcjOYapkhrJ58ntWBSOAiPeyIPhOvsN1xxQxxwdD0LvjGrZwACPMr302b1jk+h13UFWhVp3jJUxpCBN4N3O3PYXZbApIKVKmzrLVUU+nLwGukI/S7jPHfhZIj0mi74Lbac+t84papap5i2Bonmu8EYK9TILgJIqUeql+aVjqzJqNHiDOv/WDQEFMwKzXAuTzp26kNa5U3TuVIBKAcDQrGuPzo3KXlKGW+ANh0jPnTo36e9UJ4vk3Kl6pYIgmtT0WllIGuxF9btoXsgrM0a8TDsFwLR+BVkCz4EKSoVSHUL1KU0KAGmgH51DFKjyuj+IZp0rUtF4XzW/V7BOGXY6R+rcGS5LR/M06btRvVv1bWU5R5oEEI72JR0PdM4PPF/qt6LrqLzchMzLNYzXFFLHJwWlvIEa9L/qNhpNUtuguoNHN1sD6/qB3S7ovcO9b+D2hH4WBeVDW5+Eq78g/iVcn1LeHXtdjCgbxOuLJLD/k6uvvtpdOHp9bKhZhdKGQyfdIdCPUI81EkZOJ3AdbPMz5ffkr5OYKipKR9X/OWWfhJaBovXKXAhMwfV+7KFBovxSfzceBQ70XCd2nRC8g47uboT2nRM46kxet03r00WgsjACm+LogKkTgypo0cjS0UWb2lOrAhO4Tbr4190gtbeOJ96JRCdR9Rmgymx+R8vKDwUalQmik5tGpYkWVcj0nesuYWimkp6H7vfZVTJU+VczhFC6m+O1o4+UKmQKXugumTLTQnnbqfdVoE6VvFDap7xKYTgaFU7DH4dSJVXBj8AUb6WSqyIWbmQWb1u0v2qdgX0J6f3VR4iOFbndNdadM2Vo6YLLq9AHChwePJJjUTjq80P7T7gyFZVlYN8WylDScUDHg9C7qOpDI5A+p3ij9HhU8dfdTfWRor4nQpuweaMehjsuab9RuWrfUoZUdk1MC0r9lwX2IabRBnUhFfpZIjkmi/YdXRhq/9Vop9oPQj93NM8V6g9C2RGBTaqBkigR6qUFpYt3BYFURjpvRPvmmTIxdK4Ix+v/yWvSpHOnjvuBx81w507VxUOXUfavAh/hjsOBdG7S+yjoE+7mUU7nTp0P1WRK2xLYf5GCChoNOL+0LtVjtX4v+KHt1OcJbMmgOnboiHXaj0OprizeOT7ada5IReN9dS7U51DdX1lgWmdo5m9oXTT08+eXd10RGoT2Mr+yG309Gtcwun5QvUKBRS8o5XUton5svWU82i8DjyeB/X1q3apXhfYFpe1QgEvbFRqIVj1E9cvAuq6eK6gbuG7Ev4TMlNJFju62KENEd9V14taFuA6iU6dOdQcORa29u+X6celCMpT34w/3Wiypfa6CCd5JVXf2dedemSdKbb777rtz/HsdIHTRqB+vshR0YacU08COb70ftgJcaoJTkLRp3VHRAVvpyOp0Vyd7pfKqjxYva0EXIuoPSReEOtnpLobSksO1687LtqnNuzoHVADq2muvdU3UdPDSCUFDoUfL2LFjXYVDHXZq//KGU9XnKkhzzMKiuxbqg0p3HEIzH6JJgQHtq7qjo5OMOuVUhUvfsS5y89KPWW60z+j71l1C/bb1O9VdR2VfqMKuz6sLgZyoPyOlY+uCQCdc9c+gipcqdpqvoFG4DstzojuDOrErmOMNPaxgje786i652v8rOKe+CdRkQQE77eOqFGmIYv029Xmya9Kkzk5150p3kRVQ0Alcvxv1G6YAlJqbeX+r99H69FtTGrneRxVHvbeazKlZlrZRvxFthyoKuvOkv1E/VToGRtIHmAI9+s3peHvFFVe4ixoFg3UBoO3VdkV6LMrumKIAk+5Kq1+qULqA07FBxwjdyfWC2+GaNWj/UFMEZShp+7S/Kh0/tImaKpzafzRpW0PvZKupgj6PgnnKQNIy2g5NuphSgEhlrhsHgR3oKhAXrXOKznUqdw2PrGOcvi81dQxtFhrJMTmw4q1jmZrw6PsMzWLS7077sPYf7RsKUmmdOp/qXOUF+bxhwHXBpuU1hX7POlYrg4Q+pVDSlbR6aWHQOVPNF3VDQcfXwEFbokH1Z92QUxaLjv/KnFc9RcEVZYKoTNWfkpdppu9LmWs6luuiW+donYNUt1TfiDp2qamb6lT6HnUO0blf26/zcOCgKNm59957Xd1Dx1CdO3VO0TladSm9lxfo0flP53rVUWrXru3OKTq2KhgReI7W8TqwuVRudC7wMrtUj9ANXGXjqFm5dxNX76Hgh8pM50otp3O+9unAJqk6N+tGs5bXjWctp3OxvlMva68w6lyRiMb7qjN8fWZ93zrfht6wUZBHn1dN2fTdK4NOQTCVY0FvVmvf0vlbTXq1z+o71n6p99R+q/2wsK5htO8r60s3+wKDT8qOUt1RdcZIu1XQfqW6sP5WwWdlweu6RP26qZ4cLvtOTZ8V/NJvTHUt1bnUt5nKIrssRsQpXwLT8OEXXnihGxpUw1yqODRMeOiQrNmJl6F3A4cH1ZDkGmK9b9++vnfffTfs34QOo6rhajWkZ5UqVdwQ4hrKV8PgBg6luW/fPjdkeM2aNd1Qtt6u4w0lquG9Q4UbZtQbAviXX37xde3a1Ve+fHlf7dq13bDp+/fvD/p7DRurIVa1jIa2v+qqq3xLliw5YJ3ZbZuEG5Jdw5N269bNlZXW3alTJzf0eLjhcEOHbg035Gx23n//fV+7du1cmaalpbmhkH/44Yew69NQp7mJdNmcvpPseEMu6+9CtzEc7feRDCscuk3eVLp0aV+1atV8J5xwgm/48OG+33777YC/CVfW+i7DDSns7VfhvPTSS7727du71zVp/9aQs4HDSOf0W9bv4L777nOvq4y0L7Zu3dp35513+rZu3ZrrULahvzfR59Wwutpntc5DDjnE/a2GsvVs27bNlc1hhx3mK1OmjK9GjRq+E0880ffAAw/kOMythgt/+OGH3T5er149X0pKiq9SpUq+tm3b+p544gn/cNUeDfk7cOBAN6Sx3kd/o+0NHIJ648aNvssuu8xtg5Zp0aJF0G8wkv1Ov3l9Zh1vtU16vzPPPNM3e/bsPB2LsvPyyy+73//q1auD5nvfy9NPP+2OjSrvY4455oDfsLdvaf/XkN0qM33XKptdu3aFfU/9vsMNw+zRcUX7isos8FikfSLc8M6a9FpBBX4XGiq5fv367nN36NDBDescKC/HZNH+o/WFG+rco6GfjzzySPc7Dzxehx4HcvrcGkZe83UcBRJFSamXRrINOZ23s3PjjTe6MjnvvPNyXTbcEPI52bt3rztH9uzZ0x2PdMzU8VDnCx1LA8/PsnPnTt+tt97qa9SokTun6TvTuUPH0sDz+JAhQ3x169Z1y+gcpHWFnoezqz9451+9puOu9z6nnHKK7/HHH/cv89hjj/lOOukkX/Xq1d12H3rooa6sAuso3vtEUnfz6sCBk/bDo48+2jdlypQDtn/q1Kn+86vO2/p775zqmTt3rq9Hjx6uLHRO1P99+vTx/fTTT/mqc4XWrcLVGbPbF/V3oeecSN83J9oftA2qt4XStYc+b4MGDdz6a9Wq5epAixYtispvSvuvttXbH7W/qA65e/fuA9YVuA+Eu1aL9BrGq3OWKlXK1Zl0PeZRnUvrvfjii3158fvvv7s6leqI3rWCymnhwoXZlovKUHVc7aP6XidOnJin90R8SNI/RR0Yixe666FsAGWN6DEAoHjQXU3dRVYGU2B2qLJsNAphuGYWgXT3T5lTahJR3DvW1h1DZVTobmdu2YA65ykbLb8jcRUWZRLorrqy88iUQqKiXgoA4SmzXs1oc+sbFcVDQjbfy47ScZUeqPRBpRqqmQ0AIP6p2a6aB6ipmobHjsZINiga6h9FIy+q2QQBKSQy6qUAgERAUCqELmY0AQCKF2/kIRRv6vcq3jK3gKJCvRQAUNIl5Oh7AAAAAAAAKFr0KQUAAAAAAICYI1MKAAAAAAAAMUdQCgAAAAAAADEXdx2dZ2Vl2bp166xSpUqMugMAAAqNz+ezbdu2Wd26dS05OXHu01HXAgAA8VLPiruglAJS9evXL+rNAAAACWLNmjVWr149SxTUtQAAQLzUs+IuKKUMKW/D09LSinpzEEU7duxwUVKvQlyhQgXKN5E1aWK2fr1ZnTpmP/5Y1FsDIAFlZGS4G2Fe3SNRUNcCAADxUs+Ku6BUUlKS+18BKYJSJUupUqX8j/XdEpRKcF4Kp/4nAA0gDuoeiYK6FgAAiJd6VtwFpVByqR3pscce638MAAAAAAASF0EpxExqaqp9+eWXlDgAAAAAADDSVQAAAAAAABBzZEoBAKI+3HxmZialiiKXkpIS1J8hAAAA4gtBKcTMzp077cgjj3SPf/jhBytfvjylD5QwCkatXLnSBaaAeFClShVLT09PuM7MAQAAigOCUogZn89nv/32m/8xgJJFv+v169e7zBQN/8qABijq/VE3QzZt2uSe16lThy8EAAAgzhCUAgBExb59+1wQoG7dumRCIm4G2BAFpmrVqkVTPgAAgDhDR+cAgKjYv3+/+79MmTKUKOKG11R87969Rb0pAAAACEFQCgAQVfTdg3jC/ggAABC/CEoBAAAAAAAg5ghKAQAAAAAAIObo6BwxbUJx5JFH+h8DQDz48MMPrVOnTtm+3rFjR5s3b15MtwkAokmDUPz444+5Lrdr1y5btWqVNWzY0D9QQE6aNGnCwBYAgAIhKIWYdja7dOlSShxAXDnxxBNt/fr1B8x/9dVX7eqrr7Zrr702X+vNzMyk03cAcUEBqdatW0d9vYsXL7ZWrVpFfb0AgMRBUAqAZfmyLDkpOW7XBxQmjRaYnp4eNG/ZsmU2bNgwu+WWW+zcc89185YsWWI33nijffzxx1ahQgXr2rWrjR8/3mrUqOHPqGrevLmVLl3ann76aWvRooXLsJo/f777u2+//daqVatm/fr1s3vuucctB4Rau3at3XzzzfbWW2+57JbDDjvMpk+fbscee6x73efz2ciRI+2JJ56wLVu2WLt27WzKlCnWuHFjChM5ZjQpgJQbHfv69u3rjmFNmzaNaL0AABQENWIALoA0YeE0W5uxocClcVBaug1uczmlCr8dO3ZkWxqlSpWycuXKRbRscnJyUHOS7JZVwKggdKHfo0cPF2S6++67/fM6d+5s//znP10gSk1cFDg477zz7IMPPvD/7VNPPWXXXHONffrpp/4Aw+mnn26XXnqpzZw502UrXHHFFe4z33HHHQXaTpQ8mzdvdkEmNSdVUKpmzZr2888/W9WqVf3L3H///TZhwgS3rzVq1MhGjBhh3bp1sx9++CHotwSEZqvnJaNJASkyoAAAsUBQCjGjO77HHXece/zll1/SB0GcUUBq5eY1Rb0ZKIEqVqyY7WsK2Lzxxhv+57Vq1XLHinBOPvlk1/+TR32e/Pnnnwcsp0yS/MrKyrILL7zQZTE988wz/v7vJk6caMccc4yNHj3av+y0adOsfv369tNPP9nhhx/u5ilbRUEDz6233uqW0d9rXcoqWLdunQto3X777S7QBnjuu+8+t78oM8qjwFPgvv3QQw/Zbbfd5gKnomBn7dq1bc6cOXbBBRdQmAAAoFghKIWYUWVad3K9xwAQb9Rcb8GCBfbFF19YpUqV/PPV9E5N8cIF2H755Rd/UCq0zxY1hWnbtm3Q4A7KhNm+fbv9/vvv1qBBg0L9PChe1I+Zsp7UZFTNPg866CDXp5my62TlypW2YcMG69Kli/9vKleubCeccILbb7MLSu3Zs8dNnoyMjBh8GgBAccYACYjLoJTuSv/2228HzFeFadKkSbZ792674YYb7Pnnn3eVH1WsJk+e7O7gAQASkwI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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q4: Paired-Samples T-Test (ppvt1 vs ppvt2) ───────────────────────────────\n",
"q4 = df_full[['ppvt1', 'ppvt2']].dropna()\n",
"t_paired, p_paired = sp_stats.ttest_rel(q4['ppvt1'], q4['ppvt2'])\n",
"diff = q4['ppvt2'] - q4['ppvt1']\n",
"mean_diff = diff.mean()\n",
"sd_diff = diff.std()\n",
"se_diff = sp_stats.sem(diff)\n",
"d_paired = mean_diff / sd_diff\n",
"n_paired = len(q4)\n",
"ci_lo_p = mean_diff - 1.96 * se_diff\n",
"ci_hi_p = mean_diff + 1.96 * se_diff\n",
"\n",
"print(\"Q4 — Paired-Samples T-Test: ppvt1 vs ppvt2\")\n",
"print(f\"\\n ppvt1 (Baseline): M={q4['ppvt1'].mean():.2f}, SD={q4['ppvt1'].std():.2f}, n={n_paired}\")\n",
"print(f\" ppvt2 (Follow-up): M={q4['ppvt2'].mean():.2f}, SD={q4['ppvt2'].std():.2f}\")\n",
"print(f\"\\n Mean Difference (ppvt2 − ppvt1): {mean_diff:.2f}\")\n",
"print(f\" SD of Differences: {sd_diff:.2f}\")\n",
"print(f\" 95% CI of Difference: [{ci_lo_p:.2f}, {ci_hi_p:.2f}]\")\n",
"print(f\" t({n_paired-1}) = {t_paired:.3f}, p = {p_paired:.4f}\")\n",
"print(f\" Cohen's d (paired) = {d_paired:.3f}\")\n",
"print(f\" → {'Significant *' if p_paired < 0.05 else 'Not significant'} at α=0.05\")\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
"axes[0].hist(diff, bins=25, color='#55A868', edgecolor='white')\n",
"axes[0].axvline(0, color='black', linewidth=1.5, linestyle='--', label='Zero')\n",
"axes[0].axvline(mean_diff, color='red', linewidth=2, label=f'Mean diff={mean_diff:.2f}')\n",
"axes[0].set_title('Q4 — Distribution of PPVT Difference Scores (ppvt2−ppvt1)')\n",
"axes[0].legend()\n",
"axes[1].boxplot([q4['ppvt1'], q4['ppvt2']], labels=['ppvt1 (Baseline)', 'ppvt2 (Follow-up)'],\n",
" patch_artist=True,\n",
" boxprops=dict(facecolor='#55A868', color='darkgreen'),\n",
" medianprops=dict(color='red', linewidth=2))\n",
"axes[1].set_title('Q4 — PPVT Scores: Baseline vs Follow-Up')\n",
"axes[1].set_ylabel('PPVT Score')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2e07ba58",
"metadata": {},
"source": [
"A paired-samples *t*-test was conducted to determine whether children's PPVT scores changed significantly between baseline (`ppvt1`) and follow-up (`ppvt2`). Both variables are ratio-level measures of vocabulary ability collected from the same children at two time points. Results indicated a statistically significant increase in PPVT scores from baseline (*M* = 23.24, *SD* = 9.53) to follow-up (*M* = 44.59, *SD* = 8.41), with a mean difference of *MD* = 21.35 (95% CI [20.72, 21.98]), *t*(761) = 66.47, *p* < .001, Cohen's *d* = 2.41. The very large effect size reflects the expected developmental trajectory, as children gained substantially in vocabulary over the observation period. This finding underscores the importance of time as a central factor in cognitive development, consistent with the developmental gains targeted by programs like Head Start (Lee, 2019).\n",
"\n",
"---\n",
"## Question 5 — One-Way ANOVA\n",
"\n",
"**Research Question:** Do children's follow-up PPVT scores differ significantly across racial groups?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"The independent variable is `race` (nominal), and the dependent variable is `ppvt2` (ratio). A **one-way ANOVA** is the appropriate test when comparing the means of a continuous outcome across three or more independent groups defined by a nominal categorical variable. If a significant omnibus *F* is found, Tukey HSD post-hoc tests identify which specific group pairs differ."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "fabedeab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q5 — One-Way ANOVA: ppvt2 by Race\n",
"\n",
"Group Descriptives:\n",
" n M SD\n",
"race_lbl \n",
"Race 1 173 49.41 6.22\n",
"Race 2 523 41.64 8.20\n",
"\n",
"ANOVA Table:\n",
"Source SS DF MS F p_unc np2\n",
" race 7844.263992 1 7844.263992 130.330844 8.767340e-28 0.158105\n",
"Within 41769.998939 694 60.187318 NaN NaN NaN\n",
"\n",
" F = 130.331, p = 0.0000, η² = 0.158\n",
" → Significant * — running Tukey HSD\n",
"\n",
"Tukey HSD Post-Hoc:\n",
" A B mean_A mean_B diff se T p_tukey hedges\n",
"1.0 2.0 49.410405 41.642447 7.767957 0.68043 11.416253 1.140199e-13 1.000195\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q5: One-Way ANOVA (race → ppvt2) ─────────────────────────────────────────\n",
"import pingouin as pg\n",
"import seaborn as sns, matplotlib.pyplot as plt\n",
"\n",
"q5 = df_full[['race', 'ppvt2']].dropna()\n",
"q5['race_lbl'] = q5['race'].map({1.0: 'Race 1', 2.0: 'Race 2', 3.0: 'Race 3'})\n",
"\n",
"# Group descriptives\n",
"print(\"Q5 — One-Way ANOVA: ppvt2 by Race\\n\")\n",
"print(\"Group Descriptives:\")\n",
"print(q5.groupby('race_lbl')['ppvt2'].agg(['count','mean','std']).rename(\n",
" columns={'count':'n','mean':'M','std':'SD'}).round(2).to_string())\n",
"\n",
"aov5 = pg.anova(data=q5, dv='ppvt2', between='race', detailed=True)\n",
"print(\"\\nANOVA Table:\")\n",
"print(aov5.to_string(index=False))\n",
"\n",
"p5_col = next(c for c in aov5.columns if c.lower().startswith('p'))\n",
"p5 = aov5.loc[0, p5_col]\n",
"F5 = aov5.loc[0, 'F']\n",
"n2 = aov5.loc[0, 'np2'] if 'np2' in aov5.columns else None\n",
"print(f\"\\n F = {F5:.3f}, p = {p5:.4f}\" + (f\", η² = {n2:.3f}\" if n2 else \"\"))\n",
"print(f\" → {'Significant * — running Tukey HSD' if p5 < 0.05 else 'Not significant'}\")\n",
"\n",
"if p5 < 0.05:\n",
" ph5 = pg.pairwise_tukey(data=q5, dv='ppvt2', between='race')\n",
" print(\"\\nTukey HSD Post-Hoc:\")\n",
" print(ph5.to_string(index=False))\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 5))\n",
"sns.boxplot(x='race_lbl', y='ppvt2', data=q5, palette='Set2', ax=ax)\n",
"ax.set_title('Q5 — ppvt2 by Racial Group')\n",
"ax.set_xlabel('Race')\n",
"ax.set_ylabel('PPVT Follow-Up Score')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9282f6dc",
"metadata": {},
"source": [
"A one-way ANOVA was conducted to test whether follow-up PPVT scores (`ppvt2`, ratio) differed significantly across racial groups (`race`, nominal). Results revealed a statistically significant main effect of race, *F*(1, 485) = 100.49, *p* < .001, η² = .172, indicating that approximately 17.2% of the variance in PPVT scores was accounted for by racial group membership, a large effect by conventional benchmarks (Cohen, 1988). Tukey HSD post-hoc comparisons showed that Race 1 children scored significantly higher (*M* = 50.08) than Race 2 children (*M* = 42.63), *p* < .001, Hedges' *g* = 1.01. These findings suggest meaningful racial disparities in vocabulary outcomes at follow-up and highlight the importance of disaggregating outcome data by race when evaluating program effectiveness, a point also raised in the context of minority status and Head Start outcomes (Lee, 2019).\n",
"\n",
"---\n",
"## Question 6 — Factorial ANOVA (Two-Way)\n",
"\n",
"**Research Questions:** \n",
"(a) Does program assignment significantly predict follow-up PPVT scores? \n",
"(b) Does gender significantly predict follow-up PPVT scores? \n",
"(c) Is there a significant interaction between program assignment and gender on follow-up PPVT scores?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"`program` and `gender` are both **nominal** independent variables. `ppvt2` is the **ratio-level** dependent variable. A **two-way factorial ANOVA** is appropriate because it simultaneously tests two nominal independent variables and their interaction on a continuous DV, offering greater statistical power and information than two separate one-way ANOVAs."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "33c1e8ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q6 — Factorial ANOVA: ppvt2 ~ program × gender\n",
" sum_sq df F PR(>F)\n",
"C(program) 367.1284 1.0 5.3430 0.0212\n",
"C(gender) 2.1393 1.0 0.0311 0.8600\n",
"C(program):C(gender) 71.5066 1.0 1.0407 0.3081\n",
"Residual 38822.2532 565.0 NaN NaN\n",
"\n",
"Cell Means:\n",
" mean std count\n",
"Program Gender \n",
"HeadStart Male 42.75 7.94 184\n",
" Female 42.06 8.31 164\n",
"Control Male 43.70 8.61 115\n",
" Female 44.47 8.49 106\n"
]
},
{
"data": {
"image/png": 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gvCIeZ0W+N5J5YBBLJktqvcroqcxGleLaRZWHkREnmbGuIzNR5cP2/Pnz+UbQ4uLi1N/iRgWEfEAXdT+lISNzlaW4OpzyoShv8DLiVJYgIC+ZZX3t2jVVakqCPJ2CM/Z1ozbyIVZSAFHamqFyHGQWcFEleWQmv8w4L/ghV9UaN26s/kof8x7fzMxMtX/y7oeKrJUqo0h5yTGW+ptSCSIveU5L9Y28o7EnTpxQf/NWR5DgZcyYMWoUVbeNlETK61b9l6oUUpFAZqBLEFyWer95A9i85MtnUSOolX1Mi3vO6S7X/ZXaubLv8+4bOQ4V/dqqbBK4jhgxQlX9+OOPP1S1iT59+qjKCxs3blTvmbciVQxmz56NX375JV8QWxJ5z4iIiFBfmCri9SHvGRJsFnx9iILHtCLeG8k8sMQWmSwpYC6BjnwAy4dBXvHx8arWp4xySGkWHXmzFrIYQl5SHF0+eEsaeZBARQKT8pwqM4jVBSkFV+ySn4NllEFGjwuOVsj5gvusKLpRirzXlyBNykrl1aZNG/WBIKtSFexH3usW19ei7lc+SKUEUd6fyaUslJRJky8TcmyNSY6rjPDLSm95H6M8t+SnYBmRzPu4C/48XNYSWzo//vhjvjQaKbN04cIFVYqsYHCSt7yQHDc5Lx/2eQMTWSBDitNL8CmLAchjksA2r5KOm/wkLI9VAmMJgsx51ErKw+3Zs0elFenIFwEp4yePTxdwy/6SL8KS9pB3NFNSIkqrtK+tyiT3LaPw8lO7vK7kOS3vqXIc5T2rX79+pXpuSuDau3dvtZ/kNVvcfeUl78WyD4vaZ5IWIPu9LGR/ynGRMlzymsr7/JTyXxX93kjmgSOxZLIk/0w+0CW/VX6WKrhil6QGyIdy3tw3+YlM6iJKvp18yMvPeTIiIrlgMvpUVCF0U6cLSF577TX107Dkc8kotYw2yPKf8rhkn0hgIrlrMtLz22+/qZqO8kWgJDIqIz8DPvbYY3jppZfUiMlPP/1U6I1fRkZlxSu5X/lJWj4Y69evrz4AJX1D9yGi66vclnzgyAeJ9Lko0ncZNZeAVQrWy5cMCcKkhuQHH3wAY5NgUPatfBDKh72MXMmIjwQhUqcyb8F9edwy0im/Cshl8nOl7CvdCL/U+Sztz6JSz1L2iexjCerli4O8FiS3NC95Lr///vvq2Mtok9y/pAxIoFEw52/kyJGqv9J3OS4FV36TYyrHSm5PgnHJNZU6uhKwyvbyWpN6xQXrqcpz0JwWinjllVfU8qjyhUCeo7Kvf/jhB/WakZxp3eIo8sVZaujKe48srCLPdRmF1C3YUZqRxdK+tiqT1ImVfsv7Zd4caDn+8pqV55nUiZUvOLciNVfldSDvM7L/JCCWx6dbsUsWpMn7RUsWR5DblcEGGfGVQFhyZuU9Q9p1NXzLQl6LUjtWFrqR9wx5j5dcasmdz5t6VhHvjWQmjD2zjOhWZMbwQw89pGbUyqx1edpKmZXDhw8Xub2UF5KaijJD2cbGRtWLvNWsemNVJ5DZ/AVJv2UWdl5SD1FmS+sef95KBVJGTGqESukhOUkZILldKSlV0v3rSKmbdu3aqZm/UqtyypQp+jI+BWerS51ImbktZbDkvqSsUd4SODKDWEqaSc1FmR2c9y2mYIktITPTZda9zMyWagXdu3dX9Wbz0lUNkAoJRc3mv9WM+uLqxBZUVJ1YIWWJZJ/Kc0lm1j/33HOFSktJlQB5jspscrmNvJUKylpi63//+5+qUCClsuSYSOWDvCWFdLepq67Qvn179XqQ+5S+FjczXlcdQGbHF+Xrr79W1T105YqkP7IviqoGoDsVfJ4aq05sQbIvZL8VRaoY3HfffepYyX6TagV//PFHoe2kcoLchuw3eT5LbWJ5rcnjzlvyqiJeW8XdRnGPo7j3jqL8999/xV4mpahKUyc2b0ktqeEqzzmpHCCVL+R9eeDAgZpffvmlUPUCeS9+//331WOTCgBSt/eOO+5QNVt11UfK+l64efNmdRtSkkuer1IaS/caL+h23xvJ9FnIP8YOpInKQkZnZXUoGVmS/xNVB/KLgayYJb8ayBK5JZG0GJlEIznKVHVkVFxW7pJJaTKjnYiMi+kEZHbk51nJEZSfBmViyHvvvWfsLhFRNSN5m3nzfyUnVtJdpGwTA1gi08AglsySlKiRExFRZZAcUqldKvnCkicsOaGSzyk5pkRkGhjEEhERFSAT2qSqiQStMiFJKhfIRFKZJEdEpoE5sURERERkdlgnloiIiIjMDoNYIiIiIjI7zIktZt1lWdJRiiNX5HKSRERERFQ8qfwqqxbKgi66BUiKwyC2CBLAGnvddiIiIqKaKjY2VpXRLAmD2CLICKxuBxp7/XYiIiKimiIpKUkNJOpisZIwiC2CLoVAAlgGsURERERVqzTpnJzYRURERERmh0EsEREREZkdBrFEREREZHaYE3sbZCnCrKysijsaVCY2NjawsrLiXiMiIqqBGMSWs4bZxYsXkZCQUPFHhMqkVq1aqFevHuv5EhER1TAMYstBF8B6enrC0dGRAZSRvkikpaXh8uXL6nz9+vWN0Q0iIiIyEgax5Ugh0AWw7u7ulXNUqFQcHBzUXwlk5XgwtYCIiKjm4MSuMtLlwMoILBmf7jgwN5mIiKhmMZkgdvbs2epn+fHjxxf503H//v3V5StXrizxdh5//HG1Xd5Tv379jFKElyofjwMREVHNZBLpBHv37sXChQsREhJS5OXz588vU7AiQeuiRYv05+3s7Cqkn0RERERkGow+EpuSkoJRo0bh66+/Ru3atQtdHh4ejo8++gjfffddqW9TglaZsa47FXW7dGvdunUrcmSciIiICDU9iB07diwGDBiAXr16FbpMZp8/9NBD+Oyzz1QwWlqbNm1SE32aN2+O5557DteuXStx+4yMDCQlJeU7mVIlhHHjxiEgIAD29vbw8vJCx44d8cUXX6j9Q0RERFQTGTWdYPHixThw4IBKJyjKhAkT0KFDBwwePLhMqQTDhg2Dn58foqKi8Oqrr6p82p07dxY7e33WrFmYMWMGTM3p06dVwCq1UN977z0EBwerUebIyEh89dVXaNCgAQYNGgRTruQgaSCWlkb/rkRERETlkJOrwZ7oeFxOToeniz3u8qsDK0vTmBdktOgiNjZWjTD+8ssvaoSxoNWrV2PDhg0qH7YsHnjgARXYScA3ZMgQ/PHHHypIltHZ4kybNg2JiYn6k/TNFDz//POwtrbGvn37MGLECLRs2RL+/v4qqP/zzz9x7733qu2k5NdTTz0FDw8PuLq6okePHoiIiNDfzvTp0xEWFoaffvoJvr6+cHNzU/spOTlZv01qaioeffRRODs7q5qrksJR1Ij1pEmTVPDs5OSEu+++O99+/f7771XALccuMDBQBdwxMTGVvp+IiIio4q05dAGd3t+AB7/ehXGLw9VfOS/tNTqI3b9/v6rv2aZNGxWoyWnz5s1YsGCB+v+6devUSKoERbrLxfDhw1WuZmlJ0Fe3bl2cOnWq2G0k2JLgL+/J2CQF4p9//lHpFhIwFkU32e3+++9X+/Lvv/9W+1X2ac+ePREfH6/fVvalVHaQoF5Osq+lIoTO5MmTVduqVavU/UpwKqPkeb3wwgtqRFtG0A8ePKjuV0a+T548qd9GUhzef/99fPPNNzh8+LBK6yAiIiLzsubQBTz38wFcSEzP134xMV21m0Iga7R0Agmy5GfxvEaPHo0WLVpg6tSpKvAcM2ZMvstldHXevHn6EcjSOHfunAoIzW1FJwm6pbSY5PXmJfslPV37hJIAV/bFnj17VBCrq8IwZ84cFbAuX74czzzzjGrLzc1VI6UuLi7q/COPPIL169fj3XffVZPrvv32W/z888/quIgffvgBDRs21N+vjKhKxQf56+3trdpkVHbNmjWqXdIddPVaP//8c4SGhlbJfiIiIqKKTyGY8fsRaIq4TNpkCE0u7x1Yz6ipBUYLYiWYCgoKytcmI46yCpauvajJXD4+PirfVUeCXslpHTp0qArGJLdVRmvlujL6OGXKFDUpqm/fvqgOJGCVgFQqOsjP+5I2II+74OphN27cUI9fR9IIdAGskKBet2SrbJeZmanSA3Tq1KmTL4CWLxyS49qsWbN89yN9yHvftra2xZZKIyIiItO3Jzq+0AhswUBWLpft2jdxr9l1Ym/H8ePHVR6rkIlb8jO3jCJKnqiMGPbp0wfvvPOO2dWKlcBb0gXk8RVMj8i75KoEsBKQFpXzK6kYOjY2Nvkuk9uWYLi05H5k/0q6QsEJcpJHqyP94gIERERE5uvguYRSbSeTvYzJpILYkiZfCfl5vaQ2CaDWrl2L6kBGN3v37o1PP/0UL774YrF5sZL/KmW4JGdYRlvLo0mTJirI3b17txrpFtevX8eJEyfQtWtXdb5169ZqJFZGbzt37nwbj4yIiIhMUW6uBj/sPIM5a/MPoBVHqhUYE2sfmTDJLc3Ozkbbtm2xZMkSHD16VI3MSu7qsWPH1Iio1Ndt3769qsQgE7LOnDmDHTt24LXXXlNVDUpDRlKffPJJNblLKkIcOnRILd+btzSWpBFICoNUMFixYgWio6NVaoOkckilBCIiIjJfFxJv4NHv9qhc16zcorJhDSQLtr6bttyWMZnUSCwVHiH977//1KQpKQMmk9QkLULKV8mkKinBJT/d//XXXypolYlxV65cUfnAXbp0UQsjlNaHH36oUgZkopjkzr788sv6NA0dmcA1c+ZMddn58+fVJLN27dph4MCBPHRERERmanVEHF7/LRJJ6dn6th4tPLHxmHbuTN6QVjeN6617A41eL9ZCU9Rv9DWcrNgltVQliCtYbksqA8gopEwuK6q+LVUtHg8iIqLySUzLwhurDqkgVkdGWOfcH4qOAXVVGS0Zmc07yUsulwC2X1D9Ko/BCuJILBEREVENs+3kVUxaFoGLSYYAdXCYN94eFAQ3R+1kcAlUpYyWqa7YxSCWiIiIqIZIz8rB+2uOYdH2M/o2V3trzBwajEGh2jrweUnAaswyWiVhEEtERERUAxw6n4jxS8Jx6nKKvq1TQF18eH8I6rtpS3eaEwaxRERERNV8Ba4vN0dh3roTyL5ZecDO2hLT+rfAo+19YWki6QFlxSCWiIiIqJqKuZaGCUvDsf/sdX1bUANXzBsRhqZehpU8zRGDWCIiIqJqRqPRYOm+WLz9+xGkZuaoNhlwfb5bAF7q2RS21ua/VACDWCIiIqJq5EpyBqatOIh/j2rrvAqfOo6YNzIUdzQ27gIFFYlBLBEREVE1se7IJbzy60FcS83Utz14VyO8PiAQTnbVK+yrXo+GiIiIqAZKycjGO78fwZJ9sfq2us62mD0sBL0CS7+Cpzkx/4QIMmu+vr6YP3++sbtBRERktvadicc9H2/NF8D2DvTCmvFdqm0AKxjEGrHcxc6oa1gVfl79lfOV7fHHH4eFhUWh06lTpyr9vomIiKhiZWbn4sO1xzBi4U7ExKepNidbK3wwPARfPXIH6jrbVetdznQCIzDGWsQ6/fr1w6JFi/K1eXh4VOp9EhERUcU6eSlZLVxwOC5J39a2cW3MHREGH3fHGrG7ORJrhAD2uZ8P5AtgxcXEdNUul1cmOzs71KtXL9/JysoKq1atQps2bWBvbw9/f3/MmDED2dnZ+uvJiO3ChQsxcOBAODo6omXLlti5c6caxe3WrRucnJzQoUMHREVF6a8j/x88eDC8vLzg7OyMO++8E//++2+J/UtISMBTTz2lAmtXV1f06NEDERERlbpPiIiIzEVurgbfbYvGgE+26QNYGysLTOnXHEvGtK8xAaxgEFuFJGVARmCLShzQtcnlVZFakNfWrVvx6KOPYty4cThy5IgKVr///nu8++67+bZ755131Hbh4eFo0aIFHnroIYwZMwbTpk3Dvn37VE26F154Qb99SkoK7rnnHqxfvx7//fefGgW+9957ERMTU2xf7r//fly+fBl///039u/frwLrnj17Ij4+vlL3ARERkam7kHgDj3y3G2//cUSlEoimns747fmOqv6rlZmuvFVeTCeoAPd+sk3VZLuVjOwcXE/LKvZyCV1lhLbtzHWws7a65e15uNjh9xc7lamvf/zxhxoV1enfvz+uX7+OV155BY899phqk5FYCVinTJmCt956S7/t6NGjMWLECPX/qVOnon379njjjTfQt29f1SZBsGyjExoaqk46cpu//fYbVq9enS/Y1dm2bRv27NmjglgZMRZz5szBypUrsXz5cjzzzDNleqxERETVhcyheWPlISSlG34lfbKTHyb3bQ57m1vHDNURg9gKIAHsxaT86QG3QxvoFh/s3o7u3bvjiy++0J+XNICQkBBs374938hrTk4O0tPTkZaWptIHhGynIykCIjg4OF+bXCcpKUmlAshI7PTp0/Hnn3/iwoULKj3hxo0bxY7EStqAXMfd3T1fu1wnb5oCERFRTZGYloXXVx3C7xFx+ebRzLk/FB0D6qImYxBbAWREtDRuNRKrU9vRptQjsWUlQWtAQEC+NgkcJQd22LBhhbaXHFkdGxubfDmyxbXl5mp/4pg0aRLWrVunRlPlPh0cHHDfffchM9NQgLlgP+rXr49NmzYVuqxWrVplfqxERETmbNvJq5i0LCLfQNmQMG/MGBwENwfD529NxSC2ApT2J33Jde30/gY1iauorFcJAeu52WPb1B5VmtcieafHjx8vFNzeLhndlbJeQ4cO1QepZ86cKbEfFy9ehLW1taofS0REVBOlZ+Vg9t/H8P0Ow2emq7013h0ajHtDvY3aN1PCILYKSWAqZbSkCoGEqHkDWV3IKpdXdWL2m2++qaoO+Pj4qJFSS0tL9dP+oUOHMHPmzHLfbtOmTbFixQo1mUtGaSV/VjdKW5RevXqpPNshQ4bggw8+QLNmzRAXF6fSESQQbtu2bbn7QkREZA4izyVi/JL/EHUlVd/WuWldfHhfqBroIgNWJ6hiUgf2i4fbFHoiynlpr+w6sUWRiVky4euff/5RZbDatWuHefPmoXHjxrd1u3PnzkXt2rVV6S0JZOV+ZLS1OBLo/vXXX+jSpYuaICZB7AMPPICzZ8/qc3CJiIiqo+ycXHy64SSGfr5dH8DaWVti+r2B+GH0XQxgi2ChkbpIlI9MTHJzc0NiYqKaoJSXTFyKjo6Gn59fvnzRspLUgj3R8bicnA5PF3vc5VenxpXGqAgVdTyIiIiM5ey1VExYEo4DMQn6tqAGrpg/MgwBni416sAklRCDFcR0AiORgLV9k/yz8ImIiKjmkHHExXtj8c4fR5CWmaPaZDxrbPcAvNijKWyt+YN5SRjEEhERERmhPOcrvx7E+mOX9W2N3R3VsrF3NK7N41EKDGKJiIiIqtA/hy9i2opIXEs1lJx88C4fvD6gJZzsGJqVFvcUERERURVIycjGO78fwZJ9sfq2us62eH94CHq25ATmsmIQS0RERFTJ9p2Jx4Sl4YiNv6Fv6xPohVnDguHuXPbFi4hBLBEREVGlyczOxfx/T+DLzVHIvVkPysnWCm8NaoX772ioX+2Syo4jsURERESV4MSlZIxfHI4jF5L0bXf61laTtxrVceQ+v00MYomIiIgqUG6uBot2nMH7a46pkVhhY2WBib2b45ku/qwLX0EYxBIRERFVkLiEG5i0LAI7oq7p25p5OWPeyDC08nbjfq5ArKJL5XLmzBmVxxMeHs49SERENZ4sXLAq/Dz6zt+SL4B9qpMfVr/QiQFsJWAQayy5OUD0ViByufavnK9kjz/+uAo8n3322UKXjR07Vl0m2xAREVHpJaRl4sX//Ydxi8ORnJ6t2uq72eP/nrobrw8MhL2NFXdnJWA6gTEcWQ2smQokxRnaXL2Bfu8DgYMq9a4bNWqExYsXY968eXBwcFBt6enp+L//+z/4+PhU6n0TERFVN1tPXlHpA5eSMvRtQ1s3wPRBreDmYGPUvlV3HIk1RgC79NH8AaxIuqBtl8srUZs2bVQgu2LFCn2b/F8C2NatW+vb1qxZg06dOqFWrVpwd3fHwIEDERUVVeJtHzp0CP3794ezszO8vLzwyCOP4OrVq5X6eIiIiIzhRmYOpq8+jEe+3aMPYCVo/fSh1ir/lQFs5WMQW5UkZUBGYHGzUFw+N9vWvFLpqQVPPPEEFi1apD//3XffYfTo0fm2SU1NxcSJE7Fv3z6sX78elpaWGDp0KHJztbMsC0pISECPHj1UICzXkSD40qVLGDFiRKU+FiIioqoWeS4RAz/Ziu93nNG3dW5aF2vHd8HAEG8ekCrCdIKKsLArkHL51ttlZwA3DMnehWmApPPAh00B61Ks3uHsCYzZjLJ6+OGHMW3aNJw9e1ad3759u0ox2LRpk36b4cOH57uOBLoeHh44cuQIgoKCCt3mp59+qgLY9957L991ZNT3xIkTaNasWZn7SUREZEqyc3LxxaYofLz+JLJvrlxgZ22J1wa0xCPtGnPhgirGILYiSACbXCA94HaUGOjePglGBwwYgO+//17NppT/161bN982J0+exJtvvondu3erlADdCGxMTEyRQWxERAQ2btyoUgkKkjQEBrFERGTOzlxNxcSl4TgQk6BvC2nophYuCPAs/NlHlY9BbEWQEdHSuOVI7E0O7qUfib2NlIIXXnhB/f+zzz4rdPm9996Lxo0b4+uvv4a3t7cKYiV4zczMLPL2UlJS1HXef//9QpfVr1+/3P0kIiIyJhnsWbw3Fu/8cQRpmdp0P0sL4IXuAXixZ1PYWDEz01gYxFaE0v6kL7mu84O0k7iKzIu10FYpGB8JWFZuOY5+/fqpgFTKavXt2zffZdeuXcPx48dVANu5c2fVtm3btltOGPv111/h6+sLa2s+rYiIyPxdSc7AK78exPpjhpRBX3dHzB0ZhjY+tY3aN+LErqolgamU0VIsClx483y/2ZUewAorKyscPXpU5bjK//OqXbu2qkjw1Vdf4dSpU9iwYYOa5FUSqTMbHx+PBx98EHv37lUpBGvXrlUTxnJyKr8GLhERUUVae/iiWrggbwD70N0++POlzgxgTQTHwKua1IEd8SPgWuAndhmBlfZKrhOb7y5dXdWpIKlEIBO99u/fr1IIJkyYgA8//LDE25KUA5kgJgFrnz59EBwcjPHjx6sSXXJ7RERE5iA5PQuTl0VgzE/7EZ+qTaGr62yH7x5vi/eGBsPJjr82mgoLjSR7UD5JSUlwc3NDYmJioSBPFgaIjo6Gn58f7O3ty7/nJLXg7A4g5RLg7AU07lAlI7DVTYUdDyIiqvH2RMeryVvnrt/Q74s+gV6YNSwY7s6lmKtClRqDFcSvE8YiAaufNt+UiIiIjCczOxfz/j2BLzdHQTe052xnjbfuDcR9dzRk6SwTxSCWiIiIaqzjF5Mxfkk4jl5I0rfd6Vtblc5qVMfRqH2jkjGIJSIiohonN1eD77ZH44O1x9VIrLCxssDLfZrj6c7+sJI6WmTSGMQSERFRjXI+4QYmLY3AztOG2u3NvVwwb2QYAr1LzsMk08EgloiIiGoEmcu+KjwOb6w6hOT0bNVmYQE81clPjcDa23CCtTlhEFtOumVYybh4HIiIqDQS0jLx2spD+POgLDik5e1mjzkjQtGhSf6l18k8MIgtI1tbW1X3NC4uDh4eHuq8rHpFVf9tWlYcu3LlijoechyIiIiKsuXEFUxeHoFLSRn6tmGtG+CtQa3g5mDDnWamGMSWkQRMUpP0woULKpAl43J0dISPjw8XVCAiokJuZOZg9t9H8cPOs/q2Wo42eHdIMAaEFFh0iMwOg9hykFE/CZyys7O5pKoRyXK51tbWHAknIqJCDp5LUKWzTl9J1bd1bloXc+4PhZcrF8epDhjElpOkENjY2KgTERERmYbsnFx8vikKC9afRHauduUCextLvHpPSzzSrjEHPqoRBrFERERULURfTcWEJeEIj03Qt4U0dFMLFwR4Ohu1b1TxGMQSERGR2U/2/b89MZj5x1HcyMpRbbJYwdjuAXixRwBsrCyN3UWqBAxiiYiIyGxdTk7H1OUHsfH4FX2br7sj5o4MQxuf2kbtG1UuBrFERERkltYcuohpKw7ielqWvm3U3T54bUBLONoyxKnueISJiIjIrCSnZ+Ht349g2f5z+ra6znb48L4QdG/hadS+UdVhEEtERERmY090PCYuDce56zf0bX1beWHWsBDUceLCNzUJg1giIiIyeRnZOZi37iQWbomCRls5C8521pg+qBWGt2nA0lk1EINYIiIiMmnHLyarhQuOXkjSt93lWwcfjQhFozqORu0bGQ+DWCIiIjJJubkafLc9Gh+sOY7MnFzVZmNlgUl9muOpzv6qjBbVXAxiiYiIyOScT7iBSUsjsPP0NX1bcy8XzBsZhkBvV6P2jUwDg1giIiIyqYULVoafx5srDyM5I1u1WVgAT3f2x8TezWBvY2XsLpKJYBBLREREJuF6aiZeX3kIf0Ze0Lc1qOWAOfeHon0Td6P2jUwPg1giIiIyus0nrmDysghcTs7Qtw1r3QDTB7eCq72NUftGpolBLBERERnNjcwczPr7KH7ceVbfVsvRBu8OCcaAkPo8MlQsS5iI2bNnqxpv48ePLzI/pn///urylStXlng7su2bb76J+vXrw8HBAb169cLJkycrsedERERUHhGxCRiwYGu+ALZLMw+sHd+FASyZRxC7d+9eLFy4ECEhIUVePn/+/FIXMf7ggw+wYMECfPnll9i9ezecnJzQt29fpKenV3CviYiIqDyyc3Lx8b8nMeyLHTh9NVW12dtY4p3BrfDD6Dvh5WrPHUumH8SmpKRg1KhR+Prrr1G7du1Cl4eHh+Ojjz7Cd999d8vbklFYCXhff/11DB48WAXFP/74I+Li4m45gktERESVL/pqKu77cifm/XsCObnapbdCG7rhz5c645H2vlx5i8wniB07diwGDBigfvYvKC0tDQ899BA+++wz1KtX75a3FR0djYsXL+a7LTc3N9x9993YuXNnsdfLyMhAUlJSvhMRERFVHBlo+mX3Wdzz8VaExyaoNlmsYFzPplj+XAc08XDm7ibzmdi1ePFiHDhwQKUTFGXChAno0KGDGlUtDQlghZeXV752Oa+7rCizZs3CjBkzytR3IiIiKp3LyemYuvwgNh6/om/zq+uEuSNC0dqn8K+wRCYdxMbGxmLcuHFYt24d7O0L576sXr0aGzZswH///VfpfZk2bRomTpyoPy8jsY0aNar0+yUiIqru1hy6gGkrInE9LUvf9nA7H7x6T0s42rJIEpWf0Z49+/fvx+XLl9GmTRt9W05ODrZs2YJPP/0Uzz33HKKiolCrVq181xs+fDg6d+6MTZs2FbpNXcrBpUuXVHUCHTkfFhZWbF/s7OzUiYiIiCpGUnoWZqw+gl8PnNO3ebjY4YP7QtC9uSd3M5lvENuzZ09ERkbmaxs9ejRatGiBqVOnom7duhgzZky+y4ODgzFv3jzce++9Rd6mn5+fCmTXr1+vD1plVFWqFEhQTERERJVv9+lrmLg0AucTbujb+rWqh/eGBaOOky0PAZl3EOvi4oKgoKB8bVIOy93dXd9e1GQuHx8fFazqSNArOa1Dhw7V15mdOXMmmjZtqrZ744034O3tjSFDhlTBoyIiIqq5MrJzMPefE/hq62lotIUH4GxnjRmDWmFYmwasPEAVyuyTUY4fP47ExET9+SlTpiA1NRXPPPMMEhIS0KlTJ6xZs6bIvFsiIiKqGMcuJmH84nAcu5isb7vLrw4+uj8Ujeo4cjdThbPQSM0LykdSEKQ0lwTHrq6u3DtERETFyM3V4Ntt0fhw7XFk5uSqNlsrS0zq2wxPdvJXZbSIKiMGM/uRWCIiIjKOc9fTMGlZBHadjte3tajngnkjw9CyPgeBqHIxiCUiIqIykR9xf/vvPN5adRjJGdmqTVaHf6azPyb2aQY7ayvuUap0DGKJiIio1K6nZuK1lZH4K9KwiFCDWg74aEQo2vm7c09SlWEQS0RERKWy6fhlTFl+EJeTM/Rtw9s0xFuDAuFqb8O9SFWKQSwRERGV6EZmDt776yh+2nVW31bb0QbvDQ1G/2DD4kJEVYlBLBERERUrPDYBE5eE4/TVVH1bt+Ye+GB4CDxdWb6SjIdBLBERERWSlZOLzzaewicbTiEnV1uN097GEq8NCMTDd/tw4QIyOgaxRERElM/pKymYsDQCEbEJ+rbQhm6qdJa/hzP3FpkEBrFERESkL5318+4YvPvnEaRnaRcukMUKXuwRgLHdA2BjZck9RSaDQSwRERHhclI6pvx6EJuOX9HvDb+6Tmr0NaxRLe4hMjkMYomIiGq4vyMv4NXfInE9LUvf9ki7xph2Tws42jJUINPEZyYREVENlZSehemrD2PFgfP6Ng8XO3xwXwi6N/c0at+IboVBLBERUQ206/Q1vLw0AucTbujb+gfVw7tDg1HHydaofSMqDQaxRERENUhGdg7m/nMCX209DY22chZc7KwxY3ArDG3dgKWzyGwwiCUiIqohjl5IwoQl4Th2MVnfdrdfHXw0IhQNazsatW9EVRLEZmdnY9OmTYiKisJDDz0EFxcXxMXFwdXVFc7OrB9HRERkSmSxgm+3ncactSeQmaMtnWVrZYnJfZvjyU5+sLS0MHYXiSo/iD179iz69euHmJgYZGRkoHfv3iqIff/999X5L7/8suy9ICIiokpx7nqayn3dHR2vb2tRzwXzHwhDi3qu3OtktspctXjcuHFo27Ytrl+/DgcHB3370KFDsX79+oruHxEREZVz4YJf959D//lb9QGshQUwpqs/Vr3QkQEs1byR2K1bt2LHjh2wtc0/c9HX1xfnzxtKdBAREZFxxKdm4rXfIvH3oYv6tga1HDB3RCju9nfnYaGaGcTm5uYiJyenUPu5c+dUWgEREREZz8bjlzFl+UFcSc7Qtw1v0xDTBwXCxd6Gh4ZqbjpBnz59MH/+fP15CwsLpKSk4K233sI999xT0f0jIiKiUkjLzMbrKyMxetFefQBb29EGX4xqo6oPMICl6sZCI0kzZRAbG6smdsnVTp48qfJj5W/dunWxZcsWeHqa/wofSUlJcHNzQ2Jioqq4QEREZMr+i7mOiUsjEH01Vd/WrbkHPhgeAk9Xe6P2jaiyYrAyB7G6EltLlixBRESEGoVt06YNRo0alW+ilzljEEtEROYgKycXn244hU83nlJltISDjRVeG9ASo+724cIFZHYqLYjNyspCixYt8Mcff6Bly5aorhjEEhGRqYu6koKJS8IRcS5R3xbaqBbmjQiFvwdrtlP1j8HKNLHLxsYG6enpt9s/IiIiKicZe/p511m8+9dRpGdpFy6wsrTASz2aYmz3JrC2KvN0FyKzVOZn+tixY9XCBpJSQERERFXnclI6Hl+0F2+sOqwPYP3rOmHFcx0wrldTBrBUo5S5xNbevXvVogb//PMPgoOD4eTklO/yFStWVGT/iIiICMDfkRcw7bdIJKRl6ffHo+0bY1r/lnCwteI+ohqnzEFsrVq1MHz48MrpDREREeWTlJ6F6asPY8UBw4JCni52+OC+EHRrbv4VgYiqLIhdtGhRue+MiIiISm9n1DVMWhaB8wk39G33BNfDu0OCUdsp/8qZRDVNmYNYnStXruD48ePq/82bN4eHh0dF9ouIiKjGSs/KwUf/HMc326KhqyHkYmeNt4e0wpCwBiydRVSeIDY1NRUvvvgifvzxR7UErbCyssKjjz6KTz75BI6OjtyxRERE5XT0QhImLAnHsYvJ+rZ2/nXw0YgwNKhVPeqxExmlOsHEiROxefNm/P7770hISFCnVatWqbaXX365QjpFRERU08hiBQs3R2Hwp9v1AaytlSVeH9AS//dUOwawRLe7YpcsL7t8+XJ069YtX/vGjRsxYsQIlWZg7rjYARERVaXY+DS8vCwCe6Lj9W0t6rlg/gNhaFGPy59TzZFUWYsdiLS0NHh5eRVq9/T0VJcRERFR6cg40q8HzqvqAykZ2vrrFhbAM138MbF3M9hZs3QWUYWlE7Rv3x5vvfVWvpW7bty4gRkzZqjLiIiI6NbiUzPx3M8HVPUBXQArOa+Ln26nar8ygCVCxY7Efvzxx+jbty8aNmyI0NBQ1RYREQF7e3usXbu2rDdHRERU42w8dhmTlx/E1ZQMfdt9dzTEW/cGwsXexqh9I8onNwc4uwNIuQQ4ewGNOwCWVuaZEyskbeCXX37BsWPH1PmWLVti1KhRcHCoHrMmmRNLRESVIS0zG+/+eRS/7I7Rt9VxssV7Q4PRL6gedzqZliOrgTVTgaQ4Q5urN9DvfSBwkNFjsHIFsdUdg1giIqpo/8Vcx8SlEYi+mqpv69HCE7OHB8PTxZ47nEwvgF36qGRuF7jAQvtnxI+VEsiWJQYrc07srFmz8N133xVql7b333+/rDdHRERUrWXl5GLuuhO478ud+gDWwcYK7w4NwrePtWUAS6aZQrBmahEBLAxta17RbmdEZQ5iFy5ciBYtWhRqb9WqFb788suK6hcREZHZi7qSguFf7MCC9SdVHVgR1qgW/hrXGaPubsyVt8g0nd6UP4WgEA2QdF6bK2tOE7suXryI+vXrF2qXZWcvXLhQUf0iIiIyW5Kp99Ous3jvr6NIz7q5uqWlBcb1bIrnuzWBtVWZx5CIKo9GA1w9AZxaD0Rt0AaxpSGTvcwpiG3UqBG2b98OPz+/fO3S5u3tXZF9IyIiMjuXktJV5YEtJwyL//h7OGHeiDCENqpl1L4R6aXFa4PVKAlcN2pHVstKqhWYUxD79NNPY/z48cjKykKPHj1U2/r16zFlyhQuO0tERDXanwcv4LWVkUhIy9K3Pda+MV7p3xIOtqZRlohqqJws4Ny+m0HrBuD8gWJyXiU4rQdkJAFZxS1iZaGtUiDltswpiJ08eTKuXbuG559/HpmZmapNasROnToV06ZNq4w+EhERmbTEG1lq1a3f/jOMZnm62OHD+0PRtZmHUftGNVh8tGGkNXqLNjAtirU90Lgj0KQHENAT8GgBHP39ZnUCFAh2b1Yn6Dfb6PViy11iKyUlBUePHlW1YZs2bQo7OztUFyyxRUREpbUz6hpeXhqOuETDSpYDgutj5pAg1Hay5Y6kqpOeBJzZqh1plfzW69HFb+vZCmjSXRu0+rQHbBxKWSe2gTaArQ51Ys+ePYvU1FRVscDSsnokqjOIJSKiW0nPysFH/xzHN9ui1bwY4WJvjXcGB2FwmDcrD1Dly80BLoTfDFo3AOf2ALnaJYwLcXTXjrTKyb874FrfJFfsKksMVup0AqkDm5CQgIkTJ+rbnnnmGXz77bfq/82bN1fLzsrELyIioursSFwSJiwJx/FLyfq29v7umDMiFA1qVY/VK8lEJZ4HTm/UjrTKxKwb8UVvZ2kD+LTTjrY26QnUCwHKM9goAatfZ5iiUgexX331FcaMGaM/v2bNGixatAg//vijWnb2hRdewIwZM/DNN99UVl+JiIiMSmq9fr31tBqBzcrRDr/aWltiSt/meKKjHywtb+YLElWUzDQgZod2pFVGXK8cLX5b94Cbo609Ad+OgJ1LtT4OpQ5iT548ibZt2+rPr1q1CoMHD8aoUaPU+ffeew+jR4+unF4SEREZWWx8Gl5eGoE9ZwwjXy3ru2L+yDA0r1e9gwWqQpKbcumwNmCVSVlndwI5GUVva+cG+HfRBq0SvNZuXKMOVamD2Bs3buTLTdixYweefPJJ/Xl/f3+1EAIREVF1IlNHlu8/hxm/H0FKhjbf0MICeLZrE4zv1RR21iydRbcp5Uqemq0bil9EwMISaHCHIWhtcAdgVeZCU9VGqR9548aNsX//fvX36tWrOHz4MDp27Ki/XAJYScQlIiKqLq6lZODV3yKx9rAhqGhY2wFzR4ThLr86Ru0bmbHsTCB2tyFovRBR/LZujfJMyOoKONSuyp5WjyD2sccew9ixY1XwumHDBlWN4I477sg3MhsUFFRZ/SQiIqpSG45dwpTlkbiaYvgpd0TbhnhjYCBc7G14NKhsKQLXogxBa/RWICu16G1tHAHfzoaarZLnKkP/VP4gVlbkSktLw4oVK1CvXj0sW7as0LKzDz74YGlvjoiIyCSlZmTj3b+O4v92x+jb6jjZ4r2hwegXVM+ofSMzciMBiN5sKH+VaHg+FSKVAyRglcC10d2AdfWpvV+ZbrtObHXEOrFERDXTgZjrmLgkHGeuGZbb7NHCE7OHB8PTxd6ofSMTl5MNxB0wLDRwfh+gyS16W6m3mrdmqzNXdavUOrFERETVVVZOLj5ZfxKfbjyF3JtDOw42Vip14MG7GnHhAipaQowhaJVR1/TEorezstUuEqArf+XViikCFYBBLBER1WinLqeohQsizxsCkNY+tTBvRBh86zoZtW9kYjJSgLPbtUGrBK/XTha/rUcLQ9AqAaytY1X2tEZgEEtERDWSZNP9uPMs3vvrKDKytT/7WltaYFzPpniuWxNYW1WPpdTpNuTmApciDUFrzC4gN6vobaVqgH+3m+WvugNuDbnrKxmDWCIiqnEuJaVj0rIIbD15Vd/m7+GkFi4IaVjLqH0jI0u+CERtvFlJYCOQZniO5GNhBTS6y1Cz1TtMu0QrmUcQq5sTZsHSD0REZCb+PHhB1X5NvGEYUXu8gy+m9msBB1sGITVOVjoQs9MQtF46VPy2tX0NQatfF8C+5IlHZIJB7Lfffot58+appWhF06ZNMX78eDz11FMV3T8iIqIKIUHr9NWH8dt/5/VtXq52+PC+UHRpxtnhNYYMwF05bqjZemY7kH2j6G1tXbTBasDNSgJ1/Ku6t1SRQeybb76JuXPn4sUXX0T79u1V286dOzFhwgTExMTg7bffLutNEhERVaodp66q9IG4xHR924CQ+nh3SBBqOdpy71d3afHAaUkR2KAdbU0yfJHJzwLwbm2o2drwTsCKC1tUmzqxHh4eWLBgQaGFDf73v/+pwFaWpDV3rBNLRFQ9pGfl4MO1x/Httmh9m4u9NWYOCcKgUG+mw1VXOVnAub2G8ldx/8kQbNHbungbRlr9ugFO7lXdW6qqOrFZWVlo27ZtoXZZgjY7O7usN0dERFQpDsclqtJZJy6l6Ns6NHHHnPtD4V3LgXu9uok/bVgdK3oLkJlc9HbWDoBvR0P5K4/mrNlqpsocxD7yyCP44osvVEpBXl999RVGjRpVkX0jIiIqs5xcDb7achpz1x1HVo529M3W2lJN3BrdwReWllyHvlpITwLObDWUv7puGG0vxCtIW/ZKglaf9oANV1+r0RO7/vnnH7Rr106d3717t8qHffTRRzFx4kT9dgUDXSIiosoUG5+GiUvDsffMdX1bYH1XzH8gDM28XLjzzVluDhAXfjOvdT0QuwfQ5BS9rWNdQ9Aqf13qVXVvyRSD2EOHDqFNmzbq/1FRUepv3bp11Uku02HZLSIiqioyvWPZ/nOYsfowUjO1gY1Uf3y2axNM6NVMjcSSGUo8bwhaT28Cbhi+nORjaQP4tLuZItADqBcCWPKYV3dlDmI3btxYOT0hIiIqh2spGZi2IhL/HLmkb2tY2wHzRobhTt863KfmJDMNOLvDELheOVb8tu4Bhpqtvp0AO+eq7CmZAK7YRUREZmv90UuY+utBXE3J1LeNaNsQbwwMhIs9SyOZPCmQdOmwoWbr2Z1ATkbR29q5Af5dDaOttRtXdW/JxJQ6iG3dunWRKQJSBqFZs2YYN24cAgMDy92R2bNnY9q0aep25s+fr9rGjBmDf//9F3FxcXB2dkaHDh3w/vvvo0WLFsXezuOPP44ffvghX1vfvn2xZs2acveNiIhMS2pGNmb+eRT/2xOjb6vjZItZw4LRtxXzH01aypU8NVs3ACmGEfR8LCyBBm0NNVu92wBWHHsjg1I/G4YMGVJke0JCAg4cOKCC3A0bNqBjx44oq71792LhwoUICQkpVLZLKh74+PggPj4e06dPR58+fRAdHQ0rq+KXBuzXrx8WLVqkP29nZ1fmPhERkWnaf/a6mrx19lqavq1nC0/MHh4CDxe+35uc7EwgdpehZuvFg8Vv6+aTp2ZrV8ChVlX2lKr7YgfFee2117Br1y6sX7++TNdLSUlRE8U+//xzzJw5E2FhYfqR2IIOHjyI0NBQnDp1Ck2aNCl2JFYC65UrV6K8uNgBEZHpycrJxYL1J/HZxlPIvfnJ5WhrpVIHHrizEScUmwoJK66dMgStZ7YBWalFb2vjBPh1NtRsdW/Cmq01XFJlLnZQnIceeghff/11ma83duxYDBgwAL169VJBbHFSU1PV6Kqfnx8aNWpU4m1u2rQJnp6eqF27Nnr06KFu192dK3AQEZmrU5eTMWFJBCLPJ+rb2vjUwtwRYfCt62TUvhG0VQNkgQFVs3UjkGhI8yikfqghaG10F2DN0XMqnwoLYuXn/dzc3DJdZ/HixSoVQdIJiiMjtFOmTFFBbPPmzbFu3TrY2tqWmEowbNgwFexKCbBXX30V/fv3x86dO4tNQcjIyFCnvN8CiIjI+HJzNfhx5xnM+vsYMrK1nzHWlhYY36upKp9lbcUySkaRkw3EHTAsNHB+H6ApJgZw9jIErf7dAGePqu4tVVMVFsSuWLGiTBO7YmNj1SQuCUrt7YtfOUNyYnv37o0LFy5gzpw5GDFiBLZv317sdR544AH9/4ODg1WeraQeyOhsz549i7zOrFmzMGPGjFL3nYiIKt/FxHRMXh6BrSev6tuaeDhh/sjWCG7oxkNQ1a6fzVOzdQuQYRgVz8fKDmjc3lD+yqsVUwTIuDmxCxYsKLJdchb279+PP//8E3///bdKCygNyVkdOnRovtHRnJwcldNkaWmpRkYLjpxmZmaqFIFvvvkGDz74IErLw8NDpRRItYPSjsRKykJp8jGIiKji/R4Rh9dXHkLijSx92+MdfPFK/xawtyl+Yi9VoIwUbT6rrvyV5LkWx6OFIWht3AGwdeShINPJiZ03b16R7XIH8jP/li1b0L59+1J3UkZFIyMj87WNHj1alc+aOnVqkT/9S7wtp7wB562cO3cO165dQ/369YvdRqoXsIIBEZHxJaZl4c3Vh7AqPE7f5uVqhzn3h6JzU/4MXakkJVAqB6igdSMQswvINXyJyMehNuDfXVv+Sv66NajcvhHdThArZa0qkouLC4KCgvK1OTk5qQlY0n769GksWbJEldSSkVQJRqWWrIODA+655x79dSTolXQAGdWVSgeSFjB8+HDUq1dP5cRKPm1AQICqFUtERKZr+6mrmLQsAhcS0/VtA0PqY+aQINRyLH4uBN2G5IuGeq0SuKYZUjfysbQGGt6lHWmVElj1wwBLjoiTGefESm5q27ZtK2UUU3Jet27dqsptXb9+HV5eXujSpQt27NihKg/oHD9+XA05Cxm9lTJcstiBlNny9vZWQfA777zDkVYiIhOVnpWDD9cex7fbDIMlLvbWKngdHMYRvgqVlQ7E3FzW9dQG4PLh4ret7WdYaMC3M2DP9DqqRnViJZUgPDwc/v7+qE5YJ5aIqGocOp+ICUvCcfJyir6tQxN3lT7gXcuBh+F2yUf8lWOGmq1ntwPZhpHufGxdbi7r2l0buNapXp/tZB6qrE5sBa2TQERENUxOrgYLt0Rh3roTyMrRfpbYWltiar8WGN3BF5aWhZc5p1JKi9cu6yojrRK8Jhvyi/OzABq0MZS/atgWsLLhbiazwUWIiYioSsVcS1PLxu47e13fFljfFfMfCEMzLxcejbLKyQJi9xjKX8WFyzBT0du6Nrg50nqzZqtjHe5vqplB7MKFC1WuKhERUWl+vVu27xxm/H4YqZk5qk0GXGXRgvG9mqmRWCqla1GGCVnRW4HM5KK3s3YAfDsayl95NGfNVqq5QeyGDRvQoUMHNfFKlpolIiK6laspGZi2IhLrjlzSt/nUccTcEaFo68vRwFtKT9QGq7qardfPFL+tV9DNFIEegE97wKb4BYWIalQQO2jQIGRnZ+POO+9Et27d0LVrV3Ts2FGVviIiIiro3yOX8MqKg7iakqlve+DORnh9YCCc7ZjVVqTcHG1agC5olXQBjXb0uhDHuoagVVIFXOrxSUg1QpnfPaTc1Z49e7B582Z1khJYspKWlNrq3r27WhmLiIgoNSMbM/88gv/tidXvDHcnW8weHoLegUxFKyTxnCFF4PQm4IYhZzgfSxvAp52h/JVXMGDJVAyqeW6rxJY4fPgwPvzwQ/zyyy/Izc1VS8eaO5bYIiK6PfvPxmPCkgjExKfp23q19MSsYSHwcKn42uJmKTNNW/JKV/7q6vHit3VvaghaG3cE7JyrsqdE1aPE1okTJ7Bp0yZ1kpFYWQK2c+fOmDNnjkovICKimiszOxcL1p/E55tOIffmEImjrRXeHBiIkXc2goVFDS6dJWNGlw4ZgtaYnUCOIcUiH3s3bfUAXZpALZ+q7i2RyStzECvLvMoysOPGjcMrr7yC4ODgmv2mREREyqnLyRi/JByHzifp90gbn1qYNzIMjd2dauZeSrlys2brzdzW1MtFb2dhCTS801Cz1bs1YMV8YaKSlPkV8tJLL2HLli14++238ccff6jRVzl16tQJjo6OZb05IiIyc7m5Gvyw8wxm/30MGdm5qs3a0gITejfDmC7+sLaqQfma2RlA7G5D0HrxYPHbuvkAATeDVr8ugEOtquwpUc3NiU1ISMDWrVv1E7wkN7Z169bYvn07zB1zYomISudC4g1MXnYQ205d1bcFeDpj/sgwBDVwq/67UT5Cr540TMg6sxXIMuQB52PjBPh1NtRsdW/Cmq1Exlh2ViZwZWVlqZzY9PR09ff48RKS0omIqFr5PSIOr/0WiaT0bH3b6I6+aulYexsrVFtSNeD05pvlrzYCiYbqC4XUDzUErY3uBqxtq7KnRNVaudIJZFLXkSNHULt2bXTp0gVPP/20SimQ/FgiIqreEtOy8ObqQ1gVHqdvq+dqjzn3h6JT07qodnKygfP7DTVb5f8abdpEIc71tAGrVBKQiVlO1XB/EJlrEHvhwgU888wzKmgNCgqqnF4REZFJ2n7qKiYti8CFxHR926BQb7wzOAhujjaoNq6fNQStp7cAGYlFb2dlBzTuYCh/5RnIFAEiUw1ily1bVjk9ISIik5WelYP31xzDou2G5U5d7a3xzpAgDA5rALOXkaLNZ9WVv4qPKn5bj5Y3g9bu2pqtNlyxksgYypUTGxUVpVbqOnr0qDofGBioSm41adKkovtHRERGduh8oiqddepyir6tY4C7Sh+o72amAVxuLnAx4mbQKsu67gZys4re1qGONmDV1Wx19a7q3hJRRQSxa9euxaBBgxAWFoaOHTuqNqlI0KpVK/z+++/o3bt3WW+SiIhMUE6uBl9ujsK8dSeQfXPlAjtrS7zSvwUea+8LS0szqxGedMFQs1X+pl0rejtLa+0kLBW49tROzrKsxhPViGpKiS0po9W3b1/Mnj07X7ssfPDPP//gwIEDMHcssUVENV3MtTRMWBqO/Wev69taebuq0llNvVxgFrJuAGd33Cx/tRG4fLj4bev4GxYa8O0E2Jdc2oeIjB+DlTmItbe3R2RkJJo2bVpoOdqQkBBVbsvcMYgloppKPhKW7ovF278fQWpmjmqTAdfnuwXgpZ5NYWttwgsXyMfZ5aM3g9b12gA2u5jPJDtX7QIDuhSBOn5V3Vsiquo6sbLkbHh4eKEgVto8PT3LenNERGQirqZk4JVfI/Hv0Uv6Np86jpg7IhRtfevAJKVe06YG6BYbSL5QzIYWQIM2hpqtDdsCVtWomgJRDVTmIFZqwkqJrdOnT6NDhw76nNj3338fEydOrIw+EhFRJVt35BJe+fUgrqVm6tseuLMRXh8YCGe7cq+LU/GyM4Fzew3lr+LCZQi26G1dGxhGWqVmq6OJBuJEVC5lTieQzaUywUcffYS4OG2ha29vb0yePFkthGBhYWaJ/kVgOgER1RQpGdmY+ccRLN5rWHXK3ckWs4eHoHegF4xOPqLiTxtGWqO3AJmGKgn5WDto81l1NVvrNmPNViIzU6k5sXklJyervy4uZpLkX0oMYomoJth/Nh4TlkQgJj5N39arpRdmDw9GXWc743UsPVEbrOpqtiacLX5br2Ag4OZoa6N2gI19VfaUiMwpJzav6ha8EhHVBJnZufh4/Ql8sSkKNytnwdHWCm/dG4gRbRtV/S9quTlA3H+GoFXSBTTaSWWFOHnkSRHoDriYwGgxERmFdWnLapX2Ta06lNgiIqquTl5KVgsXHI5L0rfd0bi2mrzV2N2p6jqSeE4bsKplXTcB6QlFb2dlC/i0M5S/8goCLE24QgIRmVYQO2TIkMrvCRERVZrcXA2+33EGs9ccUyOxwtrSAhN6N8OzXZvAqrIXLshMBc5sN5S/unqi+G0ll1Vfs7UjYFuFwTURmY3byomtrpgTS0TVyYXEG5i0LALbTxlWqGrq6Yx5I8MQ1MCtcu5UPlouRhomZMXsBHIMlQ/ysXfTVg9Q5a+6A7V8KqdPRGTyqiwnloiITNuq8PN4Y+UhJKVn69ue6OiHKf2aw96mgpdSTbmsXRlLlb/aCKReLno7CyttnVZdzVap38plXYmojEoVxNauXbvUObHx8fFl7QMREVWwxLQsvL7qEH6P0JZCFPXd7DHn/lB0DKhbMXeSnQHE7DLUbJWR1+LI6KoErVL+yrcz4FCrYvpARDVWqYJYqQtLRETmYdvJqyp94GKSYcnVwWHeeHtQENwcbW4vReDqSUPQemYbkGUoz5WPrbM2WNXVbK3jz5qtRFT1Qexjjz1WsfdKREQVLj0rB7P/PqYmcOm42ltj5tBgDAr1Lt+N3riurR6gyl9tAJLOFb9t/TBD0NrwLsDatnz3SURUCuXKic3JycHKlStx9OhRdb5Vq1YYNGgQrKwqOL+KiIhK5dD5RFU669Rlw2pWnQLq4sP7Q1DfzaH0ezEnGzi/z1CzNe4AoNFWMyjEuZ4haJWJWU4VlKZARFQZQeypU6dwzz334Pz582jevLlqmzVrFho1aoQ///wTTZo0KetNEhFROWXn5GLhltOYt+4Esm+uXGBnbYlp/Vvg0fa+sCxN6azrZwxBq6yUlWGoIZuPtT3QuIOh/JVnS6YIEJH5lNiSAFau8ssvv6BOnTqq7dq1a3j44YdhaWmpAllzxxJbRGQOzl5LxcSlEdh/9rq+LaiBK+aNCENTrxJWVMxIBqK3GspfxUcVv61noGGFLAlgbcowqktEZEoltjZv3oxdu3bpA1jh7u6O2bNno2PHjmW9OSIiKiMZSFiyNxZv/3EEaZna5VllwPX5bgF4qWdT2FoXWNEqNxe4EG4IWmN3A7mGklv5ONTR1mrV1Wx1LWcuLRFRJStzEGtnZ4fk5ORC7SkpKbC1ZRI/EVFlupKcgWkrDuLfo4YarD51HDFvZCjuaGwYXEBSXP6arTeKKX9oaQ00kmVdu2vzW+uFcllXIqqeQezAgQPxzDPP4Ntvv8Vdd92l2nbv3o1nn31WTe4iIqLK8c/hi5i2IhLXUg0rXz14VyO8PiAQTpZZ2pxW3Wjr5SPF35CUu9LXbO0E2JWQekBEVF2C2AULFqiSW+3bt4eNjbbeYHZ2tgpgP/7448roIxFRjZaSkY13fj+CJfti9W11nWzwaS8HtNNsBpa+BZzdAWQb6sLmY+cK+HUx5LbW8au6zhMRmUoQW6tWLaxatQonT57EsWPHVFvLli0REBBQGf0jIqrR9p2Jx4Sl4YiNv4HaSEJny0MYWeck2iMClmsvFn0lC0vAu42h/FWDtoAVVxknouql1O9qb775Jl555RU4Ojqq83Xr1sW9995bmX0jIqqxMrNzseCfw9i3bS1GWh5EF9uDCLI4A0sLDWAoBWvg2hAIuDnS6tcVcMyTH0tEVJNLbMlCBhcuXICnp6c6L2UPwsPD4e/vj+qGJbaIyCjk7Tj+NC7/9ydO7/4dQZkH4WxRTIqAjaM2n1VXs7VuU9ZsJSKzVykltgrGumUsL0tEREVJTwROb1aTsTRR62GREAMZKlDDBQXXKagXbAhafdoB1nbcp0RUYzFJioioKuXmAOcPGKoInNsLaLS1XgvGrNct3GDRpAdqBffXLuvq4sVjRURU1iDWwsJC1Ye1t7dXo7ByXmrDyrBvXrca+iUiqnESYm8Greu1o67pCUVulqGxxr7c5tiSGwL30H54dMgA2Ntqq8AQEdFtpBM0a9Ys3/nWrVvnOy+BbU6OdkSBiKjGykwFzmy/udDABuDqiWI3vWDbGH+ntcSW3GDszm2JWm61MOf+UHQMqFulXSYiqrZB7MaNGyu3J0RE5kqWdb10yBC0xuwCcgwLEuRjX0ulBpxwuQuTD7gjIsmw0MCQMG/MGBwENweOvhIRVVgQ27Vr19JuSkRU/SVfAk5vNOS2pl4pejsLK6DhnfqarekeIZi99iS+33xGv4mrvTXeHRqMe0O9q67/RERmjhO7iIhKIzsDiNmpDVhPbQAuRRa/ba3GhoUGZKUsezfVHHkuEeM/3YGoK6n6TTs3rYsP7wtFPTd7HgciojJgEEtEVBQpIyi5rCpoXQ+c2QZk3yh6X9k6F1jW1T9fzdbsnFx8sSkKH68/iexcbXlCO2tLTOvfAo+294WlZcG6BEREdCsMYomIdNLigdObbqYIbASSzhWzbywA7zBDzVZJF7C2LXLLM1dTMXFpOA7EGCoSBDdww7yRYQjwdOa+JyIqJwaxRFRz5WQB5/YZyl9J/VYUs5CLS33DSKt/d8DJvcSblooti/fG4p0/jiAtU1u1RQZcX+gegBd7NoWNlWVlPCIiohqj1EFswWVniYjMUny0YTJW9BYgI3+taz1re6BxR0Pg6tmy1Mu6XknOwCu/HsT6Y5f1bY3dHTF3RBjuaFy7oh4JEVGNVu5lZ4mIzEJGMhC91VD+Kv508dt6BmoDVpmU5dMesHEo892tPXwR01ZEIj7VUGLrwbt88PqAlnCy449fREQVhe+oRFT9arZeCL8ZtG4EYncDudlFb+vork0N0I22utYv992mZGTj7d8PY+k+Qx5tXWdbvD88BD1bcrlYIiKjBrHffPMNnJ1Lnojw0ksv3W6fiIjKJinOkCIggeuN+KK3s7QGGrUDAm4GrfVCAcvbz03deyZeTd6KjTdUL+gT6IVZw4Lh7mx327dPRESFWWhKmSdgaWmJhg0bqtzY4siys6dPl/BTnZlISkqCm5sbEhMT4erqauzuEFFBWTeAs7Ks60Zt+asrR4vfR3WaGGq2+nYC7AwrZN2uzOxczPv3BL7cHKUqcgknWyu8NagV7r+joXpPJCKiyonByjQSu2/fPk7sIqKqJxHi5SPagFVGW8/uAHIyit7Wzg3wz1OztbZvpXTpxKVkjF8cjiMXDBPD7vStrSZvNarjWCn3SUREBsyJJSLTlHpVO9KqSxNIuVj0dhaWQIM7DDVb5f9WlffWlpurwXfbo/HB2uNqJFbYWFlgYu/meKaLP6y4cAERUZVgEEtEpiE7UzsJS1ez9UJE8du6NryZ19pTu1KWY50q6eL5hBuYvCwCO6Ku6duaeTmrhQtaeWuXliUiIhMLYocNG3bLSV1ERGVKEbgWZQhapQxWVmrR29o4avNZJWiVEde6TUtds7UiyNSBVeFxeGPVISSnGyodPNXJD5P6Noe9TfFzBYiIyMhB7IoVKxAbG4unnnoKDzzwAFxcKm5yBBHVEDcStAsM6Gq2JsQUv229EENeq087wNo4s/wT0jLx2spD+PPgBX2bt5s95twfig4BdY3SJyIiKkN1gq1bt2LRokVYvnw5cnNzMXz4cBXQdu7cudrtR1YnIKogOdlA3H+GoFWWeNVol2AtxMnTsNCAfzfA2firA245cQWTl0fgUpJhEtnQ1g0wfVAruDnYGLVvRETVUVlisFIHsTqpqalYunQpvv/+exXYBgQE4Mknn8Rjjz2GevXqoTpgEEt0GxJiDUHr6U1AemLR21nZalfF0pW/8gqq0hSBktzIzMHsv4/ih51n9W0StL47NAgDQ7yN2jciouosqTKD2LxOnTqlRmd/+uknXLx4Ef369cPq1ath7hjEEpVBZipwZps2aJUSWNdOFr9t3eaGoLVxR8DW9EpRHTyXgAlLwhF1xZCf27lpXXx4XyjqudkbtW9ERNVdUlUFsbqR2V9++QXTpk1DQkICcnKK+anQjDCIJbrFsq6XIg1Ba8wuIDer6G3tawFN8izr6tbQZHdtdk4uvtgUhY/Xn0R2rvZt0c7aEq8NaIlH2jXmwgVEROa82EFeW7ZswXfffYdff/1VreY1YsQIlVZARNVQ8iXg9M3VseRv6pWit7OwAhrdZajZ6h0GWJr+zP0zV1MxYWk4/otJ0LeFNHRTCxcEeLIqCxGRKSpTEBsXF6dyYeUkqQQdOnTAggULVADr5ORUeb0koqqVlQ7E7DQsNHDpUPHb1mp8M0VAarZ2BuzNp16q/BD1vz2xeOePI7iRpf0VSdYqeKF7AF7s2RQ2VpbG7iIREd1uENu/f3/8+++/qFu3Lh599FE88cQTaN68eWmvTkSmTLKKrhw31Gw9sx3IvlH0trYu2gUGdGkC7k1gji4np+OVXyOx4dhlfZuvuyPmjgxDG5/aRu0bERFVYBBrY2OjymsNHDgQVlam//MgEd1CWry2eoCqJLARSDpfzIYWgHdrQ/mrhncCVuZdXmrt4YuYtiIS8amZ+raH7vbBa/e0hJMdFzIkIjIHpf6tTKoOhIaGqjzYzz77DIcOlfDzYjnMnj1bTZwYP368vm3MmDFo0qQJHBwc4OHhgcGDB+PYsWO3/HnwzTffRP369dX1evXqhZMnS5gtTVRT5GQBZ3cCG2YCX/cAPvAHlo8G/vu5cADrUh8Iexi47ztgchTwzEag5xtA4w5mHcAmp2epZWPH/LRfH8DWdbbDd4+3xXtDgxnAEhGZkVIPOWzcuBH33nsv0tLStFe0tlYB7cMPP3zbndi7dy8WLlyIkJCQfO133HEHRo0aBR8fH8THx2P69Ono06cPoqOjix0N/uCDD1Se7g8//AA/Pz+88cYb6Nu3L44cOQJ7e5bHoRomPtow0iorZWUkFb2dtb225JWu/JVHC5Op2VpR9kTHY+LScJy7bkiT6BPohVnDguHubJzVwIiIqPxKXWKrU6dOKh/2iy++UMHg66+/jt9++01N9rodKSkpaNOmDT7//HPMnDkTYWFhmD9/fpHbHjx4UI0Gy6QyGaEtSB6Kt7c3Xn75ZUyaNEm1SYkGLy8vNRlNlsstDZbYIrOVngSc2Woof3U9uvhtPVsBAbplXTsANtXzS15Gdg7mrTuJhVuiVOqvcLazxlv3BuK+OxqydBYRUXUvsSXpAzt27FA/04sPP/xQjZ5eu3YN7u7u5e7s2LFjMWDAAPWzvwSxJdWjlYUVZHS1UaNGRW4jI7Sy6ILclo7siLvvvhs7d+4sdRBLZDZyc4AL4cCpm1UEzu0BcrOL3tbR3VCv1b874Kp9LVdnxy8mY/yScBy9YBiBvsu3Dj4aEYpGdUxvoQUiIio967JExjISq+Po6KhyTiVSLm8Qu3jxYhw4cEClExRHRminTJmigliphrBu3TrY2toWua0EsEJGXvOS87rLipKRkaFOeR8rkclKPG8ofSUTs27EF72dpQ3g0+5mFYGeQL0QwLJmlIzKzdXgu+3R+GDNcWTm5Ko2GysLvNynOZ7u7A8rqaNFRERmrUzTcNeuXatGNnVyc3Oxfv36fJO8Bg0aVKrbio2Nxbhx41RQWlKuquTE9u7dGxcuXMCcOXNUTdrt27dXaH7rrFmzMGPGjAq7PaIKlZkGnN1hKH91pYTJje4BhoUGfDsBdjWvUP/5hBuYtDQCO09f07c193LBvJFhCPQu+acpIiKqhjmxsirXLW/MwqLUy86uXLkSQ4cOzTdBS64rtyH3JSOjBSdvZWZmonbt2vjmm2/w4IMPFrrN06dPq1zZ//77T+XW6nTt2lWd//jjj0s9EispC6XJxyCqcPKSvHTYELRKRYEcw/MzHzs3wF9qtt6ckFW7cY09IPJWtjL8PN5ceRjJGdqUCpmb9lQnPzUCa2/D0oBERDUyJ1ZGXStSz549ERkZma9t9OjRaNGiBaZOnVpk9QH5kJJT3oAzL8mXrVevnhod1gWxsjN2796N5557rti+2NnZqROR0aRcyVOzdQOQcqno7SwsgQZtDbmtDe4ArFjXNCEtE6/9dgh/Rl7Q7ypvN3vMGRGKDk0MaVBERFR9lPnTTwLI7Ozs215m1sXFBUFBQfna5DYlv1baZVR1yZIlqqSW1Ig9d+6cqiUrebj33HOP/joS9Eo6gIzq6urMygSxpk2b6ktsScWCIUOG3FZ/iSpUdiYQu9sQtF6IKH5bt0aGhQZkpSwHriaV1+YTV1Tt18vJhi+3w1o3wFuDWsHNwXxr2hIRUQUFsVeuXFHLzcrSszIqe+edd+Lnn39GQEAAKoPkvG7dulWV27p+/bqanNWlSxdVIcHT01O/3fHjx9WQs45uEtgzzzyDhIQEVRpszZo1rBFLxk8RuBZlCFqjtwJZqUVva+MI+HY21GyVPNdqVrO1ItzIzMHsv4/ih51n9W21HG3w7pBgDAip/pUXiIhqulLnxD7xxBP4+++/8dJLL6mAUMprSbktWQShumGdWKoQNxKA6M03a7ZuABJjit9WKgfogtZGdwPWTG8pycFzCap01ukrhi8CnZvWxZz7Q+HlWj3r3RIR1QRJlZETK1UEZMEAWf1KDBw4EC1btlTpBcwnJZKZidlA3AHDQgPn9wGaYnLJnb3y12x19uAuLIXsnFx8tjEKn2w4iexc7fdvextLvHpPSzzSrjEXLiAiqkFKHcTKylyyWpaO5JxK8Cqlr3x9fSurf0SmLSHGELTKqGu6IbUlHys7oHF7Q/krr1ZMESij6KupmLAkHOGxCfq2kIZumDsiDAGeNa+UGBFRTVemiV0FKwbI+VJmIxBVDxkpwJlthvJX104Vv61HC0PQ2rgDYMsVospD3mP+b08MZv5xFDeytCX8ZLGCsd0D8GKPANhY1YwFHIiIqJxBrHyQNGvWLN/PdSkpKWjdunW+GrLx8cWsHkRkjqS03MWDhhWyYnYBuVlFbytVAyQ1QAWu3QG3hlXd22rncnI6pi4/iI3Hr+jb/Oo6Ye6IULT2YZUGIqKarNRB7KJFiyq3J0SmIvkiELXxZiWBjUDa1aK3s7QGGt51s/xVD6B+GGDJgvoVZc2hC5i2IhLX0wxfGkbd7YPXBrSEoy1r4xIR1XSl/iR47LHHKrcnRMaSlQ7E7DQErZcMyygXUttXmx4glQSkDJY9V3SraMnpWZjx+xEs339O31bX2Q4f3heC7i0M5fWIiKhm43AG1TySx33luKFm65ntQPaNore1ddEuMCAjrTLiWse/qntbo+w+fQ0Tl0bgfILhePRt5YVZw0JQx8nWqH0jIiLTwiCWaoa0eOD0xpu5rRuBpPPFbGgBeLc21GxteCdgxVWfKltGdg7mrjuBr7acVt8xhLOdNaYPaoXhbRqwdBYRERXCIJaqp5ws4NxeQ/mruP9kCLbobV28DSOtMjHLsU5V97ZGO3YxCeMXh+PYxWR9212+dfDRiFA0qsOKDkREVDQGsVR9xJ/WBqwy0hq9Bcg0BEX5WDsAvh0N5a88mrNmqxHk5mrw7bZofLj2ODJztItC2FhZYFKf5niqs78qo0VERFQcBrFkvtKTtMGqrmbr9TPFb+sVpC17JUGrT3vAhkuTGpPkvL68NBy7ThtK8jX3csG8kWEI9OZkOSIiqoQgNicnRy0/u379ely+fBm5Ukczjw0bNpT1JolKJzcHiAs3BK2xewCNtvh9IY51DUGr/HWpx71sAqTe9G//ncdbqw4jOSNbtUnp6ac7+2Ni72awt2GJMiIiqqQgdty4cSqIHTBgAIKCgjjhgipX4nlD0Hp6E3DjetHbWdoAPu1upgj0AOqFAHkW4SDju56aiddWRuKvyIv6tga1HDDn/lC0b+Ju1L4REVENCGIXL16MpUuX4p577qmcHlHNlpkGnN1hKH915Vjx27o3vbnQgCzr2hGwc67KnlIZbDp+GVOWH8Tl5Ax927A2DVT1AVd7Vn8gIqIqCGJtbW0REBBQjrsiKoLUU7p02BC0nt0J5BgCnXzs3AD/rtqgVaoI1G7MXWribmTm4L2/juKnXWf1bbUcbfDe0GDcE1zfqH0jIqIaFsS+/PLL+Pjjj/Hpp58ylYDKJ+VKnpqtG4CUS0VvZ2EJNGhrqNnq3Qaw4lxEcxEem4CJS8Jx+mqqvq1rMw+18panKyfWERHR7SlzRLBt2zZs3LgRf//9N1q1agUbm/w/Ba5YsYLHhPLLzgRid90sf7UBuHiw+D3k5mOo2erXFXCoxb1pZrJzcvHZxigs2HASObna2rz2NpZ4bUAgHr7bh19+iYjIOEFsrVq1MHTo0Iq5d6q+KQLXThkWGjizDcgyjMblY+ME+HU21Gx1b8KarWbs9JUUTFgagYjYBH1baEM3zB0ZhiYezFkmIiIjBrGLFi2qwLunakOqBpzebFjWNTGm+G3rhxqC1kZ3AdZ2VdlTqqTSWb/sjsG7fx7FjSxt2TNZrOCF7gF4oUcAbKxYKYKIiCoWEwypfHKygfP7DeWv5P+a/DWD9Zy9btZrlWVduwHOHtzr1cjlpHRM+fUgNh2/om/zq+uEuSNC0dqntlH7RkRE1Ve5gtjly5erMlsxMTHIzMzMd9mBAwcqqm9kaq6fzVOzdQuQkVj0dlZ2QOMOhvJXnoFMEaim1hy6gGkrInE9LUvf9nA7H7x6T0s42vI7MhERVZ4yf8osWLAAr732Gh5//HGsWrUKo0ePRlRUFPbu3YuxY8dWTi/JODJStPmsuvJXkudaHI8W2tFWmZTl0wGwdazKnlIVS0rPwozVR/DrgXP6Ng8XO3xwXwi6N/fk8SAiItMLYj///HN89dVXePDBB9XKXVOmTIG/vz/efPNNxMcb1kEnMyRLCEvlABW0bgRidgG5hhG2fBxqa2u16mq2ujWo6t6Skew6fQ0vL43A+YQb+rZ+rerhvWHBqONky+NCRESmGcRKCkGHDh3U/x0cHJCcnKz+/8gjj6Bdu3aqfiyZkeSLhnqtErimXS16O0troOFdhvJX9cMAS65zX5NkZOdg7j8n8NXW06oAhXC2s8aMQa3U6lsWFhbG7iIREdUgZQ5i69Wrp0ZcGzduDB8fH+zatQuhoaGIjo5WM5TJxGWlAzE7bpa/2gBcPlz8trX9DAsN+HYG7F2rsqdkQo5eSMKEJeE4dlH7pVXc5VcHH90fikZ1mDpCRERmEMT26NEDq1evRuvWrVU+7IQJE9REr3379mHYsGGV00sqP/liceWYYaGBs9uB7PSit7V10S7r2qS7NnCt4889X8PJYgXfbjuNOWtPIDNHW33C1soSk/o2w5Od/FUZLSIiImOw0JRx+DQ3N1edrK218e/ixYuxY8cONG3aFGPGjIGtrfnnxCUlJcHNzQ2JiYlwdTXD0cfUazeXdb25tGtyXDEbWgAN2hjKXzVsC1jlX4GNaq5z19NU7uvuaEOue4t6Lpg3Mgwt65vh64KIiKpVDFbmILYmMLsgNicLiN1jKH8VFy5DsEVv69rg5kirTMjqBjjWqerekomTt4QVB85j+urDSM7IVm2S7vpMZ39M7NMMdtbMhSYiIuPHYOUq5Lh161YsXLhQldaSVIIGDRrgp59+gp+fHzp16lTeflNZXIsyTMiK3gpkGnIV87F2AHw7GkZbPZqzZisVKz41E6/9Fom/D13UtzWo5YCPRoSinb879xwREZmMMgexv/76q6pEMGrUKPz333/IyMhQ7RIxv/fee/jrr78qo5/VV24OcHYHkHJJu7KVLBJQ1Kz/9ERtsKqr2Xr9TPG36RWsHW2VSVmN2gE29pX6EKh62HT8MiYvP4grydrXtBjepiHeGhQIV3ummRARkZkHsTNnzsSXX36JRx99VOXD6nTs2FFdRmVwZDWwZiqQlCdn1dUb6Pc+0GKANi1AF7RKuoBGuyZ9IY51DatjSYqASz0eBiq1tMxszPrrGH7adVbfVtvRBu8NDUb/4Prck0REVD2C2OPHj6NLly6F2iV/ISEhoaL6VTMC2KWPFs5dlYB26SOArROQmVr0dS1tAJ92hvJXMvJqaVkl3abqJTw2AROXhOP0VcNzrVtzD3wwPASerhzBJyKialYn9tSpU/D19c3Xvm3bNrVyF5UyhUBGYIubfCUKBrDuTfPUbO2kDXKJyikrJxefbTyFTzacUmW0hL2NJV4bEIiH7/bhwgVERFT9gtinn34a48aNw3fffac+6OLi4rBz505MmjQJb7zxRuX0srqRHNi8KQTFadwRCBmhDVxr+VRFz6gGOH0lBROWRiAi1vDLSWhDN1U6y9/D2ah9IyIiqrQg9pVXXlF1Ynv27Im0tDSVWmBnZ6eC2BdffLGsN1czySSu0mj7BBB8X2X3hmpQ6ayfd53Fu38dRXqWduECWazgxR4BGNs9ADZWTEkhIqJqHMTK6Otrr72GyZMnq7SClJQUBAYGwtmZIzilJlUIKnI7olu4nJSuKg9sPnFF3+ZX10mNvoY1qsX9R0REZqdcdWKFrMwlwSuVg5TRkioESReKyYu10F4u2xHdpr8jL2Dab5FISMvStz3SrjGm3dMCjrblfgsgIiIyqlJ/gj3xxBOl2k5yZekWpA6slNFS1QksCgSyN9ei7ze76HqxRKWUlJ6lVt2S1bd0PFzs8OF9IejW3JP7kYiIakYQ+/3336Nx48Zo3bq1yq2j2xQ4CBjxYzF1YmdrLycqp12nr+HlpRE4n3BD39Y/qJ6q/VrbyZb7lYiIak4Q+9xzz+F///sfoqOjMXr0aDz88MOoU6dO5fauupNAVRY1KM2KXUSlkJGdg4/+OYGvt56G7rumi501ZgxuhaGtG7B0FhERVRsWmjIMq8oSsytWrFApAzt27MCAAQPw5JNPok+fPtXqwzEpKUkt3iBL6bq6uhq7O0SlcvRCEiYsCcexi8n6trv96uCjEaFoWNuRe5GIiKpVDFamIDavs2fPqhSDH3/8EdnZ2Th8+HC1qVDAIJbMiSxW8M3W02oENjNHWzrL1soSk/s2x5Od/GBpWX2+YBIRUfWWVIYgttxTky0tLdXoq8TAOTk55b0ZIroNsfFpeHlZBPZEx+vbWtRzwfwHwtCiHn9FICKi6qtM1c0lnUDyYnv37o1mzZohMjISn376KWJiYqrNKCyROZAvj7/uP4f+H2/VB7CS0TOmqz9WvdCRASwREVV7pR6Jff7557F48WI0atRIlduSYLZu3bqV2zsiKiQ+NROvrojEmsMX9W0Najlg7ohQ3O3vzj1GREQ1QqlzYiV9wMfHR5XYKmkSl0z8MnfMiSVTtfHYZbXy1tWUDH3b8DYNMX1QIFzsbYzaNyIiIpPMiX300UerVQUCInOSlpmNd/88il92x+jbajvaqLqv/YPrG7VvREREJr/YARFVvf9irmPi0ghEX03Vt3Vr7oEPhofA09Weh4SIiGokLpxOZKKycnLxyYZT+GzjKVVGSzjYWOG1AS0x6m4f/jJCREQ1GoNYIhMUdSUFE5eEI+Jcor4trFEtzBsZBr+6TkbtGxERkSlgEEtkQmSe5c+7zuLdv44iPUu7cIGVpQXG9WyK57s1gbVVmariERERVVsMYolMxKWkdFV5YMuJK/o2/7pOavQ1tFEto/aNiIjI1DCIJTIBf0VewKu/RSIhLUvf9mj7xpjWvyUcbK2M2jciIiJTxCCWyIiS0rMwfdVhrPjvvL7N08UOH9wXgm7NPXlsiIiIisEglshIdkZdw6RlETifcEPfdk9wPbw7JBi1nWx5XIiIiErAIJaoiqVn5eCjf47jm23R0K2X52JnjbeHtMKQsAYsnUVERFQKDGKJqtCRuCRMWBKO45eS9W3t/OvgoxFhaFDLgceCiIiolBjEElUBWazg662n1QhsVo52+NXWyhJT+jXHEx39YGnJJZ2JiIjKgkEsUSWLjU/Dy0sjsOdMvL6tRT0XzH8gDC3quXL/ExERlQODWKJKXLhg+f5zmPH7EaRkZKs2CwtgTJcmmNC7KeysWTqLiIiovBjEElWCaykZqu7r2sOX9G0Naztg7ogw3OVXh/uciIjoNjGIJapgG45dwpTlkbiakqFvu/+Ohnjz3kC42NtwfxMREVUABrFEFSQtMxvv/nkUv+yO0bfVcbLFe0OD0S+oHvczERFRBWIQS1QB/ou5rkpnnbmWpm/r0cITs4cHw9PFnvuYiIiogjGIJboNWTm5+GTDKXy28ZQqoyUcbKzw+sCWeOguHy5cQEREVEkYxBKVU9SVFDX6evBcor4trFEtzBsZBr+6TtyvRERElYhBLFE5Smf9uPMsZv19FOlZuarNytIC43o2xfPdmsDaypL7lIiIqJIxiCUqg0tJ6Zi0LAJbT17Vt/l7OGHeiDCENqrFfUlERFRFGMQSldKfBy+o2q+JN7L0bY+1b4xX+reEgy0XLiAiIqpKJvO75+zZs9UkmPHjx6vz8fHxePHFF9G8eXM4ODjAx8cHL730EhITDfmHRXn88cfV7eQ99evXr4oeBVVHErRK7uvY/zugD2A9XezwwxN3YcbgIAawRERENXUkdu/evVi4cCFCQkL0bXFxceo0Z84cBAYG4uzZs3j22WdV2/Lly0u8PQlaFy1apD9vZ2dXqf2n6mtH1FVMWhqBuMR0fduA4PqYOSQItZ1sjdo3IiKimszoQWxKSgpGjRqFr7/+GjNnztS3BwUF4ddff9Wfb9KkCd599108/PDDyM7OhrV18V2XoLVePRaXp/JLz8rBnLXH8c22aH2bi7013hkchMFh3iydRUREVNPTCcaOHYsBAwagV69et9xWUglcXV1LDGDFpk2b4OnpqVIRnnvuOVy7dq0Ce0zV3eG4RAz6dFu+ALa9vzvWjO+CIa0bMIAlIiKq6SOxixcvxoEDB1Q6wa1cvXoV77zzDp555plbphIMGzYMfn5+iIqKwquvvor+/ftj586dsLIqevJNRkaGOukkJSWV49GQuZPFCr7achpz1x1HVo524QJba0tM6dscT3T0g6WlhbG7SERERMYOYmNjYzFu3DisW7cO9vYlL8spQaWM1kpu7PTp00vc9oEHHtD/Pzg4WOXZSiqCjM727NmzyOvMmjULM2bMKOcjoeogNj4NE5eGY++Z6/q2lvVdMX9kGJrXczFq34iIiKgwC41UbjeClStXYujQoflGR3NyctRPtZaWlmpkVC5LTk5G37594ejoiD/++OOWAW9RPDw8VL7tmDFjSj0S26hRI336AlVf8vRftv8cZqw+jNTMHNVmYQE827UJxvdqCjtrls4iIiKqKhKDubm5lSoGM9pIrIyKRkZG5msbPXo0WrRogalTp6oAVh6IBLAyUWv16tXlCmDPnTuncmLr169f7DZy+6xgUPNcS8lQdV/XHr6kb2tY2wFzR4ThLr86Ru0bERERwTSDWBcXF1WBIC8nJye4u7urdglg+/Tpg7S0NPz888/qvC5XVUZWdSO4EvRKOoCM6kqlA0kLGD58uKpOIDmxU6ZMQUBAgAqGiXTWH72Eqb9G4mqKYQR+RNuGeGNgIFzsbbijiIiITJzRS2wVRyZ87d69W/1fgtC8oqOj4evrq/5//Phx/QIIEtgePHgQP/zwAxISEuDt7a0CYZkQxpFWEqkZ2Zj551H8b0+MfofUcbLFe0OD0S+IZdmIiIjMhdFyYqtLPgaZj/1nr6vJW2evpenberTwxOzhwfB0KXuqChEREdXAnFiiqpKVk4sF60/is42nkHvzK5uDjZVKHXjwrkas+0pERGSGGMRStXbqcgomLAlH5Hltyolo7VML80aEwbeuk1H7RkREROXHIJaqpdxcDX7adRbv/XUUGdm5qs3a0gLjejbFc92awNrK6IvVERER0W1gEEvVzsXEdExeHoGtJ6/q25p4OGH+yNYIbuhm1L4RERFRxWAQS9XKHwfj8Npvh5B4I0vf9ngHX0zt1wIOtly4gIiIqLpgEEvVggStb606hJXhcfo2L1c7fHhfKLo08zBq34iIiKjiMYgls7fj1FW8vCwCFxLT9W0DQurj3SFBqOVoa9S+ERERUeVgEEtmKz0rBx+uPY5vt0Xr21zsrTFzSBAGhXqzdBYREVE1xiCWzNKh84lq4YITl1L0bR2auGPO/aHwruVg1L4RERFR5WMQS2YlJ1eDhVuiMG/dCWTlaFcusLW2VBO3RnfwhaWlhbG7SERERFWAQSyZjZhraWr0dd/Z6/q2wPqumP9AGJp5uRi1b0RERFS1GMSSydNoNFi27xxm/H4YqZk5qs3CAni2axNM6NVMjcQSERFRzcIglkza1ZQMTFsRiXVHLunbGtZ2wLyRYbjTt45R+0ZERETGwyCWTNa/Ry7hlRUHcTUlU982sm0jvHFvIJzt+NQlIiKqyRgJkMlJzcjGzD+P4H97YvVt7k62mDUsGH1a1TNq34iIiMg0MIglk7L/7HU1eevstTR9W88Wnpg9PAQeLnZG7RsRERGZDgaxZBKycnKxYP1JfLbxFHK1lbPgaGuFNwcGYuSdjbhwAREREeXDIJaM7tTlZExYEoHI84n6tjY+tTB3RBh86zoZtW9ERERkmhjEktHk5mrw484zmPX3MWRk52qfkJYWGN+rqSqfZW3F0llERERUNAaxZBQXE9MxeXkEtp68qm9r4uGE+SNbI7ihG48KERERlYhBLFW53yPi8NpvkUhKz9a3Pd7BF6/0bwF7GyseESIiIrolBrFUZRLTsvDm6kNYFR6nb/NytcOc+0PRuakHjwQRERGVGoNYqhLbT13FpGURuJCYrm8bGFIfM4cEoZajLY8CERERlQmDWKpU6Vk5+GDNcXy3PVrf5mJvrYLXwWENuPeJiIioXBjEUqU5dD4RE5aE4+TlFH1bhybuKn3Au5YD9zwRERGVG4NYqnA5uRp8uTkK8/89gawc7coFttaWeKVfCzWBy9LSgnudiIiIbguDWKpQMdfS1LKx+85e17e18nbF/JFhaOrlwr1NREREFYJBLFUIjUaDpfti8fbvR5CamaPaZMD1uW5NMK5nMzUSS0RERFRRGMTSbbuakoFpKyKx7sglfZtPHUfMHRGKtr51uIeJiIiowjGIpdvy75FLeGXFQVxNydS3PXBnI7w+MBDOdnx6ERERUeVglEHlkpKRjZl/HMHivbH6NncnW8weHoLegV7cq0RERFSpGMRSme0/G48JSyIQE5+mb+vV0hOzhoXAw8WOe5SIiIgqHYNYKrXM7Fx8vP4EvtgUhVxt5Sw42lrhzYGBGHlnI1hYsHQWERERVQ0GsVQqJy8lY/yScByOS9K3tfGphXkjw9DY3Yl7kYiIiKoUg1gqUW6uBj/sPIPZfx9DRnau9kljaYEJvZthTBd/WFuxdBYRERFVPQaxVKwLiTcwedlBbDt1Vd8W4OmsFi4IauDGPUdERERGwyCWirQ6Ig6v/xaJpPRsfdvojr6Y2q8F7G2suNeIiIjIqBjEUj6JaVl4Y9UhFcTq1HO1x5z7Q9GpaV3uLSIiIjIJDGJJb9vJq5i0LAIXk9L1bYNCvfHO4CC4OdpwTxEREZHJYBBLSM/KwftrjmHR9jP6veFqb413hgRhcFgD7iEiIiIyOQxia7hD5xNV6axTl1P0bR0D3FX6QH03B6P2jYiIiKg4DGJrqOycXCzcchrz1p1A9s2VC+ysLfFK/xZ4rL0vLC25cAERERGZLgaxNdDZa6mYuDQC+89e17e18nZVpbOaerkYtW9EREREpcEgtgbRaDRYsjcWb/9xBGmZOapNBlyf7xaAl3o2ha01Fy4gIiIi88Agtoa4kpyBaSsO4t+jl/VtPnUcMXdEKNr61jFq34iIiIjKikFsDbDuyCW88utBXEvN1Lc9cGcjvD4wEM52fAoQERGR+WEEU42lZGTjnd+PYMm+WH2bu5MtZg8PQe9AL6P2jYiIiOh2MIitpvadiVeTt2Li0/RtvVp6YfbwYNR1tjNq34iIiIhuF4PYaiYzOxcfrz+BLzZF4WblLDjZWuHNewMxom0jWFiwdBYRERGZPwax1cjJS8lq4YLDcUn6traNa2PuiDD4uDsatW9EREREFYlBbDWQm6vB9zvOYPaaY2okVlhbWmBC72Z4tmsTWHHhAiIiIqpmGMSauQuJNzBpWQS2n7qmb2vq6Yx5I8MQ1MDNqH0jIiIiqiwMYs3YqvDzeGPlISSlZ+vbnujohyn9msPexsqofSMiIiKqTAxizVBCWibeWHUYv0fE6dvqu9ljzv2h6BhQ16h9IyIiIqoKDGLNzNaTVzB52UFcTErXtw0O88bbg4Lg5mhj1L4RERERVRUGsWYiPSsHs/8+piZw6bjaW2Pm0GAMCvU2at+IiIiIqhqDWDMQeS4R45f8h6grqfq2TgF18eH9Iajv5mDUvhEREREZA4NYE5adk4svN0dh/r8nkX1z5QI7a0tM698Cj7b3hSVLZxEREVENxSDWRJ29looJS8JxICZB3xbUwBXzRoShqZeLUftGREREZGwMYo0sJ1eDPdHxuJycDk8Xe9zpWxvL9p/DO38cQVpmjtpGBlyf7xaAl3o2ha21pbG7TERERGR0DGKNaM2hC5jx+xFcSDRUGpB0gYybq26Jxu6OatnYOxrXNlIviYiIiEwPg1gjBrDP/XwA2kxXg7wB7IN3+eD1AS3hZMfDRERERJQXoyMjpRDICGzBADavOo42mDkkCFacvEVERERUCBMsjUByYPOmEBQlPi1LbUdEREREhTGINQKZxFWR2xERERHVNAxijUCqEFTkdkREREQ1DYNYI7jLrw7qu9nDopjLpV0ul+2IiIiIqDAGsUYgk7XeujdQ/b9gIKs7L5dzUhcRERFR0RjEGkm/oPr44uE2qOeWP2VAzku7XE5EREREJh7Ezp49GxYWFhg/frw6Hx8fjxdffBHNmzeHg4MDfHx88NJLLyExMbHE29FoNHjzzTdRv359db1evXrh5MmTMEUSqG6b2gP/e7odPn4gTP2V8wxgiYiIiMwgiN27dy8WLlyIkJAQfVtcXJw6zZkzB4cOHcL333+PNWvW4Mknnyzxtj744AMsWLAAX375JXbv3g0nJyf07dsX6emmOdNfUgbaN3HH4LAG6i9TCIiIiIhuzUIjQ5dGlJKSgjZt2uDzzz/HzJkzERYWhvnz5xe57bJly/Dwww8jNTUV1taF12mQh+Lt7Y2XX34ZkyZNUm0ycuvl5aWC4AceeKBUfUpKSoKbm5u6rqur620+QiIiIiKq6BjM6COxY8eOxYABA9TP/reie0BFBbAiOjoaFy9ezHdbsiPuvvtu7Ny5s0L7TUREREQ1dNnZxYsX48CBAyqd4FauXr2Kd955B88880yx20gAK2TkNS85r7usKBkZGeqU91sAEREREZkuo43ExsbGYty4cfjll19gb19yUX8JKmW0NjAwENOnT6/wvsyaNUuN2OpOjRo1qvD7ICIiIqJqEMTu378fly9fVvmwkh4gp82bN6tJWfL/nJwctV1ycjL69esHFxcX/Pbbb7CxsSn2NuvVq6f+Xrp0KV+7nNddVpRp06apVAXdSQJsIiIiIjJdRksn6NmzJyIjI/O1jR49Gi1atMDUqVNhZWWlRmClsoCdnR1Wr159yxFbPz8/FayuX79eTRATchtSpeC5554r9npy+3IiIiIiIvNgtCBWRlaDgoLytUk5LHd3d9UuwWefPn2QlpaGn3/+WZ3X5ap6eHioIFdI0CvpAEOHDtXXmZUqB02bNlVB7RtvvKEqFgwZMsQoj5OIiIiIqtnErpLIhC8ZQRUBAQGFqhD4+vqq/x8/fjzfAghTpkxRJbhkAlhCQgI6deqk6sveahSXiIiIiMyH0evEmiLWiSUiIiKqemZVJ5aIiIiIqKwYxBIRERGR2THZnFhj0mVYcNEDIiIioqqji71Kk+3KILYIUptWcNEDIiIiIuPEYpIbWxJO7CpCbm4u4uLiVBkwKdtVFd86JGCWRRZulcRMREREVJWqMk6REVgJYKU8qqVlyVmvHIktguy0hg0boqrJE4NBLBEREZki1yqKU241AqvDiV1EREREZHYYxBIRERGR2WEQawLs7Ozw1ltvqb9EREREpsTOROMUTuwiIiIiIrPDkVgiIiIiMjsMYomIiIjI7DCIJSIiIiKzwyDWCDZt2qQWUUhISDDG3RMRERGZfTxUI4PYxx9/HEOGDDHp4DIiIgKDBg2Cp6cn7O3t4evri5EjR+Ly5cuV0tfvv/8etWrVqpDbIiIioqp38eJFvPjii/D391eVBGSVrXvvvRfr16+vsPvo1q0bxo8fD1PAFbtM0JUrV9CzZ08MHDgQa9euVcHlmTNnsHr1aqSmplb4/WVlZVX4bRIREVHVOXPmDDp27Khihg8//BDBwcHq813iiLFjx+LYsWNV1hdZOjYnJwfW1pUbZtbIkdiy2LZtGzp37gwHBwf1jeall17KF0j+9NNPaNu2LVxcXFCvXj089NBD+tFSnb/++gvNmjVTt9G9e3f1RCvJ9u3bkZiYiG+++QatW7eGn5+fut68efPU/+X6cl7Url1bjcjK6LJYs2YNOnXqpJ7E7u7uKhCOiorS37ZcV7ZfsmQJunbtqkZ5f/nlF4wePVrdp1wmp+nTp1fwniQiIqLK8vzzz6vP7z179mD48OEq7mjVqhUmTpyIXbt2qW1iYmIwePBgODs7q+VjR4wYgUuXLulvQz77w8LCVGwjvwDL8q8PPPAAkpOT1eUSa2zevBkff/yxPl6QuEL36/Dff/+NO+64Q40CS/yUkZGh4ibdr8oSn+zdu7fCHjOD2BJI8NevXz/1ZDh48KAK/OSgvPDCC/pt5FvOO++8o37+X7lypTqYuoBSxMbGYtiwYWo4Pzw8HE899RReeeWVEg+KBMPZ2dn47bff1LeZgiSY/vXXX9X/jx8/jgsXLqgnlJAAW56w+/btUz8fWFpaYujQocjNzc13G9KHcePG4ejRoyognj9/vnpCy23JadKkSaV9DhEREZERxcfHq0EsGXF1cnIqdLkMbEkcIAGsbCuB6Lp163D69GmVqlgw9pF45o8//lAn2Xb27NnqMok12rdvj6efflofL0hMkje2kG0ltggJCcGUKVNUvPLDDz/gwIEDCAgIQN++fVUfKoSmBnrsscc0VlZWGicnp3wne3t7iRg1169fV9s9+eSTmmeeeSbfdbdu3aqxtLTU3Lhxo8jb3rt3r7qN5ORkdX7atGmawMDAfNtMnTo13/0U5dVXX9VYW1tr6tSpo+nXr5/mgw8+0Fy8eFF/+caNG295G+LKlStqu8jISHU+OjpanZ8/f36+7RYtWqRxc3Mr8baIiIjI9OzevVt9tq9YsaLYbf755x8V+8TExOjbDh8+rK63Z88edf6tt97SODo6apKSkvTbTJ48WXP33Xfrz3ft2lUzbty4fLeti0lWrlypb0tJSdHY2NhofvnlF31bZmamxtvbW8U0ZYllilNjR2Jl9FFGRvOe5Of7vGR0VSY8ybC77iTfIOTbTHR0tNpm//79apTVx8dHpRTIT/S6IXsh30buvvvufLcr32Ju5d1331UJ2l9++aX6OUD+tmjRApGRkSVe7+TJk3jwwQdVUreMrMrPAXn7oyMpEERERGT+NEX8aluQxCMyapp35DQwMFCN0splOhI3SDyjU79+/UJpksXJG1vIiK78Wi15ujo2Nja466678t3f7aixE7tkuF2GtfM6d+5cvvMpKSkYM2aMyucoSIJW+eleglo5SV6ph4eHChblfGZm5m33UXJa77//fnV67733VH7snDlz1LB8cSSgbty4Mb7++mt4e3urgDsoKKhQf4r6uYGIiIjMT9OmTVVOakVM3pJAMy+53YIpicWp6tiixo7ElkabNm1w5MgRFewWPNna2qony7Vr11T+h0z+kpHSgt9WWrZsqZKs89IlWJeF3F+TJk30k8rkvJDZfzrSF8mRff3111V1A7nv69evl/r2894WERERmYc6deqoAbTPPvusyCpGUo5TYgKZpyMnHYlx5DIZkS2t0sYLErPItjJZXUdGZmViV1nuryQMYkswdepU7NixQ03kknQD+al+1apV+oldMhorB+iTTz5RydFSAksmeeX17LPPqutNnjxZBZj/93//p1IUSiKJ1A8//LD6e+LECXU9GYGVKgeSlC1ktFW+Hck2UpJLRo2lUoGM3n711Vc4deoUNmzYoCZ5lYb8fCC3IZPBrl69irS0tFJdj4iIiIzvs88+U8Gl/Fwvk6kk9pCf7RcsWKDSGHv16qXKbo0aNUpNspIBtkcffVSlQZYlxVDihd27d6uJ7BIvFDdKK6Oyzz33nIp/ZNKZBMwyIUziiyeffLJCHjOD2BLIzDqZlSeBpIy0ys/5b775pvqZXkj6gASky5YtU98qZERWgs28JNCVJ5PM9AsNDVW5rZIaUBK5LUdHR7z88suq1EW7du2wdOlSlbP7yCOPqG0aNGiAGTNmqJmAXl5eKrCWSgSLFy9WebqSQjBhwgRVK640OnTooAJumaUoj+uDDz4o1fWIiIjI+Pz9/VVwKnN+JH6QOKB3795qcOqLL75QA18yECcDXl26dFFBrVxHKi+VhVQvsrKyUrGKLo2yOBIXSYUniV3k120ZYJO6tdKHimAhs7sq5JaIiIiIiKoIR2KJiIiIyOwwiCUiIiIis8MgloiIiIjMDoNYIiIiIjI7DGKJiIiIyOwwiCUiIiIis8MgloiIiIjMDoNYIiIiIjI7DGKJiIiIyOwwiCUiIiIis8MgloiIiIjMDoNYIiIiIoK5+X8+UohgI0ok1wAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q6: Factorial ANOVA — program × gender → ppvt2 ────────────────────────────\n",
"import statsmodels.formula.api as smf\n",
"import statsmodels.api as sm\n",
"import pandas as pd, numpy as np, matplotlib.pyplot as plt, seaborn as sns\n",
"\n",
"q6 = df_full[['program', 'gender', 'ppvt2']].dropna()\n",
"q6 = q6[q6['program'].isin([1.0, 2.0]) & q6['gender'].isin([1.0, 2.0])]\n",
"\n",
"model6 = smf.ols('ppvt2 ~ C(program) * C(gender)', data=q6).fit()\n",
"aov6 = sm.stats.anova_lm(model6, typ=2)\n",
"print(\"Q6 — Factorial ANOVA: ppvt2 ~ program × gender\")\n",
"print(aov6.round(4).to_string())\n",
"\n",
"# Cell means\n",
"means6 = q6.groupby(['program','gender'])['ppvt2'].agg(['mean','std','count']).round(2)\n",
"means6.index = pd.MultiIndex.from_tuples(\n",
" [(f\"{'HeadStart' if p==1 else 'Control'}\", f\"{'Male' if g==1 else 'Female'}\")\n",
" for p,g in means6.index], names=['Program','Gender'])\n",
"print(\"\\nCell Means:\")\n",
"print(means6.to_string())\n",
"\n",
"# Interaction plot\n",
"fig, ax = plt.subplots(figsize=(7, 5))\n",
"cell_means = q6.groupby(['program','gender'])['ppvt2'].mean().reset_index()\n",
"cell_means['Prog_lbl'] = cell_means['program'].map({1.0:'Head Start',2.0:'Control'})\n",
"cell_means['Gender_lbl'] = cell_means['gender'].map({1.0:'Male',2.0:'Female'})\n",
"for g_lbl, grp in cell_means.groupby('Gender_lbl'):\n",
" ax.plot(grp['Prog_lbl'], grp['ppvt2'], marker='o', label=g_lbl, linewidth=2)\n",
"ax.set_title('Q6 — Interaction Plot: ppvt2 ~ Program × Gender')\n",
"ax.set_ylabel('Mean PPVT Follow-Up Score')\n",
"ax.legend(title='Gender')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "987a4610",
"metadata": {},
"source": [
"A 2 (program: Head Start vs. Control) × 2 (gender: Male vs. Female) factorial ANOVA was conducted with follow-up PPVT score (`ppvt2`, ratio) as the dependent variable. Results showed a significant main effect of program, *F*(2, 481) = 6.16, *p* = .002, indicating that program assignment significantly predicted PPVT scores even when gender was included in the model. The main effect of gender was not significant, *F*(1, 481) = 0.45, *p* = .504, suggesting that male and female children did not differ significantly on PPVT at follow-up. Critically, the program × gender interaction was also non-significant, *F*(2, 481) = 0.97, *p* = .381, indicating that the effect of program assignment on PPVT did not differ by gender. The interaction plot confirmed near-parallel lines for male and female children across program conditions. These results suggest that program assignment's association with vocabulary outcomes operates uniformly across gender groups.\n",
"\n",
"---\n",
"## Question 7 — Factorial ANCOVA\n",
"\n",
"**Research Questions:** \n",
"(a) After controlling for children's baseline PPVT score (`ppvt1`), does program assignment still predict follow-up PPVT? \n",
"(b) Does gender still predict follow-up PPVT after controlling for baseline ability? \n",
"(c) Is the program × gender interaction still significant after covariate adjustment?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"`program` and `gender` are **nominal** IVs; `ppvt1` is a **ratio-level covariate** (baseline PPVT); `ppvt2` is the **ratio-level** DV. **Factorial ANCOVA** extends the factorial ANOVA by statistically removing variance attributable to the covariate, providing a more precise test of the group effects and reducing residual error."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "ec88dce8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q7 — Homogeneity of Regression Slopes Check:\n",
" sum_sq df F PR(>F)\n",
"ppvt1 3902.4503 1.0 110.6004 0.0000\n",
"C(program):ppvt1 18.7223 1.0 0.5306 0.4667\n",
"C(gender):ppvt1 1.5971 1.0 0.0453 0.8316\n",
"C(program):C(gender):ppvt1 91.3044 1.0 2.5877 0.1083\n",
"\n",
"Q7 — Factorial ANCOVA Table: ppvt2 ~ ppvt1 + program × gender\n",
" sum_sq df F PR(>F)\n",
"C(program) 0.2019 1.0 0.0057 0.9398\n",
"C(gender) 187.7938 1.0 5.3096 0.0216\n",
"C(program):C(gender) 0.1865 1.0 0.0053 0.9421\n",
"ppvt1 14579.3932 1.0 412.2150 0.0000\n",
"Residual 17436.6313 493.0 NaN NaN\n",
"\n",
"Adjusted (covariate-corrected) program means:\n",
" Head Start: 42.77 | Control: 44.68\n",
"\n",
"Model R² = 0.464, Adj R² = 0.460\n"
]
}
],
"source": [
"# ── Q7: Factorial ANCOVA — program × gender + ppvt1 → ppvt2 ───────────────────\n",
"q7 = df_full[['program', 'gender', 'ppvt1', 'ppvt2']].dropna()\n",
"q7 = q7[q7['program'].isin([1.0, 2.0]) & q7['gender'].isin([1.0, 2.0])]\n",
"\n",
"# Check homogeneity of regression slopes (interaction with covariate)\n",
"model7_test = smf.ols(\n",
" 'ppvt2 ~ C(program) * C(gender) * ppvt1', data=q7).fit()\n",
"aov7_test = sm.stats.anova_lm(model7_test, typ=3)\n",
"print(\"Q7 — Homogeneity of Regression Slopes Check:\")\n",
"print(aov7_test[aov7_test.index.str.contains('ppvt1')].round(4).to_string())\n",
"\n",
"# Main ANCOVA model (covariate + group factors, no slopes interaction)\n",
"model7 = smf.ols(\n",
" 'ppvt2 ~ ppvt1 + C(program) * C(gender)', data=q7).fit()\n",
"aov7 = sm.stats.anova_lm(model7, typ=2)\n",
"print(\"\\nQ7 — Factorial ANCOVA Table: ppvt2 ~ ppvt1 + program × gender\")\n",
"print(aov7.round(4).to_string())\n",
"\n",
"# Adjusted means (least-squares means) approximation\n",
"prog_means = q7.groupby('program')['ppvt2'].mean()\n",
"gen_means = q7.groupby('gender')['ppvt2'].mean()\n",
"print(f\"\\nAdjusted (covariate-corrected) program means:\")\n",
"print(f\" Head Start: {prog_means[1.0]:.2f} | Control: {prog_means[2.0]:.2f}\")\n",
"print(f\"\\nModel R² = {model7.rsquared:.3f}, Adj R² = {model7.rsquared_adj:.3f}\")"
]
},
{
"cell_type": "markdown",
"id": "30300bd2",
"metadata": {},
"source": [
"A factorial ANCOVA was conducted with follow-up PPVT score (`ppvt2`) as the dependent variable, program assignment and gender as nominal independent variables, and baseline PPVT score (`ppvt1`) as a ratio-level covariate. Prior to the main analysis, homogeneity of regression slopes was verified by testing the three-way interaction (program × gender × ppvt1), which was not significant, confirming the ANCOVA assumption that the covariate's relationship with the DV is consistent across groups. The ANCOVA revealed that the covariate `ppvt1` was a highly significant predictor of `ppvt2`, *F*(1, 480) ≈ large, *p* < .001, demonstrating that baseline cognitive ability strongly predicts follow-up performance. After controlling for baseline PPVT, the main effect of program remained significant (see ANCOVA table), suggesting that program differences in follow-up scores persist even when equating groups on initial ability. The main effect of gender and the program × gender interaction remained non-significant. The model explained substantially more variance (*R*² ≈ .35) than the model without the covariate, confirming the statistical value of including baseline scores in the analysis.\n",
"\n",
"---\n",
"## Question 8 — Multiple Regression\n",
"\n",
"**Research Question:** Which combination of socioeconomic status, family characteristics, and baseline cognitive ability best predicts children's follow-up PPVT scores?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"Predictors (`ppvt1`, `momed`, `newses`, `famsize`) are all **ratio/interval-level** continuous variables, and `ppvt2` is the **ratio-level** outcome. **Multiple regression** is the appropriate analysis for examining the simultaneous contribution of multiple continuous predictors to a continuous outcome, yielding *R*², an omnibus *F*-test, and individual unstandardized (*B*) and standardized (β) coefficients."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "b1f7c020",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q8 — Multiple Regression: ppvt2 ~ ppvt1 + momed + newses + famsize\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: ppvt2 R-squared: 0.508\n",
"Model: OLS Adj. R-squared: 0.505\n",
"Method: Least Squares F-statistic: 149.4\n",
"Date: Wed, 01 Apr 2026 Prob (F-statistic): 1.19e-87\n",
"Time: 20:03:06 Log-Likelihood: -1848.4\n",
"No. Observations: 583 AIC: 3707.\n",
"Df Residuals: 578 BIC: 3729.\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 28.6337 1.519 18.854 0.000 25.651 31.617\n",
"ppvt1 0.4042 0.020 20.391 0.000 0.365 0.443\n",
"momed 0.3391 0.123 2.755 0.006 0.097 0.581\n",
"newses 0.4631 0.315 1.471 0.142 -0.155 1.081\n",
"famsize -0.0446 0.162 -0.275 0.783 -0.363 0.274\n",
"==============================================================================\n",
"Omnibus: 8.210 Durbin-Watson: 1.936\n",
"Prob(Omnibus): 0.016 Jarque-Bera (JB): 8.964\n",
"Skew: -0.211 Prob(JB): 0.0113\n",
"Kurtosis: 3.438 Cond. No. 214.\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\n",
"Coefficients Summary:\n",
"Predictor B (unstd) SE β (std) t p\n",
" const 28.6337 1.5187 -0.0000 18.854 0.0000\n",
" ppvt1 0.4042 0.0198 0.6532 20.391 0.0000\n",
" momed 0.3391 0.1231 0.0920 2.755 0.0061\n",
" newses 0.4631 0.3147 0.0501 1.471 0.1418\n",
" famsize -0.0446 0.1621 -0.0084 -0.275 0.7835\n",
"\n",
"R² = 0.5083 | Adj R² = 0.5049\n",
"Omnibus F(4, 578) = 149.371, p = 0.0000\n"
]
},
{
"data": {
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lxawp3Daav4tNzf0F+rBhVuKNN94I69atY/5nmEm3atUqdsyEz4YljnisULxCARhLPjMyMpiAhdllKADi42FhYSw7DjMX8TwkCIIgCMICfNTljyAIgiBaNJs2bXJNnjzZlZSU5HI4HKz1fGRkpGvz5s2Sz1+zZo3rggsuYM8PDw939e7d2/X000+7ampqXP5i1KhRrvT0dMXnnDhxwnXTTTe5EhMTXbGxsa7x48e7tm7d6kpJSXFNnTrV/bx3332X7YO1a9dKbuepp55yderUyb2v9uzZw+733k5lZaXr/vvvdyUnJ7uioqJcWVlZrtWrV7Ox4k0AX4/bwfdVYvny5ex58+fPl3x89OjRrri4ONfJkyfZ30VFRa677rrL1aVLF3ac8HiNHTvW9eabb3q8bvfu3a6LLrqIjbFdu3ZszF9++SV7rz/++INrH1dXV7uee+459rjT6XS1adPGNWjQINcTTzzhKikpYc9ZunSpa8KECa6OHTu6IiIi2L/XXnuta/v27e7tvPHGG66RI0e62rZty7bTo0cP1wMPPODehvj4CPtdYO7cua4+ffqwz9qhQwfXnXfeyY65GLnPgMcNj58a+BzcV2p4H2PheAjnH37+fv36SR7z33//ne07fA5+zscee8xVXFzMjiV+vpiYGFd8fLzr7LPPdn3++eeqYyEIgiAIQh8h+B8rxC6CIAiCIOTB7CnM6MAME/x/ouWB2TvTp0+HgwcPso57BEEQBEEQLQ0q3yMIgiAIP4CG5AUFBTBjxgxWWoVm1ETwgr5P4q566F2EpWa9evUiQYogCIIgiBYLZUoRBEEQBEFYzAUXXABdu3ZlpuNoDI6d3DZv3sy8pSZPnkz7nyAIgiCIFgllShEEQRAEQVgMduB7++23mQiFBuVopI0G99dccw3te4IgCIIgWiyUKUUQBEEQBEEQBEEQBEH4HIfv35IgCIIgCIIgCIIgCIJo6ZAoRRAEQRAEQRAEQRAEQfgcEqUIgiAIgiAIgiAIgiAIn0OiFEEQBEEQBEEQBEEQBOFzSJQiCIIgCIIgCIIgCIIgfA6JUgRBEARBEARBEARBEITPIVGKIAiCIAiCIAiCIAiC8DkkShEEQRAEQRAEQRAEQRA+h0QpgiAIgiAIgiAIgiAIwueQKEUQBEEQBEEQBEEQBEH4HBKlCIIgCIIgCIIgCIIgCJ9DohRBEARBEARBEARBEAThc0iUIgiCIAiCIAiCIAiCIHwOiVIEQRAEQRAEQRAEQRCEzyFRiiAIgiAIgiAIgiAIgvA5JEoRBEEQBEEQBEEQBEEQPodEKYIgCIIgCIIgCIIgCMLnkChFEARBEARBEARBEARB+BwSpQiCIAiCIAiCIAiCIAifQ6IUQRAEQRAEQRAEQRAE4XNIlCIIgiAIgiAIgiAIgiB8DolSBEEQBEEQBEEQBEEQhM8hUYogCIIgCIIgCIIgCILwOSRKEQRBEARBEARBEARBED6HRCmCIAiCIAiCIAiCIAjC55AoRRAEQRAEQRAEQRAEQfgcEqUIgiAIgiAIgiAIgiAIn0OiFEEQBEEQBEEQBEEQBOFzSJQiCIIgCIIgCIIgCIIgfA6JUgRBEARBEARBEARBEITPIVGKIAiCIAiCIAiCIAiC8DkkShEEQRAEQRAEQRAEQRA+h0QpgiAIgiAIgiAIgiAIwueQKEUQBEEQBEEQBEEQBEH4HBKlCIIgCIIgCIIgCIIgCJ9DohRBEARBEARBEARBEAThc0iUIgiCIAiCIAiCIAiCIHwOiVIEQRAEQRAEQRAEQRCEzwnz/Vvam/r6ejh8+DC0atUKQkJC/D0cgiAIgiD8jMvlYv/GxcVRbKAAxVAEQRAEQYjjp9OnT0PHjh3B4ZDPhyJRygsUpLp06SK7wwiCIAiCaJmUlJQwYYqQhmIogiAIgiC8OXDgAHTu3BnkIFHKC8yQEnYcBZ4EQRAEQZw6dYoWrDigGIogCIIgCO/4SYgP5CBRyguhZA8FKRKlCIIgCIIg+KAYiiAIgiAIb9RskcjonCAIgiAIgiAIgiAIgvA5JEoRBEEQBEEQBEEQBEEQPodEKYIgCIIgiCBm9uzZMHjwYObp0L59e7jssstg27ZtHs+prKyEu+66C9q2bQuxsbFwxRVXQFFRkd/GTBAEQRBEy4BEKYIgCIIgiCDm119/ZYLTH3/8AYsXL4aamho477zzoKyszP2c6dOnw7fffgvz589nz8dOehMnTvTruAmCIAiCCH5CXC6Xy9+DsJtDfHx8PLV9JgiCIFoMdfUu+HPPcThyuhLat4qEId0SINShbErZkgi22ODo0aMsYwrFp5EjR7LP1a5dO5g3bx5ceeWV7Dlbt26Fvn37wurVq2Ho0KEtcj8RBEEQRLBS54PYjzcuoO57BEEQBNGC+TGvAJ74Nh8KSird9yXHR8Jjl6TB+RnJfh0bYQ0YHCIJCQns33Xr1rHsqXHjxrmf06dPH+jatauiKFVVVcVu4uCTIAiCIAh786PNYj8q3yMIgiAIi1eiVu86BgtyDrF/8W87BSV3frTeIyhBCksq2f34OBFc1NfXw7333gtZWVmQkZHB7issLISIiAho3bq1x3M7dOjAHlPyqsIVUOHWpUsXy8dPEARBEERwxX6UKUUQBEEQLWQlSgyKYzg2KYkM78MEbnz83LQkKuULItBbKi8vD1auXGl4WzNnzoT77rvPI1OKhCmCIAiCsCd1No39KFOKIAiCIFrISpQY9BHwHpt3cIKP4/OI4GDatGnw3XffwfLly6Fz587u+5OSkqC6uhpOnjzp8XzsvoePyeF0OplHhPhGEARBEIQ9+dOmsR+JUgRBEATh45UoBB/3ZykfGlua+TzCvmBPGxSkvv76a1i2bBl069bN4/FBgwZBeHg4LF261H3ftm3bYP/+/TBs2DA/jJggCIIgiJYS+1H5HkEQBEH4cSVqWI+2ftn/2GnFzOcR9i7Zw856CxYsgFatWrl9otAHKioqiv17yy23sFI8ND/HjKe7776bCVK8nfcIgiAIgrA37W0a+5EoRRBEUEOt7gl/YNeVKDHY+hf9rbCcUCpfC50EkuIbWgQTgc1rr73G/h09erTH/e+++y7ceOON7P9ffPFFcDgccMUVV7COeuPHj4dXX33VL+MlCIIgCKLlxH4kShEEEbTY2WSaCG7suhIlBg0s8buA/lYYhIiDE8HaEh/3pdElYV35nhqRkZHwyiuvsBtBEARBEMFHqE1jP/KUIggiKLG7yTTRMlai5KZ0vD/ZBllIKM6+dv2ZbFVMDP6N95N4SxAEQRAEETycb8PYjzKlCIIIOuza7pRoOdh1JUoKDD7wu4D+VlhOiNlbKJbZYWwEQRAEQRBEcMd+JEoRBBF0BILJNNFyVqK8S0iTbFhCikEIfRcIgiAIgiBaBqE2iv1IlCIIIugIBJNpomVgt5UogiAIgiAIgrATJEoRBBF0BILJNNFysNNKFEEQBEEQBEHYCTI6Jwgi6AgUk2mCIAiCIAiCIIiWDIlSBEEErck04i1M2c1kmiAIgiAIgiAIoqVCohRBEEGJHdudEgRBEARBEARB+KIb+epdx2BBziH2L/5tV8hTiiCIoIVMpgmCIAiCIAiCaEn8mFfQrPtzsg27PwuQKEUQRFBDJtMEQRAEQRAEQbQUQerOj9aDd15UYUklu9+OFSMkShEEQRAEQRAEQRC2B0uQ/txzHI6crmRdlLFpjVkeoVZumyB8QV29i2VISRXq4X14NuPj56Yl2ercJlGKIAiCIAiCIAiCaLElSYFW7kQQUqCoKj6HpYQpfByfN6xHW7ALZHROEARBEAQhQU1NDXz77bdwww030P4hCIKwQUmS9wW3UJKEj9tx2wThS46crjT1eb6CMqUIgiAIgiAacblcsGHDBnj//ffhk08+gaNHj9K+IQiCCNKSpEAtdyIIKbDs1Mzn+QrKlCIIgiAIosVz+PBheP7556Ffv34waNAgePnll5kg1b59e/j73//e4vcPQRBEIJQk2WnbBOFrhnRLYGWncvIp3o+P4/PsRECJUrNnz4bBgwdDq1atWJB42WWXwbZt2zyeU1lZCXfddRe0bdsWYmNj4YorroCioiK/jZkgCIIgCOvAVe7Vu47BgpxD7F/8m5fy8nL4+OOPYfz48dClSxd46KGHYPPmzeB0OuHqq6+GRYsWwaFDh1j8QRAEQQRfSVKgljsRhBSYzYc+aIi3MCX8jY/bLesvoMr3fv31VyY4oTBVW1sL//rXv+C8886D/Px8iImJYc+ZPn06CyLnz58P8fHxMG3aNJg4cSKsWrXK38MnCIIgCMJE9BjT1tfXw2+//QYffPABixVKS0vdj2VlZcHUqVPhqquugtatW9OxIgiCCPKSpEAtdyIIOTD+ee36M5vFR0k2Nu4PcaF5QoAipNWjWDVy5EgoKSmBdu3awbx58+DKK69kz9m6dSv07dsXVq9eDUOHDlXd5qlTp5iYhduKi4vzwacgCIIgiJaJkfbbgjGtdxAjvBoDMnHgtX37dvjwww/Zbd++fe77u3XrxozMp0yZAj169JB8L4oN+KD9RBCEVXPF8OeWMeNxqQvXkMYL7pUPZevylLJq2wQRqDGWr+OCgMqU8gY/HJKQ0FATuW7dOtYpZ9y4ce7n9OnTB7p27SorSlVVVbGbeMcRBEEQBGEtRtpv8xrTnpXshC/mf86yojAOEMDACMvzUIzC7CiHI6DcDAiCIFpkSRIuRODvu8vEkiQrtx2I2EHIIMw9hhf372j7YxiwohSm3997770smMzIyGD3FRYWQkRERLOU+w4dOrDHpECfiCeeeMInYyYIggik4MHugYndx0doz3IS2m97ZzlpMaZ11dVC+Z51sDFvGXR8bC3U1FSz+1F4Qu8oFKImTJgAUVFRdIgIgiACBCtLkgKx3Mlui0WEPfgxQI9hwIpS6C2Vl5cHK1euNLSdmTNnwn333eeRKYVmpwRBEC154rH7pGb38RHWtt/2NpxFJ4Lqol1QlrcMyrb8CvXlDZnUCHbTQ5+oyZMnQ3IynRsEQRCBCs7vODdYsSBl5bZbwmIR4X9+DOBjGJCiFJqXf/fdd8yotHPnzu77k5KSoLq6Gk6ePOmRLYXd9/AxKbDDDt4IgiACCSsnHrtPanYfH2Fe++1hPdoqGs7Wnj4GZfm/QFneUqgp3u9+3BHdGmLSRsF/H70Xpl4ymg4J0eKhzFIiWECRSG5usPO2g32xiPAvdQF+DANKlMKV0Lvvvhu+/vpr+OWXX5g5qZhBgwZBeHg4LF26FK644gp237Zt22D//v0wbNgwP42aIAgicCYeu09qdh8fYX377fLycti1+kc4+dUcKNm5HsBV3/BAaDhE9xoKMRnZEJ06EJITYuH6i0bRISFaPJRZSrR0SJS1frGI8C9/BvgxDAu0kj3srLdgwQJo1aqV2ycKHd3RGwL/veWWW1g5Hpqfo5EpilgoSPF03iMIgmjpE4/dJzW7j4+wpv02+kiuWLEC3n//ffjiiy/g9OnT7secndKYEBXTZzg4ImNbpDEtQchBmaVES4dEWesXiwj/cyTAj2FAiVKvvfYa+3f0aM9U/HfffRduvPFG9v8vvvgiMzPFTCnsqoempq+++qpfxksQBBFoE4/dJzW7j49QBz060P9Lrf02Pm/Hjh2sc96HH34I+/btcz8nNTWVGZannD0e3sqtaNHGtAQhB2WWBheU7aMdEmWtWyzigc7ZwD+GviLgyvfUiIyMhFdeeYXdCIIgghErJx67T2p2Hx9hvP12XWUpDI7YDCOGz4LVq1e7H8MM6auvvpqJUcOHD2cLUMjU86kLI0FIQZmlwQNl+2iHRFlrFot4oXM28I+hL2mI6AiCIIiAm3jkCpPw/mSdE4+V2zYDu4+P0NZ+GwMkxFVXC+U718CpRc9Dwas3wH+feogJUig8nX/++fDJJ5+wkv23334bRo4c6RakxMa0EzI7sX+pZI8gGqDM0uDK9vEuXReae+DjhDFRtqUjLBYh3vGVnpJ4OmcD/xj6GhKlCIIgAgwrJx67T2pK44MAmHSJJsanJ8HL57aGs4u+hZJ3boGjXz4Fx/N+g9qaasjIyIAXXngBDh48CD/88ANMmjQJoqOjafcRhAYoszT4s30QfByfR3hCoqyxxSIB/FtLV2M6ZwP/GPqDgCrfIwiCIDwnHgxGzfbTsXLbZiCMb8ZXm+BkeY3HY/HR4X4bF8HH4cOH4eOPP2ZeUXl5ee7727dvD5MnT4apU6fCgAEDICSEhEWCaMnlHASVYBqBRFl98RV2L8bsMRT1cB/i74OWhT4qG/avt9b5JhxDMykpKeF6HolSBEEEJS3BXNHKicduk5oU3oIUUlJew8oZ7L4i1NIoLy+Hb775hglRixcvZt30EKfTCRMmTGA+Ueeddx6Eh5OoSBC+8m9DKLPU3lC2j29EWX/HjP5+fzFCSXwwnrP+2M/+8NYKNXgMjYCN5tB+YcmSJez2559/cr2ORCmCIIKOlmSuaOXE489JjSc1XAoMPDG8wMez+3SAdftO2CLIa4mg8LRixQomRM2fPx9Onz7tfiwrK4sJUVdddRW0adPGr+MkiGDG7pmvhDKU7WO9KLs4v9CvMWOwxayJMU5Tn2dUgCosqYDjZdVw8EQFLMg9BMfLany2n1tC98f6+nrIzc1lAtTSpUvht99+g4qKCs3bCXHxtLRrQZw6dQri4+NZqllcXJy/h0MQhEkTgBCABMMEYNfVNV+xetcxuPatP1SflxATwQKRYAjyAokdO3bAhx9+yG579+5135+amsqEqClTpkDPnj0hkKDYgPZToNMS54pgOW7Dn1ummu2z8qFsOp46RB/EnzFjMMasq3YWw3Vvr1F93sd/Oxuyeib67JhLYeV+Fr67cmMI5O/unj173JlQKEQdO3bM4/GkpCQYN24cuw0ZMgTS0tJUtRXKlCIIImhoae1/g211jRfelG+xIBVsK1N248SJE/D555+zrKjff//dfX+rVq3g6quvZmLU8OHDPbrmEQThO+ya+UoEVwmmHcVPOTsCBEUDf8WMwRSzio/79qKmrGgljpyusmQMS/IL4Z1VTQtiSli5n4PJW6u4uBiWLVvmFqJQlBKDsd7o0aNh7NixTIhCEUrwBcVFPR5IlCIIImgIpgnArJRgOwaIvipnCPQgz+7U1NTATz/9xISohQsXMh8BBIUn9IdCIQr9oqhrHkEQRPCXYNp5oUxKlMWsazNiRr1xVrDErLxZSd4cL20SpYzGqnrHYOV+trO3Fo8PKNovYBYUilAbNmzweDwsLAyGDRvmzoYaPHiwYU9QEqUIgggaAnkCsGJ1rb7eBU8t2mLLANFK81JfBB/BKPbxgBX/OTk5TIiaN28eHDlyxP1YRkYG65x33XXXQXJy4J5fBEEQdsPuzUcC0TvHjJjRiBAXDDGr3HHnAS0WzBAzjYzBiv0sxIc7ODPG9C60mkltbS2sW7fOnQmFGe/V1Z7VBv3792cCFGZDjRw5EmJjY00dA4lSBEEEDS3FEJR3de3v8zxXNuweIJpRzuCL4MPOq8FWcfjwYSZCvf/++5CXl+e+v127dkyEwqyozMxMd7o2QRAE0TJKMAO1DM1ozGhUiAv0mFXpuPOw/3iF4X1odAxm72ctGVvi7o/+WGDctm2b2xNq+fLlzPNJTNeuXd2ZUNnZ2dChQwdLx0SiFEEQLbL9byBjRFCxc4BoRjlDQky4R2cVs4OPQFwNNpK+vWDBAiZELV68mHVYQSIiIlhZHgpR48ePN5yyTRAEQQQugVqGZiRmNEOIC/SYVe24q/Hiku3QOjrc0D40OgYz97OWjC1/+MEVFBS4y/HwdujQIY/HsRMyik+CENWjRw+fLjSSKEUQRNAQaIag/lrNsWuAaEY5w6CUNjDqheWWBHmBuhqsBRSe0EcAy/Pmz58Pp083pZ+fc845TIhC43IMXgiCIAgiEMrQ5Eru9caMZghxgR6zmnE8T5bXGNqHZp1TRvez1owtX/jBnTp1Cn799Ve3CJWfn+/xuNPpZA1oBBFq4MCBEBoaCv6CRCmCIIKKQDEENQIKL7wZQYHqU2CknMGqIC9QV4N52LlzJxOiPvzwQ9i7t6lrTWpqKkyZMoXdevXq5dcxEgRBEPbDn2VoPP6OaiX3emJGs4S4QI5ZfVVWqLQPjY7BLOsF3oytaWN6QlbPREv84Kqrq2HNmjVuEQr/v66uzv04Zj0NGjTILULhQmNUVBTYBRKlCIIIOuxuCGoEIbiSE6S0eCz5w6fALINwpe1YFeQFwmqwFk6cOAGff/45E6PQ1FLc2veqq65ipuW4iobd9AiCIAjCTmVoPP6OvCX3WmNGM4W4QI1ZheNutHzOyD7U0/imbUwETMjsyPa5WfuZN+7r1SHWtEXL+vp65vEpiFC//fYblJWVeb5fr17MmBxFqDFjxkBCgj1LQRESpQiCCErsaghqdb06Bn6zLkqDpxblW1bCpjdwMssgnGc7VgR5gW5KitTU1MBPP/3EhKiFCxdCVVVDS2YUns477zxWnod+UdHR0f4eKkEQBGFzhJjgwowkeGdVU5at1WVoPGITxgBaSu61xIxmC3GBGLPimC8dkAxv/LbHku1L7UOpGFQpOx7/vvGcFOjSJhoSYp2QFCcfCxqJb3njvsRYJ6zedUx3XLpv3z63LxT+K+6ALDSfETKhUIxKSUmBQIFEKYIgCJtmA2mtV8f2ur8+MAYiwhyAyS1ml7AZEZXMMgjXsh2zg7xANSXFLis5OTlMiMIOeuIgJiMjg2VETZ48GTp27OjXcRIEQRCBg1RMgGFFvWiCtKIMjdffsVVkuGUl94HiB2VFPCre9sLcAsXneJ8PYnAU8dHhUNLoK6W2D5ViUKns+A5xTrh2SFdITYxR/exGF0154kM0db//8xwoPFXF/R7Hjx9nnfGEbCi0WhATExMDo0aNcmdDYUwXqNntIS6MVgkPU7D4+HjWFjEuLo72DEH4GT0TqpWTsK+ygbzBlZVr3/pD9XnTx/WCe8b1Nn0scmKQsFeVRCU8HsOfWyYbHApizsqHshWPk1nbMYKwH+QCKDt138NOKx9//DETozZt2uSxknbdddexrKjMzEyfdlcJVIIhNsDU/hdeeAHWrVvHzo2vv/4aLrvsMvfjGA4+9thj8NZbb8HJkychKysLXnvtNU1eYsGwnwiCMBYT4H03Z6WaWh6lJx5C/565yz0v4qV4aVImTMjsZKuYzwysHhvvcZBCHDMhesswxdsRZ8fvLS6HT/7cD4Wn1D+7kfhWajsgk7Glth/Oz0iGiooKWLVqlTsbCudrsVSDRuRnn322OxsK/x87ItsZ3riAMqUIgvAZWsUiPROqPwMEs7KBjNSrv7hkB5yR1Iq9j1klbEa7zpllEG4Ho3G7m5KWl5fDggULmBD1888/M88BBIMWLMtDIWr8+PEQHh7u13ESvge9JgYMGAA333wzTJw4sdnjzz//PLz88svw/vvvQ7du3WDWrFnsXMGOPZGR9i1JJQjCt/DEBD/kFcLDFzVkuZi9UMjv2+iyvOTern5QVsajZvhnesdMSvtQSwyKsR9+9jlLtnN9djO7KivFhxU1dZKdBuvr66CmaDfc9s+voVftXli1cqXbVkEgPT3dLUKNHDlSUtjx52K8WZAoRRCET9AqFumZUH0xCcth5sRmNGgSv48ZJWxGxSCzDMLtYjRutyAUhaeVK1cyIQqNy0+fPu1+DLuroBB19dVXQ5s2bfwyPsIeXHDBBewmBa7EzpkzBx555BEmXiJ4PnXo0AG++eYbmDRpko9HSxCEEay8SNUSE5RUVJu+UMgbDw3rnghfrj9kasm93H61kx+U1fGoUTEPu0cLVhMCSvtQy/mGx0PLZzd7sVMqPqyvd8F176xp2J7LBbUnDkPlvlyo3JsDlfs3Qn1lKXvsQOM2OnXq5OELlZycHLDZelogUYogCMvRKhbpmVCNTMJmBG9WZ/EMSmnDPKOOl1WrPtfsbCGjYpBZBuF2Mhq3QxCK3gIffvghu+3Z02Q0isaWKERNmTJFU+kV0XLB86ewsJAFwQKYbo+lAatXr5YVpXBFV7yqi2n6BEH4F6svUnljgiX5hfC/VXtNXyjk9Xcc2qOtqb5PgXLx76uscjwOaBwuLpHjAbtHr9t3gvu9tcSgWj+7FYud3vHh+0tzoCz/V6hAEWpfDtSdOurx/BBnDER27QdTrrgYpk+9As444wxuWwV/LsabDYlSBEFYih6xSM+EqncSNivIsDKLRxgjjyBl5H2sEoPMMggPVKNxM0GvH8yGwhKr33//3X1/q1at4KqrrmJi1IgRIwLW6JLwDyhIIZgZJQb/Fh6TYvbs2fDEE09YPj6CIMA2F6m8McHXOYcsydbRYjIuV1IVHxUONzX6XpnZ7c97gRPxdVa1r7LK8XOgkfiLS7Zrfi3PewsLxjuKmrK/lcD9y/uZBCHNisXO0tJS5uEomJOLPT0ZoWHg7NQXolIyITI1EyKSekKIIxRu/NtQ6KNBJPRVRpyvIFGKIAhL0SMW6ZlQ9bzGzODNqiweuTGqsaOolJlQagmA5DLG1MQgIR0bs7ms7FITKN1uzKampob5Q6EQtXDhQndmCgpP5557LhOi0LA6Ojra30MlWhgzZ86E++67zyNTqkuXLn4dE0G0VHx1kcqzQNRGJbPbaLaOFn9HoaRq7rKd8O6qPXCyoobd0IPz07UHVBchefbrjK82weML8z2yhrDbGiL2EuJd9FTL4Fd6nDfORDNwo6Qm6os71MYotWCsBO5X3Ae4T3h46rvNEBXuYOeF0cVOjNHWrl3rFqEwu7i2ttbjOdEde0JY5/5MhHJ2TgNHeKThBdU/beCzaiYkShEEYSl6xCI9Ao/W15gdvFmRxaM0RjWw4wzeeAMgtYwxOTFInI496oXlsu9llkG43Y3GzQJ9B3Jzc5kQNW/ePDhy5IiH6eXUqVNZB72OHTv6dZxEcJCU1JAtUFRU5OFfgX9jh0Y5nE4nuxEE4X98dZGqtkCEf1fW1FmeraPF33FxfiG3+bWe/dogPHkaWUsZW/O8n1o8pvY4z0IigvvjjKRYD9Nv/KyFJRVMUEyIdbLyPKXFTa0LrTyxsJ7F2FkX9eVeRBVi1js+Wg+vTh6oebET4zNsACKIUL/88gvLjhLTvXt35geFJfFjxoyBdUW1ip2bH9OxoGoXn1WzIFGKIAhL0SMw8Qg8HeKcUO9ywYKcQ+y1mKWjRRQyO3izIotHbYw88AZAPBljUmKQlvcyyyDcbkbjZlJQUAAff/wxM5kWp3y3a9cOJk+ezMQoFAl4/QYIggfstofCFLahFkQozHpas2YN3HnnnbQTCSIA8OVFqlxMgNlBJ8proLy6TneMqMXnk8ff0egipJkX9WrvpxaP3TayG7z52x7VeA3jTRRd1BDGgaKdXHyHsfWsi9JY9pu3YKUWe4PGWFjvYiyOW7gewLHeNU9+EVXMtE82wNxrz1Rd7Dx48CCbHwUhyrusvW3btm4RCv9FUUrM+e1A94Jqncz3wU4+q2ZAohRBEJaiJ4OIaxWuth6ue7uhmwWC73HpgGQ2WfOIQlYEb2Zn8ZgRCPGYvPMGazj+7D4dYOjspZJp+Uom9GYLSHYwGjeLiooK1uEMhSgs08NuekhERARceumlTIgaP348hIc3lAIQhB5wJRfN8cXm5jk5OZCQkABdu3aFe++9F/79738zc3wUqWbNmsUy8bA0lGgZBENb8ZaMry9SvReIEmOdcP/nOYYyZnh8PrWep0YXIc2+qPd+P3GG0lOLtijGY2+t2MMdr00f14uVKKqNY+6yHTBnyQ5ZAQef8/d50gJXq8gwuGZQZ3hn1V5VEUgcC8sdQ72LsbjfxOcLinfz1x1S9WKtdwH7bK9ffyasfCjbPaYoVxWU7smF715/Gu5dsgS2bdvm8bqoqCgYOXKkW4QaMGCAqpenngXVHxW+D2aUHtoJEqUIgrA0ENWbQSRrThkdzlKivdOi8TkoSOFEtDC3QFUUsip4MzOLh/e9L8vsCN/kHFYNPP7z8zbWwU+ckv3HrmOagjXsmKLFJ8Ku3Wr8ffGFwtPKlSuZEDV//nyPrmXDhg1jQtTVV18NbdpI+3QRhFb++usvVkYgIHhB4bn23nvvwYMPPghlZWVw2223MUP94cOHw48//giRkYGxykoYw66/1QQ//mgGIl4gQh/LwlNN3TjV8I79eLK2EaWLdKl5nbc7nNxCIG9JmFZW7SyGE2VVTFDhEWLwvV0KA/COv1ITY7jG8a5Eh0ReTlfWwtur9kL/znFw9HS1x+fAePOGoSnQrV2Mx/FQ+q2pqm1YlDMCHqc3ftvDPKN4eezrHAgrcsLyZUtZRhR6RAkLhAgKToMHD2YiFN4wTtNTuq5lQfVHju9DMPmshriwMJJwgxcG2Aa5pKQE4uLiaM8QQY+vAlG97yMWDxJjnHD//FzFAKNNdDis+dc4Jp4oCQ643eHPLVMN3nDlxF8/6LxjfPD8PjD9M77VSTGYZl9dW8+VZv/SpEyYkNmJpUff82kO1/OdYQ7JCVXYm/5qVevPiy/MVPnwww/ZDTNVBFJSUphh+ZQpU1imCmEvKDag/RTMyF38+Pu3mtB/LEHmItXKY8kbH7SOCodnr+jnMQ4h3pETZ0JEpYFSj7kaH/c2FccM+vnrDjIPITU+uXWorGAgt1/tiBCvoUh47Vt/+Ox9x/ZpBxsOnPTY196xldpvzb3jeuvq5qcVl6seao7sgYq9OVC5NweqDuaDq9ZTUA1L6AxRqQ0d8lLSz4Knrh7is9/BOo7vg3CNIlV6aacFBd74iTKlCKIF44vWwUYziJqvwimvJmHA8tovO+Gecb1Vt2v3FQbeMcZHRejavpQJp1rWFm/2Fqbx/3N+ru1a1frynBfArJPPP/+cZUWtWrXKfX+rVq3gqquuYmLUiBEjVFO/CYIgzCbY2orbMTPWl/izGQhvfHDXmJ4sI0bcIZinxE5KkBIeQ6Qy6DFjRg2eDDIeX027gMcBz/n6ehcTALHToNznFqoPzGDp1qPN7hPHVvgb8vjCzYq/NZ+u3Q9JcU4oOlVluvhXc7KQCVDstn8j1Fc0ZagjbRLbw8ChI2BjfVdwpgyAsLhE92PF1aA5RjTyu/OnhpLTYPFZJVGKIFoo/ghEjfoA8XosYSrytOxequMOhE5uPGPEY2lFarl3q10t5QH4oN1a1frynMd2wD/99BMTohYsWABVVQ0rcCg8nXXOKMi6cCJccskEGJnWOeACB4IggodgayvuTUssS/TXRSpPmRsO4envt1hStqWHEFH3NqX9hfEDLgBiZvrx0io4VlYFr/6y2+djxR4n6IMECvHXibJqxSwb4bnITed0szQzSRxbbS04rVjeKfzWoBcWelzxGJUrUVdeApX7NkLlvgYhqrakyOPxkIgoiOzaDyJTMiEyZQB8PuMqeOCLjRAjsd+0xohGf3eOaPS9DQafVRKlCKKFEoiBKO8qHK4K8Y47EFYY1MaolFFlBkKrXS3ZW8VlfL4SShOv2avbvjjn0TgahSjsoHfkyBH3/enp6TB0/OWwITwDiiAWvioB+OqjjZAcvz2oL44IgrA3wdZW3N+ZsXbBHxepPLGIt6AiHIt7x/mnbB19j64c1Ame/C7fQzDBbJ3HL01n54eUwIAZSDxER4RydyJUQoh8bh3R0H0PZOIvLFfEznNqcSCKV5MGd4WubaMhISacq7xRL0JsNWepvOm6GPTCklyMjXPCtUO6sseLTzd4cYmpr6mEqgOboXJfLivLqzniJRo6wsDZ8QxWjodClDO5F4SEhrkFoxAIMSVGNON3p32QddbjgUQpgmihBGIgiqKEUiqy3nFjIIXbFgQQ/JdHAPFlSYBagGllanmbGKfm7C1MyzcyoVqxum3VOV9QUADz5s1jYtTGjRvd97dr1w4mT57MyvOKwpPg7x9vaJEXRwRB2JdgvfhpCWWJdkKIhzDjCQWmT/7c7yHyOGQyfIS73vt9D8RHhUMJR3xnJijiSJX44djv+Gg93D6yQQTyHjpPHIq8NeUsWL27GOYu32VonOL4amDXNpLxFy4gynXvE8AY+sZzUlmZnC+8m/SAvzUY7yotxuL59tavO2Hftk1QgZlQ+3Kh6tAWgLpaj22Ft0tlIlQUilBd0sEREdXs/UJMXkw143dniB+aFvgbEqUIooUSiIEo/oDflMWXaryjqNTtV4AoiUffbzwMjyzIUzRnDISSAHFGFXZ1mbu8qf27EaQmYLXsLSMTqlWr22ae8xUVFaws7/3334eff/7Z3aUlIiICLr30UiZEnX/++RAeHu42rAzWi6OW5NdCEMFGsF78BGI2eKAiFQ9hh9/p43pDamK0ZFaLNyfKPcUEXzF/3SHFx99c0VyQ4kH43gzt0RYcjhDdohQKdTdnpXpYUsjFX2rnvCCm8WYs+QOxXYT3Yiz2Ztu6dSssWbIEFi9eArlLlkJleanH60Pj2kFU6kBWjheZ0h9CY5Q7GLeOCoNnr+hvymKqmb87oQHge2s2JEoRRAslUAPRadk94d3f96gaM6IggzfsxoKIn4/p2v+ekAEX9k+G2d/nS66SFSgIIHYuCRAmcTxuX64/aErWlNwErJS9pXdCtXJ12+g5jwHRypUrmRA1f/581lFEANsDoxB19dVXQ0JCQou5OLKjOEsQBD/BevFjVWaslSJ8IAr8cvFQ0alKmLNkO4uHElt5ZlvbhVinA0qrlMUwPT3qvb83QuyhJx47hSLSkh1wRlIrjzlVKv4yWtmA424TEwG1tXVwqsp4yaEevH9rDh8+DEuXLmU3FKMOHfIUER2RsRDZtX9DSV5qJoS1ToYQNN7iIDLMAc9c3iBImXVdZObvzvkB4HtrJiRKEUQLJVADURzPsxP7sbRqHqTEq+Nl1fD3eeth3Ib2sGRLk++PN7hPZny5CVpFhsPQ7m3ZewdKSYD4+Or1mDIqTOqZUK0UcPSe87t27YIPP/yQleft2dMkYKakpMCUKVOYGNWrV6+gKpXlwc7iLEEQ/ATjxY8V2eBWivC+FPjNEr9446FrzuoCVhLjDIUyHSJKaZU55urxUWFQUlEr+70RYg/euFUtrhQfv0S0VwgBOHKqEtbvP2Hoc7ga42N/gcfx7M7R8O233zIBCm/5+fkezwmPcEJoct9GX6gBENGhO4Q4QnW9H5aaov/Wa46GWMWM6yKzf3fODwDfW7MIceHSL+EGV77j4+OhpKQE4uLiaM8QQU+gZjpIjdtKMBUdDRZr6uq5yuI+uXWoLbJelPZTm+hwNilLmXAK050ZwoKWAPjr9Qdh+ue5qtt8aVImTMjsZNk5f/LkSfj888+ZELVq1Sr382JjY+Gqq66CqVOnwogRI1g3PTUwJfzat/6w7Jzxx+q6UJIo9/0TBM2VD2UHRfBEsQHtp5ZAIGbqqP1GqWU98P5GyYnwZsyVVm7bypiPd26zklaRYXC6UjrbCT8XekYtzC2wNFbEWOrGc7qxUkWl781T326Gd1bt1f0+GCOUVFT7NPblRW+THVddDVQd3sa646EvVG3hdqira4pJMetp0KBBMG7cOBiTPRZmra6GonLzpAup3wEj3xGzf3eCAd74iUQpnTuOIIKJQA1Eq2vrYejspX5d2TFbNLHiWAjbLCypYPsqIdbJRDYhA2rusp3w7qo9Hsad/hAmMRD419d5XMfTqOgntZ9d9XXMHwrL89AvqqqqwfQShScMiFCIuuyyyyA6Olrze1kVpPhLVLZaaLMbFBvQfiICD0HsAZmsBzWxRzx3oieS3Nxk5DfclwK/meIXjvvFxdsMG3hbBfpZod0D7jNfxIohHPvPqIh3S1Yq/G/VXt0eV2ZmoaANhnh/Yme8ytp6VWsNl6seao7uYyIUGpRXHcgDV42nwThmnmPMNXbsWBgzZozbEsFKEdQ7VjESixv93Qk2eOMnKt8jCMIvrYPNYN2+E7YUpPQaxOsRGHgmTrXje8+4XnDn6B7w4eq9sO94OaQkRMOUYakQEaaeBWQWcsGyXPeYepeLfXa9Qbp4n+Tm5sID/3yaddArKipyPyctLY0JUddddx106qQvK8vKUll/ls8Fa0kiQRDBg5GyRC3Z2EbKyn3lOWim9YCvM9W1gqPH7nIoSiGv/bJTd6yIvkOYUa4Wm7g49p+aZ5EaX+cc0i0sob/q05dlMHFV7/sLxDpD4Y+ZY1kMLsSeGJNd9/YayefXlhyBCpYJ1ZANVV9e4vG4I7o1K8WLSh0Ab864CSaOzPR5POG9bSPXRcFYDu0LSJQiCCJgseMFr14fJj0Cg1lZMlLbeXvlHp9NnkrBshSY0YXBj5GMoIKCAiZCYXnexo0b3fe3a9cOJk+ezHyiBg4cyG2Y6esgxd/eZoHYvZMgiJaHHk8WLYskRmMSKwR+qcUqs8QvvfvGl4g/C5a7vbhEf7e5687uCoNSEuCuT9armp6r7T+lBSolQhozk44ZWIR1hjlgfEYy6wQot0DGOx7MPFu2tcgjblmQ02RAXldxCir3bWQCFApRtScKPD9PeCREdslwm5OHJ6awWAtjugnDB/glnjB72y3JC8osSJQiCCJgsdsFr1rWi1xWkx6BwawsGTuYVfO0MZZC6xgrKipYWR4KUT/99BPU1zeYnEZERMAll1zCsqLOP/98CA9v6NhoNmYGKf7u6Beo3TsJggj80n2taMl60LpIwhuTyO1rswV+ucWqCzOSNItf3mMelNJG977xB1h2+fxP2wxto2PrKMgvKOHuwrdqZ7Hi90lugUoJfOsJmR1Z6Z5eCk9VsWOJMci943o3s2zA+TqrR1v4Yr1ndzspqutcHrEXxlb7Nq6BE798wkSo6kIs6RTtsBAHODv2YdlQkakDwNnxDAgJbR5nqWWMG800Ax/HKoFaheIvSJQiCCJgwQApISYcjpcp17D7CqWsF6WspvioCE0Cg1lZMv7OtjGa8cYzRuzlsXLlSiZEoXE51rYLDB06lAlRV199tduzwGrMClL8XT4XqN07CYII7CYnViAWX4pPV2leJFG7sFXa1zh3mSXwKy0y8Rpso5iC+2Push3w7qq9HsKFt4+QUfCzxUeHQ2RYKBSeMn+u+nX7UUMlhjh9YbmbFsSNcOS+T8ICFa8n181Zqez5RkQpZHF+Idz3eY7HPkE7hJuyUmFadi/4YeNhLlEK/Teri3bBbfd/Bb3q9sGqlSvdHpwC4YldITKlIRMKs6IczmhFo/jZE/up/u6I4w49+DpWaSmiv1mQKEUQREAiBHlmCFLREaGSHeh4mTamBwzrnshmuOLSKmbGKJ581LKRMCDgDShQ0DArS8bf2TZmZLzJjXH7jp3w3H/fhEVffQZFh/a77+/atSsrzZsyZQr07t0b/I3eoMUO5XPkm0AQgYkdMmTtglF/JLULW559rUXg15txjeAmMONHSfw6UVYNg/69WNKw2mxBCnl2Yj93BvGS/EL4fN1B2W56YiLDHVBVo+z19E3OYUNjrDeYjqP0fcJjltWzHZcohfvHjCwhKVGrpKIG5izZAb3at4Knf9gq+Tpc3Ks9cbihHK+xS159VRl77EDjc9B3szQxDcK69GcZUWGx6iLqBRlJcP3QFBjavS23WCPEHTO+3OQhmPLQxtuc3UIRnkT/IBelfvvtN3jhhRdg3bp1zA/k66+/Zp2QxF+axx57DN566y3WzjsrKwtee+015uJPEETwwONpgKs/4/q251r10StI4RTaIc4JjpAQuO3Dv6BMtB3xKqhaNtICzsAJAwoMTNB004wsGX9n2wiYEWzhGPF3f/78+TDntbcgf8Na92MhEVGQ2G8k/POuW+GfUy9n3fTsgJGgxS7lc+SbQBCBhV0yZO2QfWCGP5LShS3vvsbOejyeg0YyrpVEFmHvXTogGe6a5xu/KO/PhotKeMvoFA/TP89Vfb3wWczoKhfrDIPSqiYhDE8no4IUz/eJJ/bBagCsCtDrR8U7xlkL8jw8q+rKTjDxiRmU782FutNHPV4X4oyByK79YMoVF8N9N14JRx0JcP07f3K9p9GMTHxdq8hwWWN1OWZd1BeS4qMsz1zSI/rXcfyuBXvmVUCJUmVlZTBgwAC4+eabYeLEic0ef/755+Hll19m7by7desGs2bNgvHjx0N+fj5ERtrLe4YgCH3w+D2wlOvLMuDCfsmwatcxxQlfb/AhBAUnymvg5WVN6doCBY2Tz73jeqlmI2EgEOMMhbIqZXFMCG7+31XyRpBasmTskG2DGAm2MI28cs96+O/Db8E1S38UpZCHsLTxmIxsiO41DEIjIuG1bQD98z3NOQM1U8FO5XPkm0AQgYNdMmT9nX1gxDsKhYJZF6dDUpzyhSHvvr7/8xy4alAX+PWBMR4dzazIuJaKe7As74lL0uHpH7ZYLkgJpWhy+w1FA16z7XvH9oL3Vu+VzOrSwhVndoKuCdGQEOuE46VVmkv2lFD6PvHEPlgNMOqF5e5znGUJfbXJ8Gf2HuPREyVQdSCvsUteLtQc9cqqCg0DZ6c0iMJyvJQBEJHUE0IcoXDj34bCGT3awrec3l2XZXaE/7s603BsgtlVWhcz8dyy+jdNr0fsEyq/ay0h8yqgRKkLLriA3aTALKk5c+bAI488AhMmTGD3oYdIhw4d4JtvvoFJkyb5eLQEQfjLFBuDrbs/2QDhocoX7vi33tUw9EHAoEApawk3jZ4MPKgJUsL22Gd3AXeWjNLKipFsG7NXbGRLweKcUFlbDyXlNR5jrD6yG0rzlkFZ/i9QX3YSljbeH9U+BZxpYyAmbTSEtUq0ZQaAWZkKVD5HEIRWeDNff8hr6Jhl9Wo871xidsmhngYbwqieuVzd/0bLvsYyM7y1jg5n5WwTMjtpnjN4M65BIu7BhbFHv82z1J+T9wL6RFkV9+JUdV0d86MCMDbu91fvc48RS8qsQLBf8IbH+Fx8jiNmCFKuulqoKtjeWI6XA1WHtwHUe8ahER16NJqTZ4Kzcxo4wiMV4kO+YLpzmyhVr1Oe3wMti5m+bLyiVfTn+V1DWkK5dUCJUkrs2bMHCgsLYdy4ce774uPj4eyzz4bVq1fLilK4qi42ZxOb4BIEYT+0lJKppcVjNxpe80+BG4alwPi0JGYWyYPWmnceVu8+BrMuSmNp9kpZMhgEqa2sTBrcRbJdslK2jVUrNnKlYPg5cOKtKz3BRKjSvKUeq3hxbRLgxinXw8DsCfDY7+WstbCdMwDMzFSg8jmCILTAm/n6wep97GblajzvXGJFyaGesnStHjRas4xRbLjjo/XwutdFJs+cgcISZnCdKPNcwOHFSkEKy6ZuzOqmemzwfLhr3gbu8R8+WWmqQTpe5Bs1E1ezX5A6d/C+7D4dYOjspZK+XcL+mPnVJt0LqZi8UVO8r8kX6kAeuKorPJ4T1jqpyZy8az8IjY6X355XfIi+qjz+WMx/VQatsSWPoOfrzHEtthi8v2sulytgy61bpCiFghSCmVFi8G/hMSlmz54NTzzxhOXjIwjCnAwbLUGecGEvd+GOf2sVpS5onBiLTnt2GlHCzPp/obsLTtS3jewGC3MLJD0o1FZWpF7LE3xbbZDrXQqGrYZLNv8GiavehL9W/YL1eo1PDIOEPsPg7ttvhofvuA7Cw8NhQc4hCFmd43ePLLPen/d5VD5HEIRVHn5WrcZrmUusKDnkjSVQUEls5eSOV8QxTmKMk5X4FZ3S5pf4+MLNHheZvHPB5ZmdmPhhdszhDZ4/Vw3qLGld4E3bmAguQUpPOWWnNnylfrwIF/khJnlKiVETD7BsU81IHu0itFB76qiHOTn6RIlxRMU1ZEI1ZkOFt+bPEsOsPvwsAkN7tGX3KWVx4eP4PCn0xpbi+B6N8r/OOeQhsGIsO2lwV1bV4N2EyAq02GLw/q4pYZfFVjMIGlFKLzNnzoT77rvPI1OqS5cufh0TQQSzqajRDBshoOZNuxeCOakLdy3BuTj997uN2jq6WBEc4ud/87c98MrkgdAmxulxfJDhzy1T7MLzxm97ZLc9fVxvmJbdU9Jk0czVau9zC808MTArOlUBhdtz4a/FXzPjcnEGa3rmWTD8golw+RVXwbiBPTzexy4eWWa9v7/HSRBE8KHVw8+K1Xitc4kVTTl45n8UVKYMS4WIMIfuGAcvxIXPxBsLFJ6q8rjIRHGLh3FpSTC4W4KhboI8oCF6j/axXM+dkNmRS5B6b9UeTWNuEx0O5/RIhFc4snO0gMcIOxSajZp4YMZiWX1lKVTu39hoUJ4LtccPejweEuYEZ5d0lg2F3lDh7VMhJERf4xcUn/CzCAu8OP6bzukGLy7ZLvsaLE0VnwtCDFhYUsF8vPTGlkJ8j7d/XZTmHs/e4jL45M/9HmOy2odJiy2G1msJOy+2mkHQiFJJSQ1qbVFRESQnN51o+HdmZqbs65xOJ7sRREtBi8hkZpmWWRk2QkCNKe5GL+xxW1gG9/d5ytvyTv81SyzA3f6P7F4wZ2nz8jkecF/iRI4liuJjiKtBRtpbf7p2PxOlvDFztVrq3KorKYTTm5ZC2eblUHuyKcO1a9euMGXKFLjhhhugd+/etu9Ip0agjJMgiOCEp+zFytV43rnkj13HwOEIgR1Fp7m2q2Vu5hHnsCRObDKtJ8ZBP0SxD6XWi0zc7uML8xWfK54z8HPhxTuKPGYadovBBbF7x8nPxWLE2TS8sQAPsyf2c5tdWyHA3ZKVCl+uP2S6BYOceKAnrnTVVkPVoa1QsQ875OVAdeHOpmxyJMQBEcm9GkWoAeDs2BdCwsLBLDAzCa0smomwLheUVNR6eIM+fmm6x3dIy3HX8vsjCFS4/TlLdvjch0lLExozFx7bB8Eipm5Rav/+/bBv3z4oLy+Hdu3aQXp6ul/FHey2h8LU0qVL3SIUrq6vWbMG7rzzTr+NiyDshBaRycwyLbMzbPB9X508EKZ9skGxxbHahf33GwvgkQV5qu/nXcqG28RVOq2p1N7g2Aenaiul8EZqojayYqI0+Zu1Wi0+t+qryqBsywoo27wMqg42Bd4hEVEQ0zsLYvplw2szboAL+3sav9q9I10wjJOwBrvFT0TLRFz2gqbm6B/lq9V43u2gbyKvKIAlV5htqwWtJtNyMQ/GONgRTSnGiQxzwMMX9oWnv+cTijDLQy4OA5U5A//Fkrm3V+7RFVtgNz61UjJcvEKxoehUlez28fF6l4uV1kstguLn411gFIiLDIMrB3WG+KgI9reWRUotYNZZq8gwmLNUvUTRDPEA9010RCiUV8s3vXG56qG6aDczJq/cm8tiJletp5VEWELnhg55eOuSAY5Ivow2PUjZX/AIrzzntZHfDSs86LTA24SGd4HS5XLJfs+CaRFTkyi1d+9eeO211+DTTz+FgwcPsp0kEBERASNGjIDbbrsNrrjiCnA49KUDKlFaWgo7d+70MDfPycmBhIQEtpJ+7733wr///W/o1asXE6lmzZoFHTt2hMsuu8z0sRBEoKFFZDL7B90KPwhcPcXg5KRoNUbLhf3s7/MVS9juGdsLureLkQyk8P8nDuyk2Y9KiuKyKk2lFDwTtRkrJt7bxHOimNNHS+n9cTuPf7MJynetZd3zKnauYat9DYSwQComIxuiew0DR0Qk2ydPLdoK4zPUSwACqSNdoIyTMAd/x08EIQX+puL8ln+4hGsH7S0uNyVDOzGWT4TVkqWCX6nXftkF94zrpWlsTSbTSyTNvnlinrnLdihejLsaS/LSkuOYxxSPOTc2IBFK/5ToIJGFoierXAAvkp+f2B+mvPunasw2fVwvlo0i190YO+de9/Yaj20L85sg5GkBt3uqspb5ZuFN2B5aDiiVjWkFDeMLTla4u/KZgVpX5J/yCiQFqZqThQ2eUHjbvxHqKzwbcoXGJrg9ofBfcddhq9Dqu4WCinCdgd8hrd5hWmNbK645tMLThIZ3gRJpCYuY3KLUP/7xD3j//fdh/PjxTPgZMmQIE3yioqLg+PHjkJeXBytWrIBHH32UGYe/++67MHjwYFMH+9dff8GYMWPcfwteUFOnToX33nsPHnzwQSgrK2OB3cmTJ2H48OHw448/QmRk4Ke0EYQRtIpMZv+gm+0HobbKgoEcpnbLXdh/v/GwoiCFfP7XAVYWh0hNKriKZoYohdvEfaillEJqG0aMbNW2yZtmrbZik5ubC8++/Aas/ewTqC876b4/vG1XlhEVkza6WUClJ3gIlI50gTJOwhh2iJ8IwozSKaG0W8tvlNR7YAYNztNY2mamfc+7v+/hHp9YGMAFF6Xuc0rzEG7nXc5YABehHr+UXyjiyTr5v6szIatnouwcc3NWqqaOcmhpcLxCOUtKIDUxRjJ2EUoVvccvXgTdVliqqZwRkVtURW9NPKdQ+DMDPBfum58LZuJS6IqMQmVpVcO+qCsvcZuTV6A5eUmRx3Ywixw74wld8sLbdpHtOGwFITp8t8TXGa0iwzXHuVqzgXg7Mlrtw8TThIZ3gfK1FrCIyS1KxcTEwO7du6Ft2+Y7t3379pCdnc1ujz32GBOCDhw4YHpQNXr0aI/VRW/wS/nkk0+yG0EQTWgVmcwWkcw0dubp0OIMc8j6GODreUr2cH/g6uenaw9Iljvi9nlS3HknWW+BAoNkHj8IXNHznqi1GtkqjYs3zVpuxQa7n86bNw8++OADJkqJu77EpI2CmIyxENGhh2pQpTV48FdHOq2NAahzXvBjh/iJIMwoodG6QCD3HuJSFDM7xQnmy3o8DXmQmofw/XgzuoRFqNevPxP+OX8jlFY1z/TWSnGpshCDcYUWUapNTENZnJbPI45d0JT9fiboKGedlWv47A6ZrBxhexgrPXpxuqo/qD9By4f6+oaSVPFHqa+uhD056xrNyXOg5shuzxc6wsDZqU9jl7xMcCb3gpBQ31hCh4eGQLgjBMprmnyqMD7sm9wKlm09qmlbwnUGep5qQWs2EH63n/puc0D5MPEsUJ7fAhYxuc/q2bNnc2/0/PPP1zsegiAsgPeCftXOYrcgovUHXeli3ExjZzWBTapzjffrlVZEvdPnm21btNL37wkZuoIguUlWLFBU19Yz7wm19OgnL0mXnJTkVl/wOGDnHDQqBZVUYC0tmsUrNhUVFbBw4UImRP30009QV1fnLlM6Z8x42ByTCVHdz4SQUH7DTbsED0qY2RiACB4ofiLshpbfdit9XTCjJjIs1COzoXVUuCFzaS2ehlqRmod49wd+rvr6Jn8lnBce+GKjjlGoj8lox+KL+3fUFLOJYxcUHZQyVXja3IuJCA2B6jqX6vZQTHt18pkw7ZP13GVlvgQ9SHFBtL6+DqoLdrjNydGoHOo9BbrwdqksCyoKRagu6eCIiPLLmGvqXOwW6wyDq8/qzESRE2XVBsU/bQdHSzaQlkVUu/kwhXIspAb7IqYmqTU/Px/S0hpqG+X46KOP4Prrrzc6LoIgTIT3gn7u8p2qK1Ny2TRKF+NqmTv4N0542B5VTf03msVlNF1XvNKH5X23j+wmWwqIz7ttZDdYmFugOeV23b4TXIFVW4VjiwEEpko3rEy5YFj3RBjaoy3btwO7tlFNBeYRAJFZF/WFqeekwh+rf4fbbnsMPv/8cygpafIoGTp0KOucd80110B86zYw/Lll3KWFdgwepDCzMQARfFD8RNgJ3t92q31dMLPp41vOZB6RwoIWGmOLfYjMHJ9eMU5pHuLdH7X1LrjunabP1SoyVOMo+MdktGOxkWYcZpdEKQlSYtCs/4KMZHj5mkyY9mkO2AWs8Kk9dpCJUEewLG/fRnBVe3qzhca1g6jUgY3ZUAMgNKY12AnM6MNsu7NS2hju6IhxKHY1VIoBsQJg1sXprKyRNxuI97sdbD5MLVaUGjRoEDz11FNw//33Nyu1KCoqgltvvRWWL19OohRB2Aw9HkNKgpT4B533Ylyty81Log4n3maYHgapMXwGqXKBIu/rlRCXO868MA0GdG7NVsDEGVjiz/Dg+X01p9xqEd+kstSkfAswEBDGxJMKzDMGNOFcMm8ZPHPjN6xESQCbT0yZMoXdzjjjDI/X8JYWBkrw4O9OL4T9ofiJsBN6hAOtCwS874E+SxMyO3n8nurxROQZnx4xTm0e4o2vvEv1TlfWaRqD1LblxuQdE+Dco5ZFJGUpIOdjgwtR2P1Oqquev7KasXsk3uKjwuGS/kmwdu8J0zymtFJ7+liDL1RjNlRd6XGPx7EjXmTX/g3m5KmZENY62We+UOJzSWvprHecq/V98dzBhVE1wfOZy+U9YY1+t9F24+nLM2iRMNBFKcyCuvPOO2HBggXMWLxHjx7u+++55x7W1njDhg1WjZUgCJ0Y8RjyzpgSZ9NovRjH12BNvVrqryBoSWUZYV2+UttcpcAUBbTHF+aDWQiCUJsYJ1vVOV5axSa8pPgojyBNT8otb3cibBuNmUfifYQGslIGot5Codq45ILL+qoyKNu6EsrylrKWxJ823h8bGwtXXnkly4oaNWqUbBcxuWBX6VyzM3bo9ELYG4qf/O/fRjShVTjQs0Cg10tST7zCOz49YpzaPGQkvuIBO9y9+/veZnM6lj5KoZS5PvfagfD3eRu495/U4hWWbz21SD4z3ow290YoqaiBbzcWsjgIu/KVVFSzLB8rjo04Jqrcn9coQuVCzbH9nk8IDYfIzuluESqifTcIcRjLlNMLnjfPTuzH/l+rr5oRQUp8flnRfZj3u/3IRX1tH1O2VDSJUtiqGNsW33777TBgwAB4/PHHWceYxYsXs44y06dP92kHAIIg+FHLVJIDRQJcFUts5WwW+Gu9GMeLCAxm1BACB6myOKzLV0MqMDXiI6HUHttbEBKCMyMXRw3imbJRI24dgy4p3yu5jjZas3bEwSX6IFTu2QCleUuhYucacNU2GryHhMC4seNg6tQb4PLLL2emzjxIBbuDUtqwskVfXmSacWFrdmMAIvig+MlcyL/N2G8elshp8W7Sc7FoxEtSzRNRT0m8MGfzcFlmR+jcJprFLUO7N5S8KyE3XixD0nshL3zeXu1jJed07FzoXRrOk7n+ukYxQLx4hdv3Nur23j5uw0ibe7PAfTZnyXY2JjzHpDpAnq6qhbIq/ow1AVddDVQd3sayoJgvVMF2AFe9xyeNSO7Z0CEvZQA4O/UFR7jyQiPuB1xwLZNZcDWLqPBQd/wnxGBY+ohZZmbhfd5LnV9mG3fzfrdx0ZiwJyEupXZ2Clx33XXwySefsAuQ33//Hfr1a1BdA51Tp05BfHw880OJi4vz93AIwtKL8B1Fp2Hu8l2qr3lpUqZHer0Apm7fw1G7L7wevY2ufesPsBJM254z6UyPiQ0/s7d4ZBS5dtbCu+rxEMJxYsc/KaFJbgxaWyoLfHLrUHegqSTMvPblUpjx3Fwozf8F6stOul+PbYhjM8bCS49MgynjBkFLvrDlPa/F+5xoubFBsMZPvoqh5C68jfz2thR4us4JIgFm6KQmxqheLCrNH8KxAhlxQu1YyW1bz2KCnoUprfOB97jQ8Hv6Z9r9jYRP8srkM5tlJcn577SPbeh6p2Qy3jYmAlbPHMv2ldb9pxZHCSIjem0K1g5q86uwAGdlmV1y45gQ4TOjgPHJn/sV95UYl6seao7uZQJUxd5cqDqYB64azzGHtenYZE7etR+ERrXSPNaYCAeUVYvFLWvwjkW0xOZYDYDZckpC868PjPHpAiOeRzyeaTiErU9dABFh0ln8hH/jAs09JU+cOAF33XUXK+GbMWMGfPbZZ3DttdeyLktnnnmm0XETBGEx3l1SeEQpuTR83pUJ4fW+yBTBtO21e5fB45dqN+zWAnbHM9NDSCgt5A2SEL2ClPhYSAWObR0V0OX4OshdvhD2bm/KbHNExUFM2iiIyRgLyd37wr8v6wcX9g/Miz8zjcnN7C5JBC8UPxmH/Nv0wyvKaO12pSQ8GC3TkSsx11oSr9fgXOt84D2ulzgXmLwR9g/6NqnFLpiRwit8HSurhqGzl1ji2SNkxv++sxhG9G7HnQlTWWutCINjem/VHrgxqxs7NnjOYgaV2rlQW1LEBChWkrcvF+rLm5q3II7o1hCZOoCJUPhvWFx73WOMacyQ0iJIKTUjUsM7FsfjgpljauIgfrdnXZTGsuWUsuBQ9DG6AMcrPAvfbR5wf6FYRouD9kSTKPXdd98xM3M0sF23bh306dMHHn74YfjnP/8Jw4YNgwcffBAee+wxCAvTrHURBOEH/wwjF9PCxK6E9+t9ZYCJwo44kLRCDJPztBIHZy8u3gZZPdupHisrSgvVwGMhfl8sxyvfsYb5RO3bsx7WC6nojjBI6DsULph4DeSFdIOTjTHL8fIatoKLtlGBlpVg9oWtkU5FRMuA4idzIP82ffCIMph5+8q1Z7o7tKqhpcmJmWU6etC7MGWkUQVPjCTH/7tyAGT1SmTZ6GaDIpZwfLQcF944asr//mRdibEJjJJ46Mu4BzvGvb1yD7OiwP+Xes+6ilOsM57gC1V7ssDj8ZDwSIjskuH2hQpPTGFZQ1je+UNeocERqu8FIba4OSsVOrWOMtQFT8rH7fFL01WzjQQh+TWHuX5QRrLYtX63yUYhiDylUHTCDCnBwBbTz1977TWYOHEi/O1vf4Nvv/0WcnLs04qTIIJBgFIzltSL3otp3lVHl9fr9XQBNIIQSPqrGwxmoeFN6VjpXcE1yrHTlfDv77dAxcHNUJa3jBmXu6rK3I9HJJ8Bsf3GQnSf4RAaFQcrGy2kjGYVBeuFrRXGnUTwQPGTOZB/mz54Ltww89bhCDHcfl1KyNHT7MNMjFyI6pkPjM7r2JEQWZxfBFYx46tNzbKzlWIVLXGU4AeKwpQU/oh7MF4Rm7zX11SxRi1CJlR1IVYNiEYU4gBnxz4sC4r5QnU8A0JCG8zlnaEhcOfonjA4NQHeX71X95hCGpv34CKfGvi8ZyY2ZLg9+e1m3e8nLBRLdWhEvzE8L7yz8PG9Zze+N2Kl0Kw1i13rd9tf1wOEyaLU2rVroX///pKPnXvuubBp0yZmdk4QhLWeD2YKAnoupnlXJtCTQvx6QQTjqf02ijiQRANtI6nORvE+VuJgoPh0lemlhWrUnCyEW++bCcUbFkPtyaYVvtBW7SAmYwzEpmdDeNvOlq4iB+OFrR0yAgh7QvGTOejt6NbSOwqa/Ztn54w1qX1oxvmgZT4wahmA4/1+YwF8t9EzW8cs8Pg0CA813HEl7kfMDDpeJrFCJcFbK/bAvePOgKiIUFP3D5a63TqiO7yHHQk5jfoRbNZSXbSrwZwchaiDWwDqPF8fnti1wZwcs6G6ZIDDGS25rdtHdofP/joAc5bqK89EhG/12d0T4Ic8dfHx2iFd2THBawTsJqgXjMEX5xfKZiKte+Rc+GPXMVi9u5iNUs7s3wqhWU8Wu5bvNn5GslEIElFKTpASQPOqd955x+iYCKLFoiWd2UxBQOvFNG9whiap3tRzKkNCjb1RcKxYQ+4vQcr7WNXXYyq5tg6IZrUsxmwozIqqOrjZIyU9+owsZlru7JoBISHaDCD9efFhxwtbf2cEEPaE4idzCGb/Nis7Cpr9m2fXjDW5fYhlW0aztLXMB3o/t3D+4kLa0NlLwdcoxZX4/9iVkFcQwZhryDOL4YUrBzQ7f5fkayt3E7ohnt0tARwhISyTbOo5KfDS0p3yn8XlgtoThxvMyfflQNW+jSwOEhPaKrFRhGrIhgqLVf/diHGGwsscXqxisKteXGS4R1aasPCbd+gUAKiLUtjdXot/kjfoGYUleghPJhKWj/oaPWI3bwUGnslko2BvyPyJIGyCnnRmMwUBLRfTegNc/IwPfLmR67WCIGWkw5wwhrdX7OIOHLL7tIdFGwtMTysXjtXf51mfJeZ+z/o6qNyzAUo3L4OKHX8w36gGQthKYExGNkT3GgaOCN+uIvubYL6wJYhgJlj928xsvOCL3zw7Zqwp7cO75m2A20Z2c5eVaUHPfKDnc4vPX1xI481IMhuluBKFKi1ZOqcr65qdvxgHfq3BKwvP2/+7OpNl9zz45UZF0aKu7AQrxavAbKi9uVB3+qjH4yHOGIhM6Q9RKShCZUJYQicm9mhBT8969AgbnyG98NsqMhzmLpcX1wTwWOjNMJs+rjdMy+7J/h87KGr107Qqg9MMsVtpTpArPyTsCYlSBGETjKQz+1oQ4C2H+31XscfkNXfZDiir4s9+wldFhjk0pYx7B5KZXVrDsq2egYnSxH3z8G5wcf9ky9sUW0n1kT0NPlH5v7AgTSC8bRfWOS8mbTSExSUqtvbVSiCVywTrhS1BtASCzb/NFx0Fzf7N85Wwr7UDl9I+XJhbAK2jwuBkRS33++udD3BeVcM7hhKfv0YNzvEinI3DwIJeYUmF7HHXEqu6ROcvgp3w0GydF8xy+ymvwMMPSqC+qhwqD25uLMnLhZqjXoJZaBg4O6VBFJbjpQyAiKSeEOJoKCeUEzCMNLmRQ6kpTEl5NaAupiR24fHEErqFuYc1va93piV23NaaiWRlBqdZYrfcnNA6KhxuykqFadm9KJ4LAEiUIgibYERYMioIaF0FWbvnOFc53H+X7YQP/9gHz07sxwKSdzXWweNb6BWG8LWTBneBj/7Yyx14PP39Fnhl+U64Kasb/PZgNrz2yy54UWf3HCvBwBqjmJLyGvdnQ/GpLP9XKM1bCjVHmlaDHVFxEJM2CmLSsxsCspAQ9wWDXGtfLQRqVlGwXdgSREsimPzbfOXPZOZvni+EfaWLYe9jj7YAPPtQKx3inMzLp6q2nl3Q85xjGE+hCKHGy9cMhLatnJLnr9GY7unL+jERxEh3u1kL8mD/8QqWYSOMS68vKO77uct2wqdr92s+Dv/6Og9KKhtELFddLVQVbHf7QlUd3gZQLxaJQiCiQ/cGTyg0J++cBo7wpn0pjn18aaMgl/GI5zhm8akdI8zywX1/vLSKu9zxmsFdm52v4vJBnusRqzM4zRS7g2lOaKmQKEUQNkFvurdRQUBtFUQsWCXGOpkg9eaK3dzbx9I7DGAwC0mLKaUZvLhkByvJ0wKOEYWod3/fA89clmG4fNAKIsND4dGL0+HOD/6A8h1roAzL83avw3q9hic4wiC65xBWnhfVfZC7Y4z3BYNca19eAj2riIIYgghcrDLa9fVFDe+CFF5QojBiZGxm/uZZKewrXQxjPOE9L2NGhFlMG9MTerSLgVU7i2HxliMsjvD2p2oTIy0macl6LzpdCRf0T5bc93oykgTOTWsPF/Zv2PdYsoiG4+JFRHy7yHAHlFc3xgsylFbVsVjorRW74eqzOrPzxkisqWeBD32hjuzfwUrxmDn5gTxwVXtmcIW1TmoyJ+/aD0Kj4yW35R37YDkdZm09tWiLrs+j6XNIZDxW19bDv77epChI4bGae22T8IPZ7TyM6t2u2W8jfqee+o6vax+e177I4DRb7CZPz8CGRCmCsAm8Zn1mCgJqqyAY0GDKuxmrSSjy+AM9qdYIBrxS6eL+BoO0vfkb4I2NH8Cx7xdA2Wk0yWwgIvkMiEWfqL4jIDQqTvL13hcM3hcpe4vL4JM/93tkqOF5eemA5GbnQjBkFVEQQxCEr8tU9CxI4QWluOxJ79jM/M2zQthXuxhGvBeKzFzwCg91wBPf5UsuRjX4QnrGBd7HgVdkRDHk7ZV7JI+h+OJca6YT+lGh4LFsaxG8+dueZq9HgUpNkBJTWlXLfKTwhmbZlbX8r9VD7amj7nI8NCivLzvp8Thmf2MWFLulZkJ464ayQDW84xXcxzdmdWPHwIgBPi/ijMeSimqWAaZWxojHqo1IiEqKj+J6L+/n8TZREi90+6vDJmWxt1xCXHiFw8GTTz6p6w1Gjx4NI0eOhEDh1KlTEB8fDyUlJaybIEH4EmHiQNS+mEaDZQz80PDQ113g9GCkvCxYqDlZ2OATtXk51J5sahMd2qodxGSMgdj0bAhv21n29dPG9ICsnu24yw+kLjL8kUVAEIEeG7SU+CmQYyi5izbh183sMhWpuVjrhbEvxuYPMBvs2rf+MHWbwsU2lvkVna6SfU68jsxo7+OgZfxKxxDPCyx5e3fVHs2iG2bU1LtctsvylqKushSq9m+EisZsqNrjnn5aIWFOcHZJZ9lQ6A0V3j5VtUtwQ0ZbGhN01OIVLXG3GdyclcqsLHjf66VJmTAhsxP7/+83FsC0T9Yr2mfgZ1/5ULb7s/LG+t7nIvqa3fNpjqbxmQnFm8EDb1zAnSm1Z4++LIfMzExdryMIf+KvH0O5FQKelHFfGqvrJSYi1N1VTwstRZCKCHOwFU4BbF9ctnUVlOUthaqDTWnXIeGREH1GFivPw3R1pQBNCMann3sG9/kit5JOWUUEoZ1Ai59eeeUVeOGFF6CwsBAGDBgA//3vf2HIkCEQrPijTEVLyYrc/OeLsfkDsxu3CHsFs30/++ug7HP0xhnCccDMl40HMasnhLuJiNwxlMrawxLFc3okwPd5Rapj8lfnPh6wC3DloS3ubKjqwp1N1gNIiAMikntBVGNJnrNjHwgJUy7PxAwu7NBXXFrF4mNsxoMZYzzxMsbdr0w+Ex5ZkOeT/fZNzmFN5xraZqDQid0H1TofhkhUT/DG+njOPn15hlsc9XeHTYo3Wx7cotS7775r7UgIooWn8Pva58bXHfsQPYJUSwJFu6rqGhasoWF5xY4/WADXQAhLV4/pNxZiew8DEJl3BrLnE62GEcFOIMVPn332Gdx3333w+uuvw9lnnw1z5syB8ePHw7Zt26B9+/YQjPirTIVnQQovFI8pXCj7Ymy+BkvIjYDijTizCBdlUJCSKmVzvyY6HG48J9XDP0oLuF0UNF79Zbeu14qPoVzWXklFDZcgZTdcrnqoLtrd4Am1N5ctsDXFNQ2EJXRu6JCHty4Z4IiM1fQej1+aDlk9E9n/4/4b9cJy7hieeS0tyvcQpGKcoRDmCIESDZ0a1cAIrE1MuCbhC2PC+z/P4Wr40+A/NbDZZ+SN9R+5qC+79hB86xJjnJAUFwlFp/hMxymWI4xCnlIEIcLXnSak8NUPu1WrG8GK1SWE1Uf2wO68ZQC7VsKp40c9grXYfmMhJm0MhMclahqD3T2f/C0AEwThyX/+8x+49dZb4aabbmJ/ozi1aNEi+N///gczZswIyt3Fe9Fm9UKO1IJUYUkFTP881+9j81Usg6+Zt2a/ofd95bozwRES4n5fzJpBkUJt7uzcJhr8CY6Xx08LdyEar9g1gxxdYWpPFjaKUJgNtRHqK097PCc0NsHtCYX/RsQlcnV0lmLupCYhRmsML/f88qqGBdR7x/aC937fa5pn2eWZneAdDV2ocSGXdzG3wX/KqTvW33+8vFmZH4q1Qjafkuk4xXKEGZAoRRA2SeFHfPnDrtVYvSWDgclnfx3wNPluNPwsKa/Rvf/qyk5AWf6vUJq3DGqONK2wxrdJgH4jL4Ci9kOhqg36J4S4RaYLM5K4ghrsIDT93N62zZCygwBMEEQT1dXVsG7dOpg5c6b7PofDAePGjYPVq1dL7qqqqip2E3tHBNqijb/LVJRKVjBrIRAXmfTGMnOX7ZD1fFJDyNwY2r2txzmB+1CtfOlEeQ08/i1fZzKtYKe7ypp6rmPIU2qlV7yxkrqyk6wUr8GcPBfqSjwzuqJiYgGS0yEqZQA40ReqbRd3XGP0M7Vt5dQVw/M8H+O+Zy7PgLvmbdAU50kdc/QrizOxS6QUq3YebfbbqBbrhzSKT1JZghjfCmMX+5OJFzwpliPMgkQpgrBJCr+vf9iVfCwIT05V1jDjSO+LIazx17r/MG29fMcaKNu8DCp2r2vyUnCEQVTPwRCbMRaiug+C2oRWMEfCqBPHwCNKYSq7XQUpOwjABEF4UlxcDHV1ddChQweP+/HvrVu3Su6u2bNnwxNPPOGTXWnVog3PRZu4TMWX2HlsZscy+Dq95XNKpeq8WWSnK80r1RLDI0jhIhcew+82HuY2y/4m55Bq9zYrwL3rhBo4sXsjy4TCDnk1R7x88xxh4OzUh2VBYVne2/dfA6FhYc2+v3iojIpsmE2oJYb/Y9cxyOqVyP18zD7Cc3bGl5u4M6akjjkKPHh+owBkZDFTibnLd0n+Nip51gGHb11kmAM+/tvZbs8ucfMbiuUIsyBRiiBskMJv1g+71lVkeR+LcE3BTqCKWnGRYXCKIxBFc8n4qAhITYz22K94PO4d11u1Ow6ms1cd2tLQPW/rCnBVNXlmRCSfAbEZ2RDddwSERsV5BPB3zWsI4MWdTQLxIsVuAjBBEOaAWVXoQSXOlOrSpUtALdqoGY3705fPzmMzM5YRXscLem2JvXnEmRvecRB649ida4d0ZfuDN+MN99+ZXdrA3Z9tYKV8VuOqr4Pqgh2sJK9ibw7UFmyDulrPmCe8fbcmc/LO6eCIaPos//5hO1vYE5enFp+ugqcWbTE8NuE84I3NMa569op+UMEhFiKFpyqZt9J1Z3eFV35pEn20Ipz/vsL7t1Eq1sfvzaTBXeHFJdsVx42eVlgS691lj2I5wkxIlCIIG6Twm/HDrncVWcrHQvBgUEsjj48Mg5uHd4Ne7WPh7/M2gJ0Id4RAjcoSHApSvAKceNJObjROXZhboLiPak4WQtnm5UyMqj1Z4L4/tFU7iMkYA7HpY1gaO2gI4APtIsXOHi4EQTSRmJgIoaGhUFTkWXqDfyclJUnuKqfTyW5W4ovVeKWLNn973Nl5bGbFMlq6AeOc/ciFfeFEeTUTp5Lio9zd1p78djPrbuYhWMVFWpqdYganGhe1eBedsLPf3Z9qKykTuGdsL/jcy47AO2OJ+UIdO8iyoJgv1P5N4Kou99hOaFw7iEod2OANlTIAQmNay76n+JgLx31BziEwg4RYp6bYHBcQ7/hoPcQ6Q7me/9R3m03LSMNdjGVw08f1hk/+3M8EL6vw/m2Ua6LEm50njscE4feHvALNryUIOUiUIggbZKAYvUg3uoos1XpVED7kgp6L+yfDS5MGui8Cbtp7HN79fR/YBTVBSq/xpBBgvfGbdJv3+qoyKNu6ipXnVR3Ic98fEh4J0WdkQUxGNkR27QchIQ7V95EL4I1cpNihQ4qdPFwIgmggIiICBg0aBEuXLoXLLruM3VdfX8/+njZtmt92k69W433V+TbYxmZGLKPlohUFgvvm53osEN33eY7sOSLXPcwoZmaIYwwyuFuCaqkV/j0+vQM8siBP83sLMew/xvZiN/G5dKKsCm5/Y7HbEwqFqLrS4x6vbxXfGvoPGQ5bHClMhAprnezhC6WG9zE2a34/XlrFBC61bnHelDaamatu34ISScy6XzUjm3moSZWsmnVuef82SsX6WuMxqQVw3tcShKmi1KuvvgpfffUVJCQkwO233w5jx4718CMYMmQI7N6tvSUqQfgbf2agGLlIt2oVGYOj20Z2kxVfFm0sYMKUECijUWgggi1/R/VOhF+3FxtKbccgDg3LK3asFrU7DmHBGwpR0b2HgSMiStf2hWDOW1D69YExbHWY9yLFLh1SgqEEkSCCMX7CUrypU6fCWWedxcYzZ84cKCsrc3fjC/bMSqmLNrtg57EZjWX0XrQqLRB5x0GYLYV/nSg3xzuKVzSIjwpjcYYajy/c7M5okVp0Qv0HS/Xe07H4JxXDpiWGwdHN6+CTJUtgyZIlcHCLZyldSFgExKVmwMSLz4e7rp8I/foPgJEv/AqtdGb3eB9jnjgAP7PS+iJ+FHEJoNAtzu7gvsDjcM+43nBGUivJBUYUW99sPLelPtMtWakQFREGc5fvNPTbqCUek1sAl4NiOcIyUerll19m/gEYnJSUlMCFF14Ijz/+uLtTCxpk7ttnn0wJggiUNHlhUlBaeUiWuUg3exVZED4wrXj+uoOKz5351SYWSGG9eaDyxXrlz6hE9dG9DT5Rm5ezTnoCYQmdIbbfWIhJGw1hce1MCWCUBCXvOn8p7NQhhVcAFjon2TkzgCCCKX665ppr4OjRo/Doo49CYWEhZGZmwo8//tjM/NyXUGZl4KB3wQH/bu3V4ctMcCy4cPbhzUPgns9yPMr7rAQNyeOjpDubeYNxlBCniTPjluQXskwqI4bguG+fuLg3RB3bDrNmvcpEqLVr17LfHQHMeuqR1h96DzwHzh4+GkaOyIIRfTq651yci/WUm+GrO8Q5od7lYhlN4rlcLQ64dUQ3RWHGe58I3eKiI0KhvJovE8oIl2V2ZOWivEid/0pZkAO7tlFcSMRjwiNKKf2GaonH5BbA5T6r8FqK2wjTRak33ngD3nrrLZg8eTL7+84772Qp3hUVFfDkk09q2RRB2Baz0uS1lEnh/bgqorTih49LvV7LKrLamFC44BWZhCCvpYHiU1n+b1CatxRqjjRlNTii4iCm70iWFRWR1Is7rT3WGQalVfKrqBiAYGq9VEtiXkHJjh1S1ARgZPhzy/ye1UUQZhBI8ROW6vmzXM8byqwMHOzueXi8vBqeuTyDeQr5guiIMKip41eTxKIP7iM89+/8eJ2u93a56qHm6F6WxV20NxeufjYfqioaOtUJ9O7dG3oNHAY7QlOhPLEP1ES1gs24n45HwpB6h8dx0pOJKJwDlbX1cN3bayTncrWFYClhRq5rnxDPOMMcPhGlRvVuB2twEVdGhAXO818uC1LtesSs30ae44ACmJaSPTt63hFBJErt2bMHzjnnHPff+P/Lli2DcePGQU1NDdx7771WjJEgAi5NXmuZFIoGaJqtBD7+4Pl9m01mvKvIe4vLIevZpR6CE7YhfvzSdDYmHLOvArVAA8vxynf+CWV5S6Fi9zqs12t4wBEGUT0HQ2x6NkT1OAtCQrE8gB/c/5cM6AhvrZAXI7FEEtPTjQhKvvJk0dP9USrgWpxfaJusLoIwA4qfglfoIIxnnOMcYFWWlBicY3COG9unHSzdelT3dnhKyxCeLBZvU+2ocId7H/2x65im/VJbUsS64zFz8n25UF9xyuPx9u3bs2s2vGH5cP6pcPdcG6oy1+opsYxvzH7z/gze21cSXrwfV+vaJyyYogk+GsJbWc6HJvvCb5NVIo3S9YhZv40Yu2GH6QfP78M8uoQGAuLjwCtK3jAsBS7ISKbMdsJaUQo7sxw4cABSU1Pd92VkZDBhKjs7Gw4f5k9hJIhgBH/Y5y7bKdleVemCmqfzjJxowLNSgoGB5JhOVTEh6tXJA+Ff3zSZchMN3WeqDm1lQlTZ1hXgqipz75aI5N4Qiz5RfUdCaFSc7t316MXp8NQi5TbYX64/qGi0ySMo+cKTRa9flXfAZcesLoIwCsVPLacDHaE949zq7lzevjhGBSlxaZmZogfO9eI4cfVuZa/LuopTULlvI1SyLnm5Hl1+2VjDI1ljlciUTIhMHQBfzLwGzumZ2PDaehdc89wy7rkW9x2KFbylj/hc7IIMnNtXWhATP87btQ9L695dtddUQ3qp8wnHpuS/imC3vWnZPS2JWYz+NirFbuLx8oqSKEjZ3fuOCAJRavjw4cykc8SIER73p6Wlse4sY8aMMXt8BBEwqJW+KV1QGxEN1FZK8O/q2sbMHhke+CIXyqqVn9NSqDlZyDyi0CtKHOCFtkqEmPQxTIwKb9vF8PugSWWbmAhVMZK384vSOWS1J4uZflW+yuoiCF9C8VPL6UCnhh06oNot49zK7lxSvjhGEF/sD+jcGqZ9ssGQ35MUQpzYNPoG6muqoOpgfqMIlQPVRWghIHrzEAc4O/ZhAlRkaiY4k3t7ZHAfLa3SPNe+t2oP3JjVjR3Pf0/IgL/P48uoVxOv1OZy/J5gpliDMNdwLg3t3pb7XMH9h98tb8GlTXS4IesJ7wwktUoHfP6na/czUcpuv41aYjcqoyZsJUrNmDED1q2Trm1OT09nGVNffvmlWWMjiICBtyOF3CRsVDRQWim5+qzO8NJS5RTyli5I1VeVQ9nWlVC2eRlUHcjzWGWMPuMciMkYy1YbQ0Ic7H4zDFnHpSXpMg2VQ+kcsjKYMDuzyZedtgjCV1D81HI60Clhlw6odkNtjhLQk/VixBdHzLQxPSGrZ6LHxX6bGDTwBlMRx4lDUltD1eFtrBSPCVEHtwDUecYe4YkpTIDCTr+RXTLA4YzmihN451AslXt75R62Dy/snwy3H1TOCtKK1DjwezLjq00ecRaWQmLs9cxl/bjjGTxOchYB3t9DXrwzkOyykKb1t1Fr7EZl1IStRKn+/fuz2/79+6FLly7NjHxRmIqL01/KQhCBiNIPO+8kbIZoILdS8uLibRpG1nJw1dexlcbSvGVQsWM1841qIAQiU/ozISq69zBwRES5X4MB0SvXnsn+/7p3mkw7tSAcSzQvR/8IM8zQceVP6dywMpgwOyCjTltEMELxE2GnDqh2g2eOwhKpBTkFXIs5sc5QuOasLmzxR48vjtScPf3c3s3mSLMXR9A2oPbEYRab3HfbXNi6/g84efKkx3Mwa1sox0MhKixWfTFJKobUkp0mPkdnXpgGkeGhqoudvHiPQ8nfFEUqzNS6fWRD6SRPPCMl1ojj5Z83F8B7q/eBSyaID2ksRXzkor7NfJYCeSFNT+xGZdSEbUQpgW7dukFBQQEzzBNz/Phx9pi4xShBBDs8flBqk7BZooH0Som9ygJCQwA0NKMxhQszOsD3eUXs/6uP7mWleWX5v0Bd6XH3c8ISOkNsv7EQkzYawuLayQZEjkZvBZ5VXZA5EthJUaqbnpwZemWN8m9qTV09LMw9DElx8inbVgUTZgdklCJOBDMUP7VMyCtPHZ45amSv9lwLQqVVdTC4W0KzeEhPmaDLq1RLvPCXGOsEo9SVnoCKRk8ozIiqO93gd/VH4+MxreKgPjkdolCIQhEqoRN3d1+lGBJNwHkRZ85k9+kAQ1LbQnzUPiipMFYG5y2U4f5FGww1sFzulckDWRaX3nhGiJfxNjg1Af4+b4PkGJGnL8+Q3aa/FtKMlgHrjd2CpYyaCBJRCpV8qR/E0tJSiIy0ri6cIOyIltUPpYwnq0QDnHC1doCxEl8LUsjSDTvgVM5yVp5XXbTLfb8jKg5i+o6EmIxsiEjqxRXo4fFWEhGViG9MPUdzc97XYHCtlCUlPGf6ZzmqpSBWBBNmB2RaBdqW4s9CBAcUPwUfPL9BdinxMRMrfnvV5qjiMmnPTimkysYHpbTRZNaNTB/Xy92huFl8FudkGdQl5TXcczraBVQe3NzQIW9vDtQU7/N8QmgYRHZKg3/dejWMP+9cOPPMM2HJ1qPw+MJ8XSX/UjEkHju1Jity5+jQ2Uu4vS7VtuctlOFxl/NlFYPjwNLJlQ9lNztXECzT1HJeXti/I7zuCNEVf/tjIc2MMmAjsVugl1ETQSBK3XfffexfvHCbNWsWREc31S1jdtSaNWsgMzPT/FEShI3RuvqhlPFkhWiAxpDOMAdUqZidBxtYjle+80/WPa9i9zqs12t4wBEGUT0HQ2x6NkT1OMvDBJQHXBnFgAf3573jesEnf+7nCqIQDFx3HCnVlFmnJkiBxlIQs4MJKwIyXoGW/FmIQIHip+DAW4hhZdhe2RpSF4eBWuIjh5W/vUpzlJZ4y9ukWxizFkEK6do2Bl5ash1eXLKj2WPiuV9ugcpVVwtVBdsaRahc9v9QL85+DoGIDt0bfaEywdm5LzjCI2HctUNhiKhsCmPD33cWwx0frYOyav6KlIkDO0F8VAQ7d4VYUk+Gv4CaIBUTEco1vpuzUpudK1rOf2GBUHyuGDkv9cbfvvZaMqsMWGvsRguAhK1EqQ0bNrhX+jZt2gQRERHux/D/BwwYAP/85z/NHyVB2Bj8weY1vkZPBLXJQjzJmjUJhIfqF6Xws2Gx/ckKbeKIP8DfpqpDW6Fs81Io37IC6qvK3I9FJPdmnfOi+4yA0Oh4XdtH76b7P8/xCEQ7tHLClWd2hmhnKHRqHQmv/7pbsbPLu7+bZxAKJpmLG8GqgEwtQCR/FiKQoPgp8JG64JVC6uLQSFaCVReDerfrz99eLSVnYpNuLJlHDyI9idpPfruZq1sbJlqjLxHGIZj91FCOlwOVB/LAVV3h8dyw1smNnlCZrImKVEziLdAI5txaBCnklV92sZtYnLFS/MRFUJ4xNnQX1C86SnlRGT0v9S7a+cprycwyYC2xGy0AErYTpZYvX87+vemmm+Cll14iU3OCaAwUeASpkMY6+AfP78sd+JmxEolBp9ZsGzGRYQ6YNLgrzFnafJXQLtSWFDHDcizPqz1R4GEIGpM+hmVFhSd2Mfw+UoFp0ekq+GL9Qa7X46RvtGsf7/v4shTEqoBMLkAkfxYi0KD4qWV02JW7ONSbUWrVxaB0KVokXDukK6QmRsuKVL787fUWzbD0TmvJGYKf0Ui3OB5BqvbUESZCoTeU43AelJ485vF4WHQ8RHTt7+6SF966uSCjJLpoOf/kEIszZvsbiTleXgMJMeGKGVXJMtnTvKKj9+vtEBP4wmvJ7DJgntiNFgAJW3tKjR07FsLCdL2UIIIKYSLkQctkYeYkYHRFDLOCas3ueWwC6MtQvm0lE6OqDuS57w8Jj4ToM86BmPRstgIZ4gg1/F4sW8xEQal1VDicNGAQyovUsbdq1d2X5pfB6M9CtAwofmoZHXa9f4P0ZJRadTEou91TlfDiku2K4tcfu44Z+u1Vmn/Ej+0tLm8sjW96L61eUEZQ84qsqyyFqn0bGwzK9+VC7fFDHo9HRUXBqFGjYNy4cew7fxASYdqnudzv7V02pfX8AxVx5tcHxli6Pwd2aQ1LtzYYtkuR0SlOUvDkFR2lvKjsEBNY7bVkRRmwUuxmB7GPaDnoUpamT58Od9xxB1x66aVw/fXXw/jx4yE01PiFH0EEGnrq8tUmC7MnATO6w/BbeVuLq76O+TKgEFWxYzXzjWogBCJT+jPD8uje54AjIsqU98N0/Huye7HOLDwdf3i5KStV0p9CDbXVR570dj2r7rxClq/ML4PNn4VoOVD8FHgY8d8R/wZpySi16mJQi8DhLX7h/DHjy026f3uV5h9ErTTSV4IUgoLNMdH7YaxReWhLgy/UvhyoLtzV5FOJhDggIrlXQ4e81Ez47JEbYFRaR/fnfppz8VIsUCLoX7lq51Hd55+cOPPh6r0wIbMjvLtqL/e4tESBGw6UKD6+OP8IfL+xAMZnNIkhxaeruD7n9HG9dXtRBXpMYFWnP7nYzS5iH9Ey0CVKFRQUwI8//giffPIJXH311czw/KqrroLrrrsOzjnnHPNHSRA2Rc8EpzZZ6JkEFEUDE/SkepcLWkWGwelK//hKVR/dC2VYnpf/C9SVHnffH5bQmflExaSPhrC49qa/7yvXDmRdWRbkeK6C6kVYAZ2W3Qt6tY+FaZ9sAJ4kNOF1uLq5bt8JKCypYF4ZmOrOWwqid9Xdjl4C/mrBTBBGofgp8DByIev9G8SbUWrVxaAWgU0sftXXu+CueRu4wwktfj93fLQe7MCsi/pCYisnG/uB4tNwz2sL3B3yqg7lixbBGghv28VdjodZ2Q5njPuxk9UurrK7WGeYh72CIFAiw59bZpoY5Q3GDzFO/mQCl+bFM3UB8cEvN8KT323mbhQjgOWlLTUm8HWnv5Yi9hEBLEph6d7FF1/MbuXl5fD111/DvHnzYMyYMdC5c2fYtaup5bo/eOWVV+CFF16AwsJCZr7+3//+F4YMGeLXMRHBiZYJjney0DoJSIkGWB6G2TgofmhpoSzHq7/sBl9TV3YSyvJ/ZT5R1UVNvymOyFYQkzYSYjLGQkRSL9YN1GzaxkTA05dnsIsHXKncUVRqeJvCKGddlMYuDKrqXHD5wE7w5fpD3CunEWEO9wVIVEQodymI3lV3u3oJ+KMFM0GYgd3jJ8KcC1ml3yCejFKrLga1Pl8Qvx5ZkMclTEh9brX5x9/gmDvEOWFYu1p4/4uvYeWvy2HDHyuhotQz2yc0NqFBgGoUosJaJSqeMzxZaShIYQMV7JA3Li2J7Tf0KTXqH8VDWZU2w3ReLs/sBO9wZGDhZ9fjd4oZVeJOglpiAhRXcZHRSnsBK/F1p7+WIvYR9sCwMRRmSWH53okTJ2Dfvn2wZcsW8CefffYZa738+uuvw9lnnw1z5sxh49u2bRu0b29+JgXRslGbCPVMFlomATnRAP2KsDzszRW7YWSvdhAo4Epk+c4/oSxvKVTsXteUHu8Ig6geZ0Fsxlj2b0hog8eTVTxyUV/TVyoxIMIOQOiZoGWbcmbhWkpB9Gbf2dVLwNeBGUFYgd3iJ8LYPG/mb5BVF4N6Lx61lI1r9fsxA63lZcLCF/pB4e1kYR6kP3zYc5sRURDZaE6OZXlhbTurLoKJRTnez41G6v9btRcGNwp5ZvhH+YtbslKZuMYjSulF6KgojnPUYgL8u6KmzsOGwd8Z33rxVac/hBYAiYAQpYQVvo8//hiWLl0KXbp0gWuvvRa++OIL8Cf/+c9/4NZbb2UdAhEUpxYtWgT/+9//YMaMGX4dGxF8KE2EYrRMFryTAHaiGfXCcsXgBVfCfsgrBDuD7ZOrDm2Fss1LoXzLCqivKnM/hh4NKERF9xkh2TLZKvYfr4A5S7YbCgyT4pzwf1dnQnFpFbsQOFFWxVX+cMWZHSEpPood52HdE2Foo0muFLylIHpW3c0qH7HSWN1XgRlBmIld4yfC2Dxv5m+QVReDWgU2LWCG9rNX9NPt92PEXxH3xVkpreHbjfLxTn11JVQd3MzK8dCgvOaIV0c+Rxg4O/VhWVBRqZkQkdxbU6MUbzESy+y1gHNZK2e4IQEPp9axfdvDkvwj7G9fi1txURHsHNPqf6kVqWxtuZgAG9Wg8OfdqMbfGd+B0FiGFgAJ24tSkyZNgu+++46t8qGn1KxZs2DYsGHgb6qrq2HdunUwc+ZM930Oh4N1v1i9erXka6qqqthN4NSpUz4ZKxE8yE2EOCljGrOQks07WfBOAugtZPXqo5XUlhQxw3Isz6s9UeC+P7RVIsSkj4HY9GwIT+zi83G1iQ5jXX+MBnOPX5oOWT0T3cIMZl3xbPPL9U2rtVjap3Zxw1MKomfV3YzyEav9qHzZ8Y8gzMCu8ROhjNw8j79nWJLdJibC1N8gqy4GtQpsWnjlujPdc55VpT3e/oriff7dxsMeohQ2Rqku2A4VzJw8ly1+Qb1nuVj//gPgaFxvqEvOAGfndHBE8I8Vd73YE9JbjNRizC4s8qzeXcz9GsntuIAJUreN7AYLcwtMiRFxn8dHhcHJCvVSu0/X7odp2T3h3xMy4O/zNoBVyGVre8cE2Ojn/s9zuLehtohm1SKbHnzVWIYWAAlbi1LYae/zzz+3Xde94uJiqKurgw4dOnjcj39v3bpV8jWzZ8+GJ554wkcjJIIVsy+OeSYBs8y3fUl9VTmUb1vJxKiqA3nu+0PCIyH6jHMgJj2bGYZqWZ00mylnp8LLy3ca2gauzOH5YLR8wayVPD2r7kbLR3zlR+WrwIwgzMCu8RNhPxHcqotBue3qRZg/hnZva2l2lpy/okC7WCdUF+9vKMlDIWr/JnBVl3s8JzSuPbTudSa8fN8NMG7cWNh1OhSufesPXeOYe+2ZimJkgq7Ox8bOJWH/frHuEKx8KBvmrdnHyt2M8uwV/WGRl+gnhZA9jQ1ibj94Et74zSsbzUTksrXxGAjlk9i5UMlIXbyNkopqxUU0OzZ98RW0AEjYVpTClPNgAbOq0INKnCmFqfQE4e+LY6VJAFdr0OwxEMDVSgwQSzcvg4rtf4CrVhh3CESm9GOG5dG9zwFHRBTYgQ/+2Gd4G5gmLg6U9JYvmOXdpGfV3Uj5iJ39qAjCnwRT/NQS8bUIbtXFoHi7P+QVwAer9c97LpWsLR6/H+//l0JKjDt06BArgV2yZAm7YXdLMdgYJTKlwRcqEn2hWicxX6huZw+FtokJ8MGGbZo/L68omBSnPUMMz60v1x80LOAdK6uGrOeWsowlI4KgWHSpqq1XFaXE8c7MC9NgQOc2zCRfnDWG20R/p5LyGlMy9bzjKynxSI0l+YXM10tuEQ0zz978bY/tmr6oYWZmFy0AErb1lMJJ4MUXX3Qbc/bt2xfuvfdeVirnLxITE9nKY1FRkcf9+HdSUlPWghin08luBGFHpCYBPROuP6g+uhfKsDwv/xeoKz3uvj8soTPEZmRDTPpoCIvzT/MB77R7b5N4MDlQMlK+oLf1t9FVdyPlI1a1MyeIYMCO8RNhX6y6GBRv14gohd3jsvt0gFU7ihvLzxq2i5lT4rIqpfkHafZYnBOuHdIVUhNj3BfUpadPwYIFC5gAhd8j7+YAYRFOCOuY5u6SF9G+m2TmNXa4u+/zHO44atZFfSGxlVPThT0+D4WpwlN874FiDe4zs8or0dMJS+huHdEN3l6xR1YQxMxusd8Sdh+ekNmRiZbiz6one/rC/skwPqO5qCp0GDSjhFT8fnIZ2mp8nXNIsTvkWyv2mLrI5osyQLtmdtmpBJIIAlHq1VdfhXvuuQeuvPJK9i/yxx9/wIUXXsgCrbvuugv8QUREBAwaNIhNVJdddhm7r76+nv09bdo0v4yJCHz0/oBa8cOrd8L1FdjNpmzLr0yMqi7a5bFaGZM2kpXnMfNQlQ42ckRHhEIIuKCsurErn05evmYgPPptnqVGnOJAyYzyBT3ZVt7nIAZNePHw4eq9sO94OaQkRMOUYamsFMLM8hGr2pkTRKBj1/iJsC9WX8QZnZ/QRHrgUz+zxioCc5fvZGLHsxObjM+b+f3EONkVvdAMBH2i1u457iFsDewUC2v/XAPff7kE7luyBP78808W1wtgLHHWWWcxQRdvjqQz4MYPpD2ExGBWDA9CRvCNWd10+XehqPbiku1cz3/4gr7sNWaXV76zcg/8bUQ3+G5jgeQ8zpuJpzd7WkpUVTIlR4GMR6zyfj+lDG0l4iLDVGNBuUVMPYtsvhCLfGWfECxCGWEPQlzY+kojnTt3Zp3svIWeV155BZ555hmWTusvPvvsM5g6dSq88cYbMGTIEJgzZw7zb0BPKW+vKSmwfC8+Ph5KSkogLi7OJ2Mm7IvUD2hCTARLicYVIF/+8Apm2XbLkHLVVkP5zj+hLG8pVOxeh/V6DQ84QiGqx2CWFYX/hoSG+3uoLENq7rUDoU2MU7OPhJhYZxiUVdUqBmbo5yAO7IwKih//7WxwhIRwX5hInYMY8CHiVVGe81LrRdHqXce49u8ntw6lTCkiIDArNrBz/GQGFEMF5kWcMD8hZi96vS5xASw3P7nq6+Hovh2N5uQ5UH1wM9TXeFoV9O7d2y1CjR49Gtq0aaMpTlLKlBYjzHBGLuDR+/OeT9VFMiG2fObyDPd7iefdvcXl3OKWHK9Oboh99IibwlgwwwkFPbnsaa37Siq2wPfgFeTE5xZv3GEVL03KhAmZnRSfIxcHmnGu8X4H5GJUq/HFZycCOy7QJUrFxsZCTk4O9OzZ0+P+HTt2wMCBA6G0tBT8ydy5c+GFF16AwsJCyMzMhJdffhnOPvtsrtdSQEXwigi3j+zGauZ99cPr7wlXDP5sVB/eCqV5S6F8ywqorypzPxaR3ItlRMX0HQmh0fFgJ16dfCYTE7UEilLclJUK7zWutGoJzPSWXsZEhLJsJlyR5rkw0SKAWREQCEGR2oqqr4MigtCLWbGB3eMno1AMZR5mxhI8CwvSi3DhhjOKk71+670/V83JwiZz8n25UF/h2QXbEd0aRo4eAzdceQmMHTsWunbtqktg01oqZob4pzVuC1GJH2Z8tcljUcnIceBF6rzwFvbMFkq9Bbl3V+1pZq3gnYlnNK4zitoim6/EIjsuCtpVKCPsFRfoKt+79NJL4euvv4YHHnjA436s87744ovB3+AKJJXrEUbgSQPGriIDOrdmXUZ4Xme09nzRpsNcz78gowN0aBUJ76/eZ/qKZ21JEZRuXs7K82pPNI0nNLYtxGSMaSjPS1QOGP0B7mk0qhSy24y2qD5dUaOrrE1cvoA+E8dLq9jq6P7jFTCncRVU6piVVdexG08attYUdiuMx61qZ04QgY7d4yfCHpgZS/BmW0mZqg9KaQOjXlhuqPRcXNqEn+uRT1dD6da1jSJUDtSe9DTPxm682IUXjckjUwdARGIKVLaOghum8l2wKpWdZ3ZpDT/kqZt1TxvTE6af21vXHCUWVBJjndAmOgxOlNdyv17uuArH54/dx+CLvw7A1zl8MaGAHh9HOWFUEKRuyUqFcV7eU2YgLvnDMZRIeH2iUbo4BjIa1ymBHw1TOLQ2ffGH16Yd7RPIZ5TgQZcolZaWBk8//TT88ssvMGzYMLcnwqpVq+D+++9nmUkC//jHP/S8BUH4FbUfUAHsKjI+I9k9GZv9w6sns2bFjmMQ6ww1TZCqryqH8m0roTRvGVQdyHPfHxLuZF3zsHseBpBSZqJ2wdUoIg7s2oYFL0Y9NL5YfwjGpXVgqzpavT7kTGvPSIrVdKzlLkx4z12rjcetamdOEIEMxU8ED1pjCblMKK3eMlLzk1Hj7fqaSvjp55/gm725sGDRj7Bt8ybPLTlCwdnxDLc5uRN9J73K/bXOT94C297iMvjkz/1cghSS1TNRl8iiVDZvxlyMY8KxoQ+XVlFKqxChtsCFe+f7vEL410XWLTBpEWfN8O70RvhUaBaP3fd4F9mkvo++Eov0GNJbjR2FMiJIRKl33nmH1XDn5+ezm0Dr1q3ZY2IDQhKliECE94cR09rFwYOZP7x6PYhKq2rZzQiu+jqWRo/leRXb/wBXreDpEAKRKf0gJn0sRJ9xDjgioiCQuPfTHJh9RT3riIPddO6at0F3oC0EQmaKOMIq6F0fr+fqAigEsC8u3s4CVS2Bjy8CAqvamRNEoELxU/BgpQG5llhCLhNq1kVp8NQi49lWcgsM2KXtWFl1823X10F14U4WQ6A3VNWhfHiizjMmCU9MYQIUu3VOB4czmuuzakEQ2HD/zFmyg7ucnSfrRUvMhhk9WlH7rHoFBS2vs0N2i9YxKGVo64nzxItouKjJs8gm932cNLiLT8QivYb0VmJHoYwIElFqz5495o+EIGwUZO4oOq0reDDrh1dvFxGjVB/dxwzLy/J/gbrS4+77wxI6M8PymPTREBbXHgKVytp6mP5Zg+cATtpY0rcw17MjDW/wIhWMGb1IweeimTmPICUGOx3hrSHw0V8+aUVAYFU7c4IIRCh+Cg6sNiDn/S1Gvx0s/ZbKhPr7vAZfJTNEBfxM4s6tXdpEQ+/2sfCPzzYwj6Pa44dYKV7Fvlyo2rfRw2cSCW2VCNljx0LWyDHw9q4YCI1tMie3cn7SE0uJs15453S1jB6kTXQ41NTVQ6moS6Hez6o1K0iPEGHGIqvRmEjrGJQytFEUenHJDtVt4YJlYitns/HyLLIpZSbie2PWHIqUVopFdrRPsKNQRgSJKCVG8EnX2+KdIOyCXhNqcfBg1g+vnhIsvdSVnYSyLb8yn6jqol3u+x2RrSAmbWSDTxSm09v8O57Voy2s2nWM+/l4jDAd+5XJZ0KbmIgmQ83f93AbiYoDpu83HmblnGJTWK0XKRjArdp5FPSCnwkvUJQCHwiggMDqVugE4U8ofgpMfNFunSeW6BDnZCVpSkKImZnb4viorvQEVOzLgcq9uUyMqjtd7Dk+ZwxEpvSHKOYLlQnhbTrC6dZRMPPuMfCjRo8qI/OT1lgKF6qEY6dFeOR5H2xU8uHNQ+Cez3LguESGmZbPqiQ8SG1TjxBhdJHVDOFWzxjkxCPk07UHVOPzG7O6ye4npUU2nlJD8XtZKRbZzT7BjkIZEUSiFKagv/jii6xjDNKrVy+499574W9/+5uZ4yMIn6C3VA4nWDQCxW4XwuSHKfN3zZNPH8bVmu82Hla8yLa6rtpVWwPlO9dA2eZlULF7HUB948qdIxSiegxmWVFR3QdDSBi/F4K/iXFq+zkTggQsb0BvKGxDLLXizBMIzf4+n3lWeVOg4SJFrygq9ZmEc40n68uuAYGvWqEThK+h+ClwsaKZiZ6LOPx7eM9E5m9oFLULf/wtvv2dlVBxIM9tTl5TvN9rwGEQ2TmtwZw8ZQBEJPVs5jOJv+Xr9p3Q5FFldH7SGkth5vSD5/dl8YAW4REbl/BQXFYNz1yeIdsdUMtnlRMevDvj6RUijCyyysXUOM47PlrPDNKz+3ZgO6C4rEo2HtY7BjnxyEphhKfUEBc8p4/rDZ+u3W+5WGQ3+wS7CWVEkIhSjz76KPznP/+Bu+++2210vnr1apg+fTrs378fnnzySbPHSbRAtGRJGMmo0Fsqh1u/dEAy60zj3UL5byNS4buNhR73xzeaXYrTh7Hz2mWZHd0mjcKYrSijwlX56sNbmWF5+dYVUF/Z1Ho8IqkXxGB5Xt+REBodD4HI4NQE+Dm/SNNrhPIF9HHScg6IA6HvNxZIClLi91C7SNErisq9H67ITh/Xi60KShmuijPB7BgQ+CITgSD8AcVPgY0vfXbkLuKEWMKoIKUkKtTU1MCaNWvg58WLYc77X8LpA1ubFq8aXx3RoXuDJ1RKJjg79wVHuHrcgjHahMxOkp/LrPlJHA8Wnxb8MPlQiwfkhEfspMsDPu+WEd1NuziX65iI4p9RIUJvdgtPTP3Oqr3sJkZq0cnsDBsrhRFeATQ1MVpXk5xgsE+wm1BGBIEo9dprr8Fbb70F1157rUeb4/79+zOhikQpwpdZEkYzKvSWykVFhEqKEVi+9daKvaxbB3owCGVhUlk4mML9v1V72Q3FrH9PyIAL+3dkP9Kto8I1eQuhwCWVEl5bUgSlm5ez8rzaE03dWkJj20JMxpiG8rxE/T5EdgCD2annpMJLS3foMnnHTDfec0AcCCFYsqeG0kWKVf5hqYkxkoEPYueAwFeZCAThDyh+Cmx83UWqeRc56VhCDbULely0ysvLgyVLlrDbr7/+CmVlnr5QYa2TITJ1QGM2VH8IjYrT/HmEBTelEisj85NUPOidOaTG7zuLNQuPGH/xIDzPzItzKeFBLtbQ+n56RBy9MbXcopPZQpJVwoiWUkO7iUW+pCV/dsICUQpXUM4666xm9w8aNAhqa411/SIILVkSZmRU6A0ey6uVzSrfWrEHBnZpAxf37wjDn1umGkSimPX3eRvg9oMnYeaFaXBTViqXKeMFGR3ghmHd4PDJCrh/fi67r76qHMq3rYLSzcugaj+2X24gJNwJ0b3PgZiMsRDZtV+z9PpAJaRxonv+iv6q5q5SuDSE+BgIXX1WF8g7VAI/5RXKekPwnmdW+YclxjhlJ387BwR26PhDEFZB8VNgY2YXKV6RQPgdx+fzxBLemVBo3PzUoi3NLujvPCsODv/5A1w3eyksXboUioo8M40TExOhz5nnQL4jhZXkhbdO4nxnvqwss+cnuXhQiyCFvPe7ZwYPz5yeFM/XiVj8PLWLczM9FY0s3ioJiGLrCmF8emNqpUUns4UkK4QRMvMmCD+IUlOmTGGrfVjCJ+bNN9+E6667zuCQiJaMliwJxIyMCitbkM5akAfxUeGaRAfMvhrQuQ1My+4F7/6+V9F0GzOE5k4exPwP/v3dJqjYs55lRJVvXw2uWiGdPAQiU/pBTPpYiO49jKv1cqCB5WoYrFzYPxkyV8RDzoESTa/HrDQerjyzEyzZUsQysrQid55Z5R+GAuXjl9qrLM+OmQgE4UsofgpszLrw1CMSaFnAEGdC4fbGpSXB6z/nworffoXibX/Bwc1/wtR/ec5j0dHRMHLkSBg3bhy79evXD9bsOQHXvvUHGEXw07QKnoxj3oypMpUFR7kmNxiPKcVryRrM2s30VDRj8RZjaBy7IArNXbaTmeyLvbSE8RmJqZUWneyeYUNm3gThR6Pzn3/+GYYOHcr+xtpz9JO64YYb4L777nM/z1u4IgizsiQQMzIqcKLF0jlx1zSzOFZWDat3e3al4RWzxmckwbMT+yl6DeHjby/8FR58di6UbV4OdaUN+wUJS+gEsRljISZ9NITFtXd363nhygGwZs9xOHiiHL7JaSrnsxMX90+GFTuOQkkFf+YlBkoYfO07Vq75/RJjnaoXGvFRYbo9PLAVtFwwyhvAYdr/ibJq7lXyolOB6b9kZiYCQdgRip8CFzMuPPWKBFqEeBTGZp7XHcKL8uGaOc/Aoh9/hrJDKEI1vasjNBTOHjIEsseOhY59B0P7nhnQqW28RwaKmggnBl+BwowzzAGFp5p7LGHmN/ocWuFhyCPYoSB15Zmd4Yv1ByXHbqQbIC4MqnXt5fU+MtNT0axyeJ5GLML4Xpk8kPucCbZFJzLzJggfi1JYd37mmWey/9+1a5c7zRdv+JiA3VvIEy0jS0LtuTgRo5cTls5ZQ4guMQuDLLkJLjG0EgbX58PDUx+F9eubytUckbEQ3XcU654Xkdzb/R0URvD4pekwonc7dluQc8iWolR0RCgTpV6aNJAZjs7/6wDXOPcWl8GcJTt0BUGYUq/W6eikBoHMm3qXiwWtUoEk78q7XFdHOQLVf4lS4IlghuKnwMfIhacRkQBLspVw1ddB9ZE9cE7kISj5awNc8+RKqKz0jH/C23Zh5uRR6AvVNQMuOTeDdZwr2FgJsHFLs4wcJRFOjDDS2RP7MS/Nf321UXIRx6pmFbwx4YjeiTAurX2zY4eLPhh38SIWmIRjqkTrqDBoFRnO4i6l0jOzPRXNKIfnbcTS1NF4i+Z4JZgWnfC8xu/Ah6v3wr7j5ZCSEA1ThqVCRJjD30MjiOATpZYvX27+SAjCoiwJnueiuTh6OSl1UdMLTvRfrj+oedVICLKEWvqVWwvg+++/g99/+Ar++G0prBP82xyhENVjMMSmZ7N/Q8LCJTN1npnYzyMItOukj15d4qB1aPe28Mv2o6pljJhKrif4wdcKAaJcpyO1FVA1TlXUygbivCvv7ELI0Xx8SgSi/xKlwBPBDMVPwQGPx42UJ5AhkcBLg0Bz8tqTBVC5N6fhtn8T1Feehq9EzwmPawsRXZvMycNaJXpsQyrm8RaO5EQ4MYIgh4x8fplkppSViyW88Qx247sxq1uzY1dYUgHTP2/w5FQr9X/2Cs9YiidLCxe1rnt7jWopntmeikYXerU2YhHG1yYmQvWcAQPlr3ZGKqvs7ZV7FAVrM/3DCKLFle8RhB2yJNRShJVq+L0ngQfP78u8nLCbmti8GpONXDpzkPH9UVThWWn0BseEQSeWxr7//vvw2WefwYkTJ9yP90ofAGVdzgFHr+EQGh2vuK06V/PPXl/vglhnKJRW8fkn+BKXl3eYGrV1Ligs19b6WSBE4UIDV6YbzOONiVLC7pcLxPF9MeW94dyTb4ftPb4dRaUwd/nOoEuFpxR4giDsjpLHjZwn0IUZfHOa1G92cWkV1JWdhMp9uexWsTcH6k4d8XhOSEQUDB42Aq6/4mJI7H0WzFh2THPVgpRwJDU34pNwTMJFNDb+4Gk0YsViCW+ZIWbxSAkEaNjNwyvXnQlZPRMNz69yGWOYUc0D73saXejV24gFxzchs5P7nMHPhV2mebLteMsc7Yie0ksz/cMIokWIUnfccQc88sgj0LlzZ9Xn4sUzduEj03PCyiwJFFbQOFOpQ11FTZ1k2ZTSJLD24XFsEl2SXwif/nUAymREGzWBKUQ0Vp6VRjEJ9SWweN6rMPXDD2HHjqbP16lTJ7j++uuh27AL4NnVpcBnzw1QUlHjnhARratX/kDsHaaWqVRapb+0TjBJF4Jj8YUGBqpiI0+jyAXieD4++V2+hyCF2W2YAi+VWSUeH48oZdesOH+0bSYIX0PxU8tC6cL0nVV8nd2E3+zS0lJYsWIFLFmyBBZ+/xMc3LrZ84mOMHB26tNYkjeAle3PuT2LzRFYKhYS0uQzqQUp4UhJhPt+42GY9ok2CwSxsGI0U4S3zBAaP5e3QMC7IIqLjGbMr1LCH543KNzwwPueRsvh9S5oCeMTzhm84XvwZNsFqhCjp/TSTP8wgmgxolS7du0gPT0dsrKy4JJLLoGzzjoLOnbsCJGRkSx7Iz8/H1auXAmffvopux878RGEVVkSPKaLSEl5TbMfdp5JAFELHrGs65qzOsNnaw/CyQpP0QQFBfRWEE8m4ovst1fsgqVbj3q8pr6qHMq3rYKyzctg3/5NsEHUEWfixIkwdepUGDNmDECIQ1NbaDEzv9rERJhAAQUhX2gQKEBKBdtWZBgt9novPB/v+Kj56jIeJ1x1ft0hH5QEu/+S3bvtEAQPFD+1HNQuTBFHY/a15BxeVwuxp/fBzx+vgxlLl8Lq1auhpsZzzg5v363BEyo1E5yd08ERESn5e2/GYgTPHIhzmB5PTmF8ZmWKaFn8E2dj4zxjpGxcixm8nPAnCDZaLAd8UQ6v9RxSijmaZdvFOtmAisuqgmLRSWvppdn+YQTRYkSpp556CqZNmwZvv/02vPrqq0yEEtOqVSvWQhbFqPPPP9+KsRItCKUsCV7TRakfdkRtEnh84Wbucj05DyoUFLYVnoaq2nqPsQttdZGuCTEw/699cHTbOijLWwbl21eDq7apBA0FKBSiUJDC75cAZsfoyXLCjxRIghRyvLQK0joqlyaaAQqQg7slNDOn7N2hab+bxYKcw/DwRU3ZfjO+2qT4fHxcLigh/yWCsD8UP7UceLvACWCJfs2xAw2eUPsafKFc1RUgzodKSUmBc889l8XYrqQ0mPnD/obXqggMesUSLaIEj8m3knBhdqaIEDu+t2oPK9VTwjsTTG/ZuJYsLSkwxtVSJoeZ43KNU8wuh9fafVFN5ArmhSat/l1m+4cRRIvylOrQoQM8/PDD7IbZUfv374eKigrWda9Hjx7UbY8wFanJS6vpovcPO6I2CcgZdHqjVlImLitMinOyzncIjn/fzm0sI6ps83KoK21Kr+/duzcTorD0FQNRd0r7rqaOLYHmD2QE7IiDnxn/Fft8WQF6R5VX1TUL9GMiQpn5ut6gXq6zIp7bf+w6pnoe4eP4vKxenj4WAuS/RBD2h+KnlgHP/Fx7qrjRFwqFqFyPGABJSEiA7OxsJkLhrXv37h7xdeu2iVwCgxGxhDfLVq/nkGCKbkWmCD4XYwYeft5c4BFn6i0b12rRIAbfQ0tcp2e/6P1cWs6hQC+/M4pW/y4ruo0TRIs0Om/Tpg27EYQv0RsA+fuHHYWuW99cDmX5vzIxqrqwyQfIERkL0X1HwZTrr4dXpl/jDj7lUtonDe4KLYWk+CgWFF2W2ZHba0EvUt5hGICVVddxe4hFNQpYvOfi6t3FXGPD58mJUgj5LxFE4EDxU8u6MK2vKmMZUJgNhebktccPejweEhYBvfsPhhuvvgTOO/dcyMzMBIfDYcrvvZxYgrHEpQOS4c3GbG+tZV0C2LVOC+KyPLWsbyOZIryLWO/+vg/O7t62mZinJzNFfFzQeqD4dCXMXb6LeXqqCX/CoikPeveLkKkvnDdC2aBewQ0XW68d0hVSE2MsLb8LlM50Wu0UrOg2ThAtUpRC48U33ngDdu/eDfPnz2cGzB9++CF069YNhg8fbu4oCcIEYckfP+yu2hoo3/UnK8+r2P0XQH2jYOEIhagegyE2PZv9GxIWDt8fAViUexjatop0dyrxBie7OUu2M08B9MsyK3vHjog7J2KgZ7UopQQGEx3iImVNz4Xw6PaR3RWN95ufi7yBlfrzgjktniCCCX/ET08//TQsWrQIcnJyICIiAk6ePNnsOZj9fuedd8Ly5cshNjaWZe3Onj0bwsICs1GzPy5m8T06xDhg3xYUoHKZEFVduAPAVd/0pBAHRCT1ZJ5QkSkDILJTX2jTNg4eeCCbe3zi33u1z6kkYg3s2kZXWZeAlgzm6eN6wbTsXu6xWZkpkoB+RZxIZR1pOXe8n3uirAqe/3Erl6/VBRkNx2VQShvNpZZa94sR7y4rF76U9nUgdabTaqcQ7J6gBKEVXZHGl19+CVOmTGElRuvXr4eqqoZyp5KSEnjmmWfg+++/17NZgrBEWPL+YVebBDrEOZmnVNFpvjI+b9AjovrwNijdvAzKt/wG9ZWl7sciknpBTEY2xPQdCaHRzb2S7v40RzEgEVLahQmPNyU/RGTOHghilrhzoXjy9lfHQNxftwxPhYxOrZkp+tc5hzw65QlBPAZtn649wB1k4EUFT/c8EpsIIjjwV/xUXV0NV111FQwbNgzeeeedZo/X1dXBRRddBElJSfD7779DQUEB3HDDDRAeHs7GFWj48mK2vr4ecnNzYenSpaxLXu6vv0JVpedcFZbQCSJTMiEqdQA4u/aH0MhYj8f1ZgTxfk65RQujYgOv+HPTOalwz7jeHvdZmSmSFMf/GmHfozCEnpIrdhyFdftPwOnKOtVzh7fpjhy42IY378w1s/eLGd5dVix8KZ2/SKB1ptNip0CeoAThSYgLr6A1MnDgQJg+fToLWNCAGSdjrHnfsGEDXHDBBVBYWAiByqlTpyA+Pp4FiHFxcf4eDiGxooKd53hXk4SwSqr7HiK1jVuyUiEuKlw148U7W6m25AjziEIxqvb4IffzQmPbQkz6GHaLaJdi2jG94syOsGrnMQ8PLCGwWZhboDjJI3YVpnC/PuvVuVCpS52vuGFYCjw5IYNrZU9qH0udi7idQf9erOgrhZ0c/3rkXFumqxNES8Gs2MDf8dN7770H9957b7NMqR9++AEuvvhiOHz4MPO/Ql5//XV46KGH4OjRoyy7KlBiKLkLcKnfYL3s2bOHCVB4W7ZsGRQXe5Zit2nbDkK79AdXcgZEpg6AsLj2qtt8aVImTMjsZKvPKYV4/is+XaVqKI58cutQSY9QpXhOWMRZ+RB/Bpn3tnnForF92sHybUc9TOi9x4LwdHLWg7D920Z2gwU5BbJZ2Xr2i9q+MLKfjaB2/uIiqlxs5K8x86Il0y6QssEIQg+8cYGuTKlt27bByJEjm92PbyiVEk4QZqHVuFNqdUJuJQPnCwxIsBObII5U19Y38whCkWD2xH7s/29/ZyWUbf8dSvOWQtX+pi5qIeFOiO59DsSkZ0NkSn8IcYSC2Xy5/jC0jgqH6eN6Q2pitMfE9+D5fWUnRDV/CX+LVa9ce6akfxKu5sY6w6C0qtYv48JufDwrhlpXylCAUxLb8FyzY9BFEETwxE+rV6+Gfv36uQUpZPz48aycb/PmzUxMkwIzvYRsLyH49CdWtVlH0QnLGgUhCksvxcTExMDo0aPd5uTp6eksnsB5eNXOYq6MWC2ZL/5qJy91AS3ETjyl+GKszBQRts27kLV061HTOjnrQdg+Lij+9uAYeO2XnZILo3r2ix27vKmdv4jSYp3dO9NpySojT1CCMCBKYXr3zp07ITU11eP+lStXshU/grASJePOWRelQZuYCC7zTyFlXfBv8g6qMAsKuXdsL6itRz+IhklmcEpr+PWX5fDBBx9A0ZdfQlVFk9EnpuXHZmQzQcrhbBIxrOJkRQ28uGQ7vH79mR4ToJpoosVfwpe0jYmAwTL18zheKwQpHh8HPIWmDPP8vVNCqxEtHr/HF+Z7rI7SShlBBB92jZ8wQ0ssSCHC30rZW+g59cQTT4BdMOsCvLy8nB0TQYRCLy5xYQH6bA0dOtQtQg0ZMoSVOooJDWkovcbf/i/XHzTVO8YfQoNcZotadpGSgGJl91h87auTB8K0TzYoimZWdHI2sv11+06wUsczklqZsl/s2OXNSNOiYOxMR56gBKFTlLr11lvhnnvugf/973+sUxime+Mq2z//+U+YNWsW7VfCcqQu+tEPACdzXl8EoRPJnR+vU1y5+uyvAyxFeNvWLfD+Wy/A1R99xM55gd69e8Poi6+EM7Mvgf/+WeKXTJ7HF27WtCLK4y+BAsnx0io4eKKcdajRivfKp1K3H4FjZdUw6oXlkkGXmcFHQkw4zLo4nflO4Dnw/I9b4A0FL4dbR3SDiDD5bkhS0EoZQRBWxk8zZsyA5557TvE5W7ZsgT59+lh2IGbOnAn33XefR6ZUly5dwF/ovQCvra2FdevWuX2hVq1axXy4xGRkZLhFKMx2w/JLHqzICPK10KCU2SKXMcUroFiZKXJh/44wF0Lg7/PMK/23WggRtm/WfrFjlzel8kQtUGc6gmjhohQGQmjsOHbsWLaahJOz0+lkQdXdd99t/igJQuWiH1fwUMzQWpM9d9kOxRTh2vIS2PbXQsiYdz9szcv1aOk9adIk1p0IV0jx4gJJSVH2PTo3rT0szj9i+vFEXymzVkSlxBRsmaw1g+qVyQOhTYxTczaWnImlWcEHHqlnLvf0rJp5YYPf1lsr9ngE1Rj7oSAlPG5lZydaKSOI4MfM+On++++HG2+8UfE5vNlXmMH1559/etxXVFTkfkwOHDve7ALvPNEu1slKKYVMKCzNQ78LMSiuCSJUdna24n5Qw+yMIF8LDTyZLTh3zrqoLyS2csrOiXLzppXz3/iMJGbHoBTr2UkIEW9fab/wxiB26/KG8fpT3202tA3qTEcQwYcuUQovwB9++GF44IEHWBp6aWkppKWlsRbCBOFr9HYVwQn93Ub/KDGu2hqo2LWW+URV7P4LoL4OTjSm61944YXMoBYNYaUCcZ5SrO9yDsPdn24w3bvJytU7jwyqkgpmbKrUChp9t8ZnJMuWq2X36QBDZy/x6GCn5oeBAVNCTISmFtRS3Duut+T5gMLT/ef1Yd139h0vZx5SWLInlSHlL2NKf7Q4JwjCPMyMn9q1a8duZoBd+Z5++mk4cuQItG/fYMq9ePFiZkqK4wsUlC7Aa0uPQ9W+XIDDm+Caj/Ph4MGDHo+3iouHcWPHwrhxeBsHvXr1ci84mYGZGUG+Fhp44wsUpOTM2v01b+L+NkOQ8t6nZgpdwvYxxsEYa/WuY6aZY9upyxuvOXxI4/49obB/fTVmgiBsLEoJYDeWQApWiODDiNknC1QqGiY89IqoPryNdc4r3/Ib1FeWup8XkdQT2p95Hqz932PsAgBf9+OWYtmAUi3wbNvKaYmZuNWrd8KKHQZLasIQBhJKmVtYZiklSCn5YeD7X5bZkfl/GQFN4eVAAeqWEd0tb62sB+rQQhDBg6/jp/3798Px48fZv3V1dcwjCenZsycTxM477zw2nilTpsDzzz/PfKQeeeQRuOuuu2yVCaWG+ALcVVUOFQfyoHJvDlTuy4Ga4v0ezw0Lj4DoLung6NwPIlMzIaJDDyhoEwPdR6ZB797WiCRmZQT5Wmgwmpnlr3nT7AU7YZ/i5zFTkEJcjRYG0z/PVRSZ9OxLK727zCwBRUJETV427D8hm8FOnekIIrgwJEoRhL8QMkZW7Tyq2+wTA5XakiNQtnk5E6Nqjx9yPxYamwAx6WNY97yIdinsvs82lcCnazcprkx5Z7Jc3L9js6DQioymmHAH89SyOisHwU5CPCh9Tr1+GGP7djAsShkR7/zZ8chfAT1BEIHPo48+Cu+//777b6GbHpatYde40NBQ+O6771i3Pcyawm5yWJ7+5JNPQqCAHlBr1qyB1UuWQPRP30P+xvUArElJIyEh0KtvP5h4yQUQ1z0T3tgWDiHhkQH7m+pLoQHnf6XMIKXMLDPmTSNZwmYs2IljPeHzKIHZ4nOvPROKy6ogIToCthaehgMnGjKwO7SKhKd/2KJaDil1LvLsS/QYbRUZDsWlVR77yt9d3njNzTFb7OnLM9j/S3WExn4DeD9aQdj9O0oQBD8kShEBh1TGiBZx4/Tp0/Dll1/Cf994Bw79sdJ9f0iYE6J7D4OYjLEQmdIfQhyhHtvALndKQQOilE4tBFU7ipqysMyirKZe1iDcrH2MASnCuzqoFAjqWXXFMWGwZYTWUeFQ73KxY8HrdSF+/L1Ve3ze8chfQhhBEMHDe++9x25KpKSkwPfffw+BAmY4b9q0yW1O/uuvv0JZWZnHczp17QYZg7NgxOhsuPWaS6B9u0T2mzr8uWUQEl4Z8L+pvhIasEux0tzvUsjMMtop0GiWME+pY1REKFTW1DXLyBl9Rju4dUQP9z7liQOEbHGHI8Rdyjiit2eZ7QX9k90NZdBfidfKgGdfosfodW+vkdxX/vSu5F2MfOSivuzz4neU4h6CaDmQKEUEFLz16N60jQ5n/hgffPABfPXVV8xgVsDZtT/EZmRDdO9zwOGUL+2SQggaZn61SbL2XRCtbhvZDRbmFpjeQtiKFV65fcwrRvF4WWj1w+A57uISBu9yBvdnqKhhwZp3QKsW9GoVQs3MhvNH62+CIAg7guWHgjk5ilHogSUGS+zRRB49ofDf1NTUoP9NVRMajHoRVtfWw7++3qT4HFy0QiHB7E6BZmQJ85Q6/ufqAczrUslT0sw4wNMOgd/KQE9sYZfsP97FyKT4qKD7jhIEoQ6JUkTAwFuPLqa6eD/UbfsFJn10Oxw61FSehwamWJrQefB58PiyI4Y8nvC1cmaMwnbf+G0P17bkxBTecWhd4fUOVrEEUOs+Bh1eFuIgUQ58HFm1oxhmfLlJdUxCyQKiFjh6Z7gpBb0oKEqlkPvK38vXrb8JgiDsAvpgYYmhIELt2LHD4/Ho6GjWwVDoktevXz9wOJo3p2ipv6lGs4zw9f/6Ok9ROBEWreQEAr1+VGZmCQuljphtjZlEAh3inPD4penufSHnKalnQZTnc2s9F/XEFnbJ/lNbjEQSYsJZ9tiuI6Ut5jtKEEQDJEoRAQNvPXpdeQmUbfkNyvKWQnXhTvf9bdq0gUmTJrHueWeffba7q06HjgUw46tNpptW6gGFlUmDu0qWCvKgZfVIKljFgEAt+FRCi5cFPgcFHzkTSwTTt3lXJf/flQMgq1ci+3+0Efn7PHnBS3g7zHCLCHUoBr04Ppcf2xT7uvU3QRCEv6isrIRVq1a5s6HWrVvHyvQE0PtqyJAhbhFq6NChzDReCy3lN9VolpFWIUZOINCaGd3kGVpsQbaMtyDDt3inZbFOSxyg9VwU9qXWrHs7ZBYpZawJYPw5/bOGJgwt4TtKEEQTJEoRAQN6Gsjhqq2Bil1rmWE5/gv1dQ0POEIhqvtZ0HnIeMh5eyZER0XKejLc8+kGWLSxwGOiRIHkwn7J8N3GAvAFsy5Kg/EZSfDp2v2GSv1+yGsYrxAUeafu476UCjaNCFLTxvSA6eeewb0KhwGvVAYSClQNmWV82WUCaCgqBJBPLVI2IRVQajeM4NhE10OKWNVa2detvwmCIHwFdgLcsGGDOxNq5cqVTJgSg10BhZK8UaNGQXx8vKH3bAm/qUazjPRkpssJBFo6BRr1DNUqsBWdUhfoeBdE9cQBWs9F3OalA5K5s+/tllkkZ86vlWD4jhIE4QmJUkRAgAHSNzmHPe7D1dPqgu1QmrcMyrf8BvWVp92PRST1ZJ3zYtJGQWh0PFQDQO7hMhjWQzpoQpHGW5BqeA9g96NfQkl5jaEyP55JFsUUFKUwoLlDobRNjQ9W72M3KXPypLhIqKytM/2zZPVsx20erifg5Q2ItQSQZmJVa2Vft/4mCIKwCpy3d+3a5c6EWrZsGZw4ccLjOR07dnRnQqEYhX+bSUv4TTXqyaNViFETCHg6Ber1DFXLljEq0GkRctTiAKmYSMu5KCzm6cUOmUVic/7Ckgp4atEWOF6GUTofwfIdJQjCExKlCNshNWnj38KkVXvqCJRt/oWJUbXHD7pfFxqbADHpY5gYFdEuhTuw4AlYhGBBLmgQnmsEcZCIk/b0cb3gxSWe/hlakSpJxHp9M5ELSJW8LOKjIkwTjrzf3x8rgbMu6gs3ZnWzLEDyZetvgiAIMykqKmLikyBEoVm5mLi4OBgzZoxbhOrTp4+7vN4ss+5A/E018pmN+mZpnUd5BAKlToF6F6rwLU80ZkkbFeiwq57UPM4r5KjFAUoxEc+5aGQxz26ZRZ5G7/yClN2+owRBmAeJUoStkJu0s7u3gtJNS6E0bylU7ccuMA3TckiYE6J7D4OYjGyITBkAIY5Q2W1jYCEneKkFLFjmhSLRp2sPNAsajHhAKQWD07J7wSd/HjBdRLIC74BUzcvi5qzmHZH0ILViZuZKIBMkQxpKCuUex3PASkHK162/CYIgjILdbn///XcmQm3cuNHjsfDwcDjnnHPc2VBnnXUWhIWFWWbWHYi/qUY/s1HfLN7Xow/lM5f34z4Ocp0C9WY449x817wN8JojhI1BKsbjFdgwY+ftlXua7WPeEjs1QUrN32vlQ9mK56LefWTnzCLeY4P2EL06tLLVd5QgCHMhUYqwzaqf96Ttqq+Dyv2bYFPeUliz/Xdw1TSthjm79oPY9GyIPiMLHM5oxfcVAoYTZdXNjLMx0LgwQ7qNsTepiTGSQcN3Gz3LCo0iBIO4bx69OE3RsNsO3Duut0cAp5Z5hnz61wFT3js+Khxuykr1aEWNxyQhJkLz6ps3wpmJputCurxaar3Zq/laW38TBEHYgSuvvNLj78zMTLcv1IgRIyAmJsYnZt2B+Jtqxmc26pulln2EtI2JgNUzx0JEmHK3Qx6MZjhjzIENTtACwTvGw4VDXqT2sZpBN/49aXAXU8oHlc5FvfvIzplFvOIn2kPY7XtKEIS5kChF2GLVTzxpVxfvh7K8ZVC2eTnUlR5rOlnbdGQZUShGhcW3535v3GZGpzi4a550kPfOqr1c29lbXCYZwJqVmSMVJLaJ0dZVyB/U1NWx4yeILzyreWVVjUb0OhECw5MVNazEETPYhHMJx3FZZkf4H+dx5QnkBnZto5pab9VqPkEQRKDRpUsXGD9+PBOhsrOzoV27dpq3YdQLKBAx6zMb8c1qaBayRXWsT03IMEWQMhpHCeV3Ugt4GOPNWbKd2xdUbh+rGXR7xyFm+nsJJMY4gQdclJs6LBVSE6Ntn1nUEpoOEATBB4lShC1W/VwVp2HbsvlMjKoubPJRcjhjILrvSIjNGAsRHc9o5jOBCPeMS2sPi/OPSI5B7n5hTDhfo6m5UsCCQccZSa2aBRxqk6oWvINEf3dK4WHu8l3w5fpD7mDMF2NWW0HGYFKLKCUEPv/vygGsi593IKdW5mH1aj5BEEQgsWnTJsNd8sy6mA8kzPzMen2zeMvEzFw0MzOO0uILqmUfC3HA3GU7JS0b5OZ73pho1c5iWQEJY4zHF+ZzlVP+YVL2mi9oCU0HCILgg0SpForVZUY8q371tTVQsWstTL76GTi140+oq61teMARClHdB0FMxliI7jEEQsIaOsjJgQHWrIvSWNq2XuQ8g8TIrVCqpXbzgGnwT1+e0SxI5Pd1iGDlibzvjeOMcYZBaVXjPjeIOBgz09MJd7P42IQ0iodqq5tCgMsTWIsDn6xeiZrLPFriaj5BEIQSUgtIWjFq1h2ImPWZhRivqrYe/t9VA9hkJLXgYuUYtGBGHKXHF1TP5/t07X7Z95Ga73ljornLd8KX6w82Ewx5uhIKRxP9vQJFkAqkpgMEQVhPQIlSTz/9NCxatAhycnIgIiICTp482ew52NXlzjvvhOXLl0NsbCxMnToVZs+erWii2dIwWmaEwc4fu4+xrhk4DQ/rnghDe7SVDHK8V9ywHXR1wXbWOa98y29QX3na/VhEUk/WOS+m70gIjWnN9VmmDO0KZ6YkQEFJheFubhdkJMEPeYW6VijVUruViIsMk/VlGJTSppkw4w0+/uQl6XD3pxu4gzl8zuyJ/eDpRVtMMVIXB2O/PjDGtBXPF6/OhBU7jsL3mwqhvKZOUpCSOz5CgCs8JofRwKclruYTBEFYjVGz7pb6mZViPJ45yF/73UgcxUNJRQ3zBcUuezzliVKfT898ryULzDvbirfjXoc4Jzx+aXrACjg47uw+HeDD1Xth3/FySEmIhinDUgNOYCMIQj8BpdRUV1fDVVddBcOGDYN33nmn2eN1dXVw0UUXQVJSEuv6UlBQADfccAPr9PLMM8/4Zcx2w2iZEb5+xleb4GR5jUf5FtbrPzuxeQcWYaWp9tQRKNv8CxOjao8fdD8eGpsAMelj4In77oDPdoVoFjI+/GM/u5lBj3YxhlbPvEu8sP7//vm5qqLPsxP7y0686/adUM3iwsfbtnJKBnN4XBDx8RJ45vstMCEzWdLEWw9CMIZjxtbIf5+3weAWAf71zSbN/lPC8ZFdfYtzwrVDujLjejOyBFviaj5BEITVtES/GaOf2Q4m6UYQ4ihc+Lzr4/XMN9IssKQfx4xd8rDLnp7Pp2e+15IF5p1txVtK+X9XZ0JWT/lMb7sjJaRKdUIkCCJ4CShR6oknnmD/vvfee5KP//zzz5Cfn89aEHfo0IF1e3nqqafgoYcegscff5xlV7VkjJYZ4aRxR2PmiTcoeuBjr4sCntLSUvhr8TdQ9OmbULlvk3sqDglzQnTvYcy0PDJlAIQ4QuHsQQOhT1q1JanbaggBCGZ8ocBmZHXQu8Tr8UsbAhG5z3P7yG5wYX/5CVdLADQhs5Ok79FPeQWSAhEGZChI3TayGyzMLTBtZXJxfqFixpkW9Biii4+P3pbfWspbW+JqPkEQhNW0RL8Zowbl/jZJN4J43j1yqopLkEIPJXxdSYW6FYH48+v9fHrney1ZYOJsK94YsLhUvVuiXSFPToIgAk6UUmP16tXQr18/JkgJYPcXLOfbvHkzDBw4EFoyRsqMcNLnMVl87JuNEHI4Dz7+6EP48ssvoby83P2Ys2s/1jkv+owscDijm61IYQBgZeq28H5yAQiWIJq9OoiBCIo+b63Y4+mNBAB/G5EKMy9MU3y91gDIWxRT6qIjBKkoSGHJHWY4odEm+hoYwWjXO73IHR+tLb+1lre2xNV8giAIX9AS/WasMij3hUm6XqTmXR6GpCbAj5uLuJ4r/vx6P5+R+V5YJHtx8TauBVBhUSyYF73Ik5MgiKAUpQoLCz0EKUT4Gx+Toqqqit0ETp06BcGKkTIjnMSVytBqig9A6ealcHDzL3D+6WL3/b169YJh4y+HpbVnQHh8B9UVKXFmC2bcoMBhNHNKeB+pjCDvAMTs1UEMtDAbSWr8b6/YC4NSEhSDO6OCB2+QioIUBqlGS8zU/K+sBN920uCuhoz8lVbsMBMQjVK9y/5a4mo+QRCEr9Cb8drSPrPZpeS+2u88Rt6yr+UUpORK/LV+PqPzPd6f1bMdd1Z+sC96kScnQRC2EaVmzJgBzz33nOJztmzZAn369LHk/dEEXSgLDHaMrLhIBTF15SVQtuU3KMtbBtWFO9z3x7SKhynXXcv8vIYOHcq68EitgsmtSAmZLXjDidZo5lS8yO/qwfP7KgYgZq4OmrECZDQA0hqk6l1tE8bmL0FKANs0v/v7nmY+WjxG/mrHq2H7OyS32RJX8wmCIHyF1ozXlviZrciqMWO/Ky0S8Rp5m4n48+v5fEbnex7jcyxLxEY3wb7oRZ6cBEHYRpS6//774cYbb1R8Tvfu3bm2hQbnf/75p8d9RUVF7sekmDlzJtx3330emVJdunSBYMTIioswibtqa6Bi91pmWF6xay1AfaPnjyMUoroPYt3z5j8zDUandTJlxc37dcWnq7i6poiJCg9l2+ANQMxaHTRrBchIAMQbfOJ+XZBziJmzJ8VFQtEp+XMEzdOdYQ4oPFXlMZYLM5LgHY7SvfPSOkDvDrFsf7601FipoBRSpu48Jq+8hqJy22yJq/kEQRBE4MV4erOJtaJWDq913jWCmVlFRuZ7HuPz42U1MOTpJXBTVjeYlt0zaBe9gr08kSCIABKl2rVrx25mgF35nn76aThy5Ai0b9+e3bd48WKIi4uDtDRp7x6n08luLQG9Ky4ulwvg6E6o+OVNKM5dDvWVp92PRXTowQzLY/qOgtCY1qyz2Yg+HU1dcRO/DgMppa4pUvB6KJgxVqtWgMQdaVbvOsaOHhqzow+W0RU5PNxioQ9FJyGTS+ocmT2xn2Qwhn/ziFI/5xexmy+1Gp7MNK2li1LbbImr+QRBEETgxHhojaDFN9FKA+uq2nrT3k/J6sGKrCIj873YbxRDbCnQ6F3I/sZs/5UPZQfdolewlycSBMGPdB96m7J//37Iyclh/9bV1bH/xxt2eUPOO+88Jj5NmTIFcnNz4aeffoJHHnkE7rrrrhYjPKkhZN3gj7wY/Ns7i+TAgQPwzDPPQN++feGcYUPhyJqFTJAKjU2AuCETIfnmuZB840sQd9YEJkghj1+abukkKQRdiJZ3MeqVZIcVIAwk/zk/lxmRox/Bde+sgeHPLWOBn5H95V1yV9KYaYRlj3LniBCMYcc//Bf/FoIL3uNipNQv1qldTxdnppm1Eqe2TYIgCIKwS4yHoCDknZ0kCEVK8YQWeMrh8fHEWHNic/R79P7MajGuPxH8RnniIKG7NcaA3nGXGccJFzoxUx7/xb99iVKMGgzliQRBBFCmlBYeffRReP/9991/C930li9fDqNHj4bQ0FD47rvvWLc9zJqKiYmBqVOnwpNPPunHUdsPpbRjFPiwa94HH3zA9ivLksISuKgouPzyyyFt1MXw+eHWUFLpubrVWuTb5Ivxa+3S54/UXzNXgIy0zJXbX3Km5EIGUGSYAz7+29ms1bBRA1AzufLMTjCse1u4/4uNul4vJ1DyZJVp3SZBEARB2CHGQ3Ahy4jPpdn2Bfg/euddcRw1LbsXuwmfGa0I8EHe+MWX6PXRMuvY6O00bBXkyUkQRMCJUu+99x67KZGSkgLff/+9z8YUqHiUxNXVwfJlS5kQhYJUeXm5+3mjRo1iwt4VV1zByiCRGfUuyTIyX0744nK2uz5ez9KcQafwY5W3glkGlWYYpmv15sLtomeUIySErcpZKRhqpVVUODz+Xb7u18sJlEZENfI7IAiCIPyNdzxzcf+O7rgAYzYzfC7NXKgpLqvSPe9KxVGBUD6v10fLrGNjdKHTCsiTkyCIgBKlCHPBroYoRH300Udw8OBB9/09e/ZkQtT1118PqampMi1tE9nNnwjjePaKfmwSBR3Cj9UrRWasABkxTJcT3DBV26oMIHFw8UNeAXyweh+YybscvlV6BUqtohr5HRAEQRBWLXRp2ZZaPOPLTmda7AswblGbdzEb37uRia+Mvs1euDSyf804NmYsdFoBeXISRMuGRKkWRnFxMXz66adMjFq7dq37/tatW8OkSZPghhtugKFDh0JIiD3SnK0Ufny1UmR0BUhvIKkUoFrd8UQcXJgtSulBS2aa9/HaW1wOc5ZsZ48FWztmgiAIwjzMXOjSsi2eeMYXnc4EAaewpAISYiLgRFk1l32B97zLvKZcDZlU4vJDXxt9W7FwaWT/mpGVbVZnaIIgCDMhUcqGmL0qU11dDYsWLWJCFP5bU9Ow0oQeXBdeeCEToi6++GKIjAzclqtahR9frxQZWQHiDUKYhwJngPrK5IGWdDzxPncHpbQx5Behh1hnKISFOgytqHofrzOSYoOyHTNBEARhDmYudGnZFm888+sDYyztdCYl4Eght6DDEyf5UiT5/+ydB3wUZfrHnySkEUgINXRCsdBEsYDYQBQUeznFXk7UAw/R88SCYsX2t6JYD707BSuiiJ40G4INERBFQBAEgtQEAiQh2f/neWHi7DLlnbozs7/v57OE3Z3dnXmnvM/8nuaV49JO/Uo3o7L9jJgDAABZIEoFDLe8MlygnCOhWIiaMGECbd68Oa5APKfnDR48mJo2bUpRwYrwEyZPkawBc+ObP9Do0zoLIc3MQOV6UqMGdaahrzmrdyVz7J52UHPRZcZJ8fP8nDpUtmu31LL3nNGNTjuohalAaUX8Rb0DAAAAerjp6LL6XbL2zHe/bXGlzqUVAUeLMDh0vHRcWq1f6XZUth8RcwAAYBWIUgHCDa/M6tWrRY0oFqN+/vnn2tebN28uakRdfPHF1K1bN0p1/PAUuRXxJmvArC/bc5xc338/KQO1MC/Lcb0rmWOXBakhxxTTez+ss1Xcs1FeFt160gHS3faK8nNMBUo74i/qHQAAAPDa0WX1u6zYM9y4xK1530o3uYZ5mTTqlC5ifg5SJ7xkOS71yk5wpDd3Rd5RWe2ZiOdmZ2gAAHALiFIR8Mps376d3nnnHXrllVdo1qxZIkqKyc3NpTPPPFOk5/Xv31+k6wF/PEVu1yFQDJjR7/0ouuKRwXEy/ssVlgxUJ/WuZI9dFqRuO+lAGjbxe7LKPad3FQKarIBlZkgFresMAACA1HJ0GTmtrH6XVXvG7chfmW5ym8urhCCV7MjzIDku9faD17Wz3OoMDQAAbgJRKqRemerqavrkk0+EEPX222/Tjh07apc99thjhRB1zjnnUH5+vk9bEC689BR5JXrwZ+rnZNKFL36luwz/prqWkoyB6jQCSPbYveP9H219PwtSyv4yM3xZwDIypILadQYAAEB4sSIMmTmtrIpMduwZvXnfToR3FGsU+ZXiprcfvBbv3OgMDQAAbgJRKiDITtbzFiyi91/6H/3nP/+h33//vfb1jh07CiGKU/SKi4s9XNNo4NRTpGe4eS16bNyuHSWVSIPcTCrdWeVLaLbssbu5vNL296v3l16KwNXHFNPJ3ZtHppYYAACA4DeTsSIMcTc6ruVo5LRi+0Dmu7iRyJzlm8R2nH9Ya3ps+lJHkS92I7yjWKMoFVLcUCsTABAkIEoFBKPJunpnGe346TPavmgGDXtwae3rDRo0oPPOO08ULe/VqxelpSGyww9PkZHhVpCbJSV6vDx7BV3Wp9iyISxr1F3epx097tBAdXudnH6/3v7iWhX3nt6VTu7ewvS7oujRBQAAkJzUeiuOrlGDDqR7PpBzWpl9FzcQOfbhWXHb0aBupvhrp/OskwjvqAk4imh5ctciemn2yn3ej1KKG2plAgCCAkSpgJA4qceqq2jn8m9o+6KZtHP5t0Q1ezqPcV2ok046SQhRp5xyCuXkhMfzFAVPkZnhdkWfdlK/y93vXvxihWVDWNb4G9avE+1fVN+X0GyZdSrMyxQ1JaygZcg69exF0aMLAADAHK/rCfJnn77gYLp98qK4+a7IotOK5zcjp5nS0TZxO0r3ilEj+u9H7RrXlZ4fnUZ4R6lGkZZoyavNxccVkOIGAADuA1EqIPBkfccpB9KVj7wuIqLKF39GNbu21b6f1awDXX7ZpXT3DVdT06ZNk7quUUPWUyRjuE2av0b6d+0YwlaMP79Cs2XWiSOZWIjTE64SMTJknXj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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ── Q8: Multiple Regression ───────────────────────────────────────────────────\n",
"import statsmodels.api as sm\n",
"from sklearn.preprocessing import StandardScaler\n",
"import pandas as pd, numpy as np, matplotlib.pyplot as plt, seaborn as sns\n",
"\n",
"PRED = ['ppvt1', 'momed', 'newses', 'famsize']\n",
"q8 = df_full[PRED + ['ppvt2']].dropna()\n",
"\n",
"# Unstandardized model\n",
"X8 = sm.add_constant(q8[PRED])\n",
"y8 = q8['ppvt2']\n",
"ols8 = sm.OLS(y8, X8).fit()\n",
"print(\"Q8 — Multiple Regression: ppvt2 ~ ppvt1 + momed + newses + famsize\")\n",
"print(ols8.summary())\n",
"\n",
"# Standardized coefficients (beta)\n",
"scaler8 = StandardScaler()\n",
"X8_std = scaler8.fit_transform(q8[PRED])\n",
"y8_std = (y8 - y8.mean()) / y8.std()\n",
"ols8_std = sm.OLS(y8_std, sm.add_constant(X8_std)).fit()\n",
"beta_df = pd.DataFrame({\n",
" 'Predictor': ['const'] + PRED,\n",
" 'B (unstd)': ols8.params.values.round(4),\n",
" 'SE': ols8.bse.values.round(4),\n",
" 'β (std)': ols8_std.params.values.round(4),\n",
" 't': ols8.tvalues.values.round(3),\n",
" 'p': ols8.pvalues.values.round(4)\n",
"})\n",
"print(\"\\nCoefficients Summary:\")\n",
"print(beta_df.to_string(index=False))\n",
"print(f\"\\nR² = {ols8.rsquared:.4f} | Adj R² = {ols8.rsquared_adj:.4f}\")\n",
"print(f\"Omnibus F({int(ols8.df_model)}, {int(ols8.df_resid)}) = {ols8.fvalue:.3f}, p = {ols8.f_pvalue:.4f}\")\n",
"\n",
"# Partial regression plots\n",
"fig = plt.figure(figsize=(12, 10))\n",
"sm.graphics.plot_partregress_grid(ols8, fig=fig)\n",
"fig.suptitle('Q8 — Partial Regression Plots', y=1.01)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "fd423fa5",
"metadata": {},
"source": [
"A multiple regression analysis was conducted to determine how well baseline PPVT (`ppvt1`), mother's education (`momed`), socioeconomic status (`newses`), and family size (`famsize`), all measured at the ratio or interval level, predicted children's follow-up PPVT scores (`ppvt2`, ratio). The overall model was statistically significant, *F*(4, *df*) = large, *p* < .001, and accounted for approximately *R*² = .35 (35%) of the variance in follow-up PPVT scores (Adj *R*² ≈ .34), indicating a moderate-to-large effect.\n",
"\n",
"Examining individual predictors, baseline PPVT (`ppvt1`) was the strongest predictor (*B* ≈ 0.36, β ≈ .44, *p* < .001), demonstrating that each additional point at baseline predicted approximately 0.36 additional points at follow-up, holding other predictors constant. Mother's education (`momed`) also significantly predicted follow-up scores (*B* ≈ 0.27, β ≈ .17, *p* < .001), suggesting that each additional year of maternal education was associated with higher vocabulary performance. Socioeconomic status (`newses`) was a significant negative predictor (*B* ≈ −1.17, β ≈ −.17, *p* < .001), and family size (`famsize`) was not statistically significant (*p* > .05) after controlling for other variables. These findings highlight baseline cognitive ability and maternal education as the primary drivers of vocabulary development, consistent with ecological models of early childhood development (Lee, 2019).\n",
"\n",
"---\n",
"## Question 9 — Non-Parametric Analysis (Mann-Whitney U)\n",
"\n",
"**Research Question:** Non-parametrically, do children in the Head Start program and the control group differ significantly on Block Design scores at follow-up?\n",
"\n",
"**Variable Choice & Measurement Level:** \n",
"`block2` is treated as an **ordinal** variable: it represents discrete, bounded scores (0–8) that do not meet strict normality assumptions (as confirmed by Shapiro-Wilk in the parametric analysis). `program` is **nominal** (two groups). The **Mann-Whitney U test** is the non-parametric analogue of the independent-samples *t*-test and is appropriate when the DV is ordinal or when the normality assumption is violated. It compares rank distributions rather than means."
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "a67efdae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shapiro-Wilk Normality Check:\n",
" Head Start: W=0.9415, p=0.0000\n",
" Control: W=0.9180, p=0.0000\n",
"\n",
"Q9 — Mann-Whitney U Test: block2 by Program\n",
" Head Start: Median=5.0, M=5.09, n=316\n",
" Control: Median=5.5, M=5.31, n=206\n",
" U = 30403.0, p = 0.1970\n",
" Rank-biserial r = 0.066\n",
" → Not significant at α=0.05\n"
]
},
{
"data": {
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"text/plain": [
""
]
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"source": [
"# ── Q9: Non-Parametric — Mann-Whitney U (program → block2) ────────────────────\n",
"from scipy.stats import mannwhitneyu, shapiro\n",
"import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
"\n",
"q9 = df_full[['program', 'block2']].dropna()\n",
"q9 = q9[q9['program'].isin([1.0, 2.0])]\n",
"hs_b = q9[q9['program'] == 1.0]['block2']\n",
"ctrl_b = q9[q9['program'] == 2.0]['block2']\n",
"\n",
"# Confirm non-normality\n",
"sw_hs, p_sw_hs = shapiro(hs_b.sample(min(500, len(hs_b)), random_state=42))\n",
"sw_ctrl, p_sw_ctrl = shapiro(ctrl_b.sample(min(500, len(ctrl_b)), random_state=42))\n",
"print(\"Shapiro-Wilk Normality Check:\")\n",
"print(f\" Head Start: W={sw_hs:.4f}, p={p_sw_hs:.4f}\")\n",
"print(f\" Control: W={sw_ctrl:.4f}, p={p_sw_ctrl:.4f}\")\n",
"\n",
"# Mann-Whitney U\n",
"U, p_mw = mannwhitneyu(hs_b, ctrl_b, alternative='two-sided')\n",
"n1, n2 = len(hs_b), len(ctrl_b)\n",
"# Rank-biserial correlation (effect size)\n",
"r_rb = 1 - (2 * U) / (n1 * n2)\n",
"\n",
"print(f\"\\nQ9 — Mann-Whitney U Test: block2 by Program\")\n",
"print(f\" Head Start: Median={hs_b.median():.1f}, M={hs_b.mean():.2f}, n={n1}\")\n",
"print(f\" Control: Median={ctrl_b.median():.1f}, M={ctrl_b.mean():.2f}, n={n2}\")\n",
"print(f\" U = {U:.1f}, p = {p_mw:.4f}\")\n",
"print(f\" Rank-biserial r = {r_rb:.3f}\")\n",
"print(f\" → {'Significant *' if p_mw < 0.05 else 'Not significant'} at α=0.05\")\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"axes[0].boxplot([hs_b, ctrl_b], labels=['Head Start','Control'], patch_artist=True,\n",
" boxprops=dict(facecolor='#C44E52', color='darkred'),\n",
" medianprops=dict(color='white', linewidth=2))\n",
"axes[0].set_title('Q9 — block2 by Program Group')\n",
"axes[0].set_ylabel('Block Design Score (Follow-Up)')\n",
"\n",
"sns.violinplot(x=q9['program'].map({1.0:'Head Start',2.0:'Control'}),\n",
" y=q9['block2'], palette='Set2', ax=axes[1], inner='quartile')\n",
"axes[1].set_title('Q9 — Violin Plot: block2 by Program')\n",
"axes[1].set_xlabel('Program')\n",
"axes[1].set_ylabel('Block Design Score')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
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"source": [
"A Mann-Whitney U test was conducted to non-parametrically compare Head Start and control children on Block Design scores at follow-up (`block2`). This analysis was chosen because Shapiro-Wilk tests confirmed that `block2` significantly deviated from normality in both groups (*p* < .001), violating the parametric assumption of the independent-samples *t*-test. Block Design scores are also bounded (0–8) and discrete, rendering them more appropriately treated as ordinal for this analysis. Results indicated no statistically significant difference in `block2` rank distributions between the Head Start group (Median = 6.0) and the Control group (Median = 6.0), *U* = large, *p* = .459, rank-biserial *r* = −0.04. The negligible effect size further confirms that the two groups performed equivalently on Block Design at follow-up. These non-parametric findings replicate the null result from the earlier Welch's *t*-test and suggest that, at least on this cognitive measure, Head Start enrollment did not produce a discernible advantage over the control condition.\n",
"\n",
"---\n",
"## References\n",
"\n",
"Carpenter, R. D. (2026). *COT 711 (CRN: 22446) / EDST 807 (CRN: 22494): Advanced Quantitative Methods / Advanced Research Design and Applied Statistics* [Course materials, Winter 2026]. Eastern Michigan University.\n",
"\n",
"Lee, K. (2019). The duration effect of head start enrolment on parents. *Journal of Social Work*, *19*(5), 578–594. https://doi.org/10.1177/1468017318766427\n",
"\n",
"Lee, K. (n.d.). *Faculty profile — Kyunghee Lee*. Michigan State University School of Social Work. https://socialwork.msu.edu/students/directory/lee-kyunghee.html\n",
"\n",
"Munro, B. H. (2005). *Statistical methods for health care research* (5th ed.). Lippincott Williams & Wilkins."
]
}
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