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139 changes: 114 additions & 25 deletions lab-hypothesis-testing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -38,20 +38,21 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"#libraries\n",
"import pandas as pd\n",
"import scipy.stats as st\n",
"import numpy as np\n",
"\n"
"from scipy.stats import ttest_ind\n",
"from statsmodels.multivariate.manova import MANOVA\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 43,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -278,7 +279,7 @@
"[800 rows x 11 columns]"
]
},
"execution_count": 3,
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -297,11 +298,32 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 44,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-statistic: 3.3350\n",
"P-value: 0.0008\n",
"✅ Dragon Pokémons have significantly higher HP than Grass Pokémons.\n",
"\n"
]
}
],
"source": [
"#code here"
"dragon_hp = df[df['Type 1'] == 'Dragon']['HP']\n",
"grass_hp = df[df['Type 1'] == 'Grass']['HP']\n",
"\n",
"t_stat_1, p_val_1 = ttest_ind(dragon_hp, grass_hp, equal_var=False, alternative='greater')\n",
"\n",
"print(f\"T-statistic: {t_stat_1:.4f}\")\n",
"print(f\"P-value: {p_val_1:.4f}\")\n",
"if p_val_1 < 0.05:\n",
" print(\"✅ Dragon Pokémons have significantly higher HP than Grass Pokémons.\\n\")\n",
"else:\n",
" print(\"❌ No significant difference in HP between Dragon and Grass Pokémons.\\n\")"
]
},
{
Expand All @@ -313,11 +335,55 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Multivariate linear model\n",
"================================================================\n",
" \n",
"----------------------------------------------------------------\n",
" Intercept Value Num DF Den DF F Value Pr > F\n",
"----------------------------------------------------------------\n",
" Wilks' lambda 0.0592 6.0000 793.0000 2100.8338 0.0000\n",
" Pillai's trace 0.9408 6.0000 793.0000 2100.8338 0.0000\n",
" Hotelling-Lawley trace 15.8953 6.0000 793.0000 2100.8338 0.0000\n",
" Roy's greatest root 15.8953 6.0000 793.0000 2100.8338 0.0000\n",
"----------------------------------------------------------------\n",
" \n",
"----------------------------------------------------------------\n",
" Legendary Value Num DF Den DF F Value Pr > F\n",
"----------------------------------------------------------------\n",
" Wilks' lambda 0.7331 6.0000 793.0000 48.1098 0.0000\n",
" Pillai's trace 0.2669 6.0000 793.0000 48.1098 0.0000\n",
" Hotelling-Lawley trace 0.3640 6.0000 793.0000 48.1098 0.0000\n",
" Roy's greatest root 0.3640 6.0000 793.0000 48.1098 0.0000\n",
"================================================================\n",
"\n"
]
}
],
"source": [
"stats_cols = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
"\n",
"df = df.rename(columns={'Sp. Atk':'Sp_Atk', 'Sp. Def':'Sp_Def'})\n",
"stats_cols = ['HP', 'Attack', 'Defense', 'Sp_Atk', 'Sp_Def', 'Speed']\n",
"\n",
"manova = MANOVA.from_formula('HP + Attack + Defense + Sp_Atk + Sp_Def + Speed ~ Legendary', data=df)\n",
"manova_result = manova.mv_test()\n",
"\n",
"print(manova_result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"#code here"
"- The MANOVA test shows that Legendary and Non-Legendary Pokémons differ significantly across the combination of stats (HP, Attack, Defense, Sp_Atk, Sp_Def, Speed).\n",
"- Considering all stats together, Legendary Pokémons have a different profile than Non-Legendary Pokémons."
]
},
{
Expand All @@ -337,7 +403,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 46,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -453,7 +519,7 @@
"4 624.0 262.0 1.9250 65500.0 "
]
},
"execution_count": 5,
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -483,22 +549,45 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-statistic: 37.9923, P-value: 0.0000\n",
"✅ Houses close to schools or hospitals are significantly more expensive.\n",
"\n"
]
}
],
"source": [
"def euclidean_dist(lon1, lat1, lon2, lat2):\n",
" return np.sqrt((lon1 - lon2)**2 + (lat1 - lat2)**2)\n",
"\n",
"dist_school = euclidean_dist(df['longitude'], df['latitude'], *school)\n",
"dist_hospital = euclidean_dist(df['longitude'], df['latitude'], *hospital)\n",
"\n",
"close = (dist_school < 0.5) | (dist_hospital < 0.5)\n",
"df['close_to_school_or_hospital'] = close\n",
"\n",
"close_prices = df[df['close_to_school_or_hospital']]['median_house_value']\n",
"far_prices = df[~df['close_to_school_or_hospital']]['median_house_value']\n",
"\n",
"t_stat, p_val = ttest_ind(close_prices, far_prices, equal_var=False)\n",
"\n",
"print(f\"T-statistic: {t_stat:.4f}, P-value: {p_val:.4f}\")\n",
"if p_val < 0.05:\n",
" print(\"✅ Houses close to schools or hospitals are significantly more expensive.\\n\")\n",
"else:\n",
" print(\"❌ No significant price difference based on proximity.\\n\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -512,9 +601,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.13.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}