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Solved lab #367

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7473b62
Solved lab
fran-eliot Sep 15, 2025
0a60e37
Fixing lab
fran-eliot Sep 15, 2025
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224 changes: 211 additions & 13 deletions lab-hypothesis-testing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -278,7 +278,7 @@
"[800 rows x 11 columns]"
]
},
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -297,11 +297,39 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-statistic: 4.4991348531252635\n",
"P-value: 7.0097137393088205e-06\n",
"Reject the null hypothesis: Dragon-type Pokémon have significantly higher average HP than Grass-type Pokémon.\n"
]
}
],
"source": [
"#code here"
"# MOD: considerar Type 1 y Type 2 para Dragon/Grass (pequeña mejora)\n",
"dragon_mask = (df['Type 1'] == 'Dragon') | ((\"Type 2\" in df.columns) & (df['Type 2'] == 'Dragon'))\n",
"grass_mask = (df['Type 1'] == 'Grass') | ((\"Type 2\" in df.columns) & (df['Type 2'] == 'Grass'))\n",
"dragon_hp = df.loc[dragon_mask, 'HP'].dropna()\n",
"grass_hp = df.loc[grass_mask, 'HP'].dropna()\n",
"# Filter HP values for Dragon and Grass types\n",
"\n",
"# Perform a one-tailed t-test\n",
"t_stat, p_value = st.ttest_ind(dragon_hp, grass_hp, alternative='greater')\n",
"\n",
"# Print the results\n",
"print(f\"T-statistic: {t_stat}\")\n",
"print(f\"P-value: {p_value}\")\n",
"\n",
"# Conclusion\n",
"if p_value < 0.05:\n",
" print(\"Reject the null hypothesis: Dragon-type Pokémon have significantly higher average HP than Grass-type Pokémon.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis: No significant difference in average HP between Dragon and Grass types.\")"
]
},
{
Expand All @@ -313,11 +341,139 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HP: t-statistic = 8.98, p-value = 0.0000\n",
" -> Significant difference in HP between Legendary and Non-Legendary Pokémon.\n",
"Attack: t-statistic = 10.44, p-value = 0.0000\n",
" -> Significant difference in Attack between Legendary and Non-Legendary Pokémon.\n",
"Defense: t-statistic = 7.64, p-value = 0.0000\n",
" -> Significant difference in Defense between Legendary and Non-Legendary Pokémon.\n",
"Sp. Atk: t-statistic = 13.42, p-value = 0.0000\n",
" -> Significant difference in Sp. Atk between Legendary and Non-Legendary Pokémon.\n",
"Sp. Def: t-statistic = 10.02, p-value = 0.0000\n",
" -> Significant difference in Sp. Def between Legendary and Non-Legendary Pokémon.\n",
"Speed: t-statistic = 11.48, p-value = 0.0000\n",
" -> Significant difference in Speed between Legendary and Non-Legendary Pokémon.\n"
]
}
],
"source": [
"#code here"
"# Separate Legendary and Non-Legendary Pokémon\n",
"legendary = df[df['Legendary'] == True]\n",
"non_legendary = df[df['Legendary'] == False]\n",
"\n",
"# List of stats to compare\n",
"stats = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
"\n",
"# Perform t-tests for each stat\n",
"results = {}\n",
"for stat in stats:\n",
" t_stat, p_value = st.ttest_ind(legendary[stat], non_legendary[stat], equal_var=False) # Welch's t-test\n",
" results[stat] = {'t_stat': t_stat, 'p_value': p_value}\n",
"\n",
"# Print the results\n",
"for stat, result in results.items():\n",
" print(f\"{stat}: t-statistic = {result['t_stat']:.2f}, p-value = {result['p_value']:.4f}\")\n",
" if result['p_value'] < 0.05:\n",
" print(f\" -> Significant difference in {stat} between Legendary and Non-Legendary Pokémon.\")\n",
" else:\n",
" print(f\" -> No significant difference in {stat} between Legendary and Non-Legendary Pokémon.\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Legendary</th>\n",
" <th>Non-Legendary</th>\n",
" <th>Legendary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>HP</th>\n",
" <td>67.182313</td>\n",
" <td>92.738462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Attack</th>\n",
" <td>75.669388</td>\n",
" <td>116.676923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Defense</th>\n",
" <td>71.559184</td>\n",
" <td>99.661538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sp. Atk</th>\n",
" <td>68.454422</td>\n",
" <td>122.184615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sp. Def</th>\n",
" <td>68.892517</td>\n",
" <td>105.938462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Speed</th>\n",
" <td>65.455782</td>\n",
" <td>100.184615</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Legendary Non-Legendary Legendary\n",
"HP 67.182313 92.738462\n",
"Attack 75.669388 116.676923\n",
"Defense 71.559184 99.661538\n",
"Sp. Atk 68.454422 122.184615\n",
"Sp. Def 68.892517 105.938462\n",
"Speed 65.455782 100.184615"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# MOD: mostrar también medias por grupo para contextualizar las diferencias\n",
"import pandas as pd\n",
"stats_cols = [c for c in ['HP','Attack','Defense','Sp. Atk','Sp. Def','Speed'] if c in df.columns]\n",
"if stats_cols:\n",
" means_table = df.groupby('Legendary')[stats_cols].mean().rename(index={True:'Legendary', False:'Non-Legendary'})\n",
" display(means_table.T)\n",
"else:\n",
" print('No se encontraron columnas de estadísticas estándar para mostrar medias.')"
]
},
{
Expand Down Expand Up @@ -483,10 +639,52 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-statistic: 37.992330214201516\n",
"P-value: 3.0064957768592614e-301\n",
"Reject the null hypothesis: Houses close to a school or hospital are significantly more expensive.\n"
]
}
],
"source": [
"# School and hospital coordinates\n",
"school_coords = (-118, 34)\n",
"hospital_coords = (-122, 37)\n",
"\n",
"# Function to calculate Euclidean distance\n",
"def calculate_distance(lat, lon, coords):\n",
" return np.sqrt((lat - coords[1])**2 + (lon - coords[0])**2)\n",
"\n",
"# Calculate distances to school and hospital\n",
"df['distance_to_school'] = calculate_distance(df['latitude'], df['longitude'], school_coords)\n",
"df['distance_to_hospital'] = calculate_distance(df['latitude'], df['longitude'], hospital_coords)\n",
"\n",
"# Determine if a house is close to either a school or hospital\n",
"df['close_to_school_or_hospital'] = (df['distance_to_school'] < 0.50) | (df['distance_to_hospital'] < 0.50)\n",
"\n",
"# Divide the dataset into two groups\n",
"close_group = df[df['close_to_school_or_hospital'] == True]['median_house_value']\n",
"far_group = df[df['close_to_school_or_hospital'] == False]['median_house_value']\n",
"\n",
"# Perform a two-sample t-test\n",
"t_stat, p_value = st.ttest_ind(close_group, far_group, equal_var=False) # Welch's t-test\n",
"\n",
"# Print the results\n",
"print(f\"T-statistic: {t_stat}\")\n",
"print(f\"P-value: {p_value}\")\n",
"\n",
"# Conclusion\n",
"if p_value < 0.05:\n",
" print(\"Reject the null hypothesis: Houses close to a school or hospital are significantly more expensive.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis: No significant difference in house prices.\")"
]
},
{
"cell_type": "code",
Expand All @@ -498,7 +696,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "venv",
"language": "python",
"name": "python3"
},
Expand All @@ -512,7 +710,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.12.2"
}
},
"nbformat": 4,
Expand Down