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250 changes: 237 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": 4,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -278,7 +278,7 @@
"[800 rows x 11 columns]"
]
},
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -297,11 +297,52 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#code here\n",
"#H0: dragon_hp <= grass_hp\n",
"#H1: dragon_hp > grass_hp \n",
"# significance: 0.05\n",
"# one tailed"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#code here"
"df_dragon = df[df[\"Type 1\"]==\"Dragon\"][\"HP\"]\n",
"df_grass = df[df[\"Type 1\"]==\"Grass\"][\"HP\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TtestResult(statistic=np.float64(3.590444254130357), pvalue=np.float64(0.0005135938300306962), df=np.float64(100.0))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.ttest_ind(df_dragon, df_grass)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can reject the null hypothesis, since the pvalue is 0.0005, stadistically lower than our significance value."
]
},
{
Expand All @@ -313,11 +354,55 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"HP\n",
"F_onewayResult(statistic=np.float64(64.5792954533426), pvalue=np.float64(3.330647684845447e-15))\n",
"\n",
"Attack\n",
"F_onewayResult(statistic=np.float64(108.10428446988696), pvalue=np.float64(7.827253003204001e-24))\n",
"\n",
"Defense\n",
"F_onewayResult(statistic=np.float64(51.57020970407507), pvalue=np.float64(1.584222609442392e-12))\n",
"\n",
"Sp. Atk\n",
"F_onewayResult(statistic=np.float64(201.3960102412466), pvalue=np.float64(6.314915770426125e-41))\n",
"\n",
"Sp. Def\n",
"F_onewayResult(statistic=np.float64(121.83194848913693), pvalue=np.float64(1.8439809580406472e-26))\n",
"\n",
"Speed\n",
"F_onewayResult(statistic=np.float64(95.35980155754147), pvalue=np.float64(2.354075443689382e-21))\n"
]
}
],
"source": [
"#code here\n",
"#H0: legendary_pokemons = non_legendary_pokemons\n",
"#H1: legendary_pokemons != non_legendary_pokemons\n",
"# significance: 0.05\n",
"\n",
"stats = [\"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\"]\n",
"\n",
"for stat in stats:\n",
" legendary_pokemon = df[df[\"Legendary\"] == True][stat]\n",
" non_legendary_pokemon = df[df[\"Legendary\"] == False][stat]\n",
"\n",
" print(f\"\\n{stat}\")\n",
" print(st.f_oneway(legendary_pokemon, non_legendary_pokemon))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"#code here"
"We can reject the null hypothesis, for the pvalues are below our significance value."
]
},
{
Expand All @@ -337,7 +422,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -453,7 +538,7 @@
"4 624.0 262.0 1.9250 65500.0 "
]
},
"execution_count": 5,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -483,22 +568,161 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": []
"source": [
"import numpy as np\n",
"\n",
"def euclidean_distance(x1, y1, x2, y2):\n",
" return np.sqrt((x2 - x1)**2 + (y2 - y1)**2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Distance to the school\n",
"df[\"distance_school\"] = df.apply(\n",
" lambda row: euclidean_distance(\n",
" row[\"longitude\"],\n",
" row[\"latitude\"],\n",
" -118,\n",
" 34\n",
" ),\n",
" axis=1\n",
")\n",
"\n",
"# Distance to the hospital\n",
"df[\"distance_hospital\"] = df.apply(\n",
" lambda row: euclidean_distance(\n",
" row[\"longitude\"],\n",
" row[\"latitude\"],\n",
" -122,\n",
" 37\n",
" ),\n",
" axis=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 8.187319\n",
"1 7.966235\n",
"2 8.143077\n",
"3 8.154416\n",
"4 8.183508\n",
" ... \n",
"16995 4.233675\n",
"16996 4.332320\n",
"16997 5.358694\n",
"16998 5.322593\n",
"16999 4.249012\n",
"Name: distance_hospital, Length: 17000, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"distance_hospital\"]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"close\n",
"False 10171\n",
"True 6829\n",
"Name: count, dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"close\"] = (\n",
" (df[\"distance_school\"] < 0.50) |\n",
" (df[\"distance_hospital\"] < 0.50)\n",
")\n",
"\n",
"df[\"close\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#H0: close_houses <= far_houses\n",
"#H1: close_houses > far_houses\n",
"#significance: 0.05"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"close_houses = df[df[\"close\"] == True][\"median_house_value\"]\n",
"far_houses = df[df[\"close\"] == False][\"median_house_value\"]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TtestResult(statistic=np.float64(38.04632342033554), pvalue=np.float64(4.817835891327844e-304), df=np.float64(16998.0))"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.ttest_ind(close_houses, far_houses)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can say that if we were potential real state customers interested in buying a house close to a school or a hospital, this result is statistically significant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -512,7 +736,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.13.9"
}
},
"nbformat": 4,
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