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solved lab #404

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154 changes: 140 additions & 14 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 +297,56 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#code here"
"#In the first place I create a serie with the data of HP per type of pokemon\n",
"\n",
"dragon_hp = df[df['Type 1'] == 'Dragon']['HP']\n",
"grass_hp = df[df['Type 1'] == 'Grass']['HP']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are comparing two means of 2 different populations i will use Two Sample T-test\n",
"\n",
"#### Hypothesis:\n",
"\n",
"H0: mu_hp dragon = mu_hp grass\n",
"\n",
"H1: mu_hp dragon != mu_hp grass\n",
"\n",
"*significance level = 0.05*"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TtestResult(statistic=np.float64(3.3349632905124063), pvalue=np.float64(0.0015987219490841197), df=np.float64(50.83784116232685))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.ttest_ind(dragon_hp,grass_hp, equal_var=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The p-value is smaller than 0.05 so there is significant difference between the two means of each group. So we reject the null hypothesis"
]
},
{
Expand All @@ -313,11 +358,40 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#code here"
"cols = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
"\n",
"legendary_pok = df[df['Legendary'] == True][cols]\n",
"non_legendary_pok = df[df['Legendary'] == False][cols]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = [ 8.03612441 10.39732102 7.18124012 14.19140621 11.03775106 9.76523433] p = [3.33064768e-15 7.82725300e-24 1.58422261e-12 6.31491577e-41\n",
" 1.84398096e-26 2.35407544e-21]\n"
]
}
],
"source": [
"t_stat, p_value = st.ttest_ind(legendary_pok, non_legendary_pok)\n",
"print(\"t =\", t_stat, \"p =\", p_value)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we compared all the stats for all of them the p value is way to small compared to 0.05. We can say that the stats are different between legendary and non legendary.\n"
]
},
{
Expand All @@ -337,7 +411,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 16,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -453,7 +527,7 @@
"4 624.0 262.0 1.9250 65500.0 "
]
},
"execution_count": 5,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -483,17 +557,69 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": []
"source": [
"import numpy as np\n",
"\n",
"\n",
"def euclidean_distance(x1, y1, x2, y2):\n",
" return np.sqrt((x1 - x2)**2 + (y1 - y2)**2)\n",
"\n",
"\n",
"df[\"dist_school\"] = euclidean_distance(df[\"longitude\"], df[\"latitude\"], -118, 34)\n",
"df[\"dist_hospital\"] = euclidean_distance(df[\"longitude\"], df[\"latitude\"], -122, 37)\n",
"\n",
"df[\"dist_closest\"] = df[\"dist_school\"]\n",
"df.loc[df[\"dist_hospital\"] < df[\"dist_school\"], \"dist_closest\"] = df[\"dist_hospital\"]\n",
"\n",
"close = df[df[\"dist_closest\"] < 0.5]\n",
"far = df[df[\"dist_closest\"] >= 0.5]\n",
"\n",
"close_prices = close[\"median_house_value\"]\n",
"far_prices = far[\"median_house_value\"]\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The hypothesis that we set is:\n",
"\n",
"H0= house price close = house price far \n",
"\n",
"H1 = house price close != house price far"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"TtestResult(statistic=np.float64(37.992330214201516), pvalue=np.float64(3.0064957768592614e-301), df=np.float64(14571.229910954282))"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"st.ttest_ind(close_prices,far_prices, equal_var=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The p value is extremely low so we can say that is a huge diference of price if the house is close to a hospital or a school."
]
}
],
"metadata": {
Expand All @@ -512,7 +638,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.13.6"
}
},
"nbformat": 4,
Expand Down