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done #400

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97 changes: 84 additions & 13 deletions lab-hypothesis-testing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 44,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -278,7 +278,7 @@
"[800 rows x 11 columns]"
]
},
"execution_count": 3,
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -297,11 +297,55 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 45,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(np.float64(4.097528915272702), np.float64(0.00010181538122353851))"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 1. Filter groups (using 'Type 1' et 'Type 2')\n",
"hp_dragon = df[(df['Type 1'] == 'Dragon') | (df['Type 2'] == 'Dragon')]['HP']\n",
"hp_grass = df[(df['Type 1'] == 'Grass') | (df['Type 2'] == 'Grass')]['HP']\n",
"\n",
"\n",
"# 2. Run the T-test (assuming the ttest_ind function from scipy.stats)\n",
"# The parameter 'equal_var=False' is often preferred when the sample sizes are different.\n",
"from scipy import stats\n",
"t_stat, p_value_two_sided = stats.ttest_ind(hp_dragon, hp_grass, equal_var=False)\n",
"t_stat, p_value_two_sided \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(5.0907690611769255e-05)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"# 3. Convert the p-value to a one-sided analysis\n",
"p_value_one_sided = p_value_two_sided / 2\n",
"\n",
"p_value_one_sided "
]
},
{
Expand All @@ -313,11 +357,38 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 47,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(array([ 8.98137048, 10.43813354, 7.63707816, 13.41744998, 10.01569661,\n",
" 11.47504445]),\n",
" array([1.00269117e-13, 2.52037245e-16, 4.82699849e-11, 1.55146141e-21,\n",
" 2.29493279e-15, 1.04901631e-18]))"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"#code herestats_legendary = df[df['Legendary'] == True][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
"\n",
"# Filter all rows where 'Legendary' is True\n",
"stats_legendary = df[df['Legendary'] == True][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
"# Filter all rows where 'Legendary' is False\n",
"stats_non_legendary = df[df['Legendary'] == False][['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']]\n",
"\n",
"# The list of stats to test\n",
"stat_columns = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
"\n",
"from scipy import stats\n",
"t_stat, p_value_two_sided = stats.ttest_ind(stats_legendary,stats_non_legendary, equal_var=False)\n",
"t_stat, p_value_two_sided \n",
"\n"
]
},
{
Expand All @@ -337,7 +408,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 48,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -453,7 +524,7 @@
"4 624.0 262.0 1.9250 65500.0 "
]
},
"execution_count": 5,
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -498,7 +569,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -512,7 +583,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.13.5"
}
},
"nbformat": 4,
Expand Down