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[lab-goodfit-indeptests] Maria Bourbon #161

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145 changes: 134 additions & 11 deletions your_code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,55 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=191.93184027673232, pvalue=5.85583627060059e-38)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your answer here"
"# your answer here\n",
"from scipy.stats import poisson\n",
"from scipy.stats import chisquare\n",
"import numpy as np\n",
"f_obs = np.array([35,99,104,110,62,25,10,3])\n",
"mean = 2.435\n",
"poisson_dist = poisson(mean)\n",
"poisson_pmfs = np.array([poisson_dist.pmf(i) for i in range(1,8)]) \n",
"poisson_pmfs\n",
"with_tail = np.append(poisson_pmfs,1- poisson_pmfs.sum())\n",
"with_tail\n",
"f_exp = with_tail*448\n",
"f_exp\n",
"chisquare(f_exp = f_exp, f_obs = f_obs)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5.85583627060059e-38 < 0.05 #REJECT "
]
},
{
Expand Down Expand Up @@ -60,11 +104,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=336.43955678670346, pvalue=8.771593494342625e-74)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your answer here"
"# your answer here\n",
"from scipy.stats import chisquare\n",
"\n",
"observed_frequencies = [138, 53, 9]\n",
"expected_proportion = 0.05\n",
"total_samples = 200\n",
"categories = 3\n",
"\n",
"expected_probabilities = [expected_proportion**i * (1 - expected_proportion)**(categories - i) for i in range(categories)]\n",
"\n",
"expected_frequencies = [total_samples * prob for prob in expected_probabilities]\n",
"\n",
"expected_frequencies_adjusted = [freq * (total_samples / sum(expected_frequencies)) for freq in expected_frequencies]\n",
"\n",
"chisquare(f_obs=observed_frequencies, f_exp=expected_frequencies_adjusted)\n"
]
},
{
Expand All @@ -79,17 +148,71 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 25,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.0047192801370408155"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#your answer here"
"#your answer here\n",
"import numpy as np\n",
"from scipy.stats import chi2\n",
"f_obs = np.array([[32, 12], [14, 22], [6, 9]])\n",
"\n",
"row_totals = [44,36,15]\n",
"col_totals = [52,43]\n",
"grand_total = [95]\n",
"\n",
"f_exp = np.outer(row_totals, col_totals) / grand_total\n",
"\n",
"chi2_statistic = np.sum((f_obs - f_exp)**2 / f_exp)\n",
"\n",
"degrees_of_freedom = (f_obs.shape[0] - 1) * (f_obs.shape[1] - 1)\n",
"\n",
"p_value = 1 - chi2.cdf(chi2_statistic, degrees_of_freedom)\n",
"p_value"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.0047192801370408155 < 0.05 #Don't Reject"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -103,7 +226,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.11.5"
}
},
"nbformat": 4,
Expand Down