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[lab-goodfit-indeptests] yara miranda #170

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[lab-goodfit-indeptests] yara miranda #170

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123 changes: 115 additions & 8 deletions your_code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,45 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=191.93184027673232, pvalue=5.85583627060059e-38)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"# your answer here"
"#In this case, the p-value is extremely small (close to zero), which is significantly less than 0.05. Therefore, at a 95% confidence level, we reject the null hypothesis. There is strong evidence to suggest that the variables are not independent.\n",
"\n",
"#In conclusion, based on the chi-squared test, there is a significant relationship between the variables in the contingency table."
]
},
{
Expand All @@ -39,6 +73,13 @@
"Does the distribution of defective items follow this distribution?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -60,11 +101,35 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Power_divergenceResult(statistic=336.43955678670346, pvalue=8.771593494342625e-74)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your answer here"
"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)"
]
},
{
Expand All @@ -79,12 +144,54 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0047192801370408155"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"#your answer here"
"#don't reject hypothesis test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -103,7 +210,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.11.5"
}
},
"nbformat": 4,
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