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[lab-goodfit-indeptests]Ricardo Mendes #147

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147 changes: 138 additions & 9 deletions your_code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -12,13 +12,77 @@
"Based on these results, we create a Poisson distribution with the sample mean parameter = 2.435. Is there any reason to believe that at a .05 level the number of scores is a Poisson variable?"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# your answer here\n",
"\n",
"\n",
"from scipy.stats import poisson\n",
"import numpy as np\n",
"import scipy.stats as st\n",
"\n",
"from scipy.stats import binom"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can not reject the null hypothesis\n",
"p-value: 0.4836889068537269\n"
]
}
],
"source": [
"# H0: The number of scores is a Poisson variable (with a sample mean parameter = 2.435)\n",
"# H1: he number of scores is not a Poisson variable (with a sample mean parameter = 2.435)\n",
"\n",
"O = np.array([35, 99, 104, 110, 62, 25, 10, 3])\n",
"\n",
"population = O.sum() # population total\n",
"\n",
"mean = 2.435 # sample mean\n",
"\n",
"alpha = 0.05\n",
"\n",
"poisson_dist = poisson(mean)\n",
"\n",
"poisson_pmfs = np.array([poisson_dist.pmf(i) for i in range(0, 7)])\n",
"\n",
"tail = 1 - poisson_pmfs.sum()\n",
"\n",
"poisson_with_tail = np.append(poisson_pmfs, tail)\n",
"\n",
"E = poisson_with_tail * population\n",
"\n",
"chisquare_result = st.chisquare(f_obs = O, f_exp = E)\n",
"\n",
"if chisquare_result.pvalue < alpha:\n",
" print(\"We can reject the null hypothesis\")\n",
"else:\n",
" print(\"We can not reject the null hypothesis\")\n",
" \n",
"print(\"p-value:\", chisquare_result.pvalue)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# your answer here"
"\"\"\"\n",
"There is reason to believe that the number of scores is a Poisson variable\n",
"\"\"\""
]
},
{
Expand Down Expand Up @@ -60,11 +124,49 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can reject the null hypothesis\n",
"p-value: 0.015715783395950887\n"
]
}
],
"source": [
"# your answer here"
"# your answer here\n",
"\n",
"# H0: The sample comes from a binomial population (with n = 10 and p = 0.05)\n",
"# H1: The sample does not come from a binomial population (with n = 10 and p = 0.05)\n",
"\n",
"O = np.array([138, 53, 9])\n",
"\n",
"population = O.sum()\n",
"n = 10\n",
"p = 0.05\n",
"alpha = 0.05 # assume\n",
"\n",
"binom_dist = binom(n, p)\n",
"\n",
"binom_pmfs = np.array([binom_dist.pmf(i) for i in range(0, 2)])\n",
"\n",
"tail = 1 - binom_pmfs.sum()\n",
"\n",
"binom_with_tail = np.append(binom_pmfs, tail)\n",
"\n",
"E = binom_with_tail * population\n",
"\n",
"chisquare_result = st.chisquare(f_obs = O, f_exp = E)\n",
"\n",
"if chisquare_result.pvalue < alpha:\n",
" print(\"We can reject the null hypothesis\")\n",
"else:\n",
" print(\"We can not reject the null hypothesis\")\n",
" \n",
"print(\"p-value:\", chisquare_result.pvalue)"
]
},
{
Expand All @@ -79,17 +181,44 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We can reject the null hypothesis\n",
"p-value: 0.004719280137040844\n"
]
}
],
"source": [
"#your answer here"
"#your answer here\n",
"\n",
"# H0: there is no association between patterns of physical activity and the consumption of sugary drinks\n",
"# H1: there is an association between patterns of physical activity and the consumption of sugary drinks\n",
"\n",
"alpha = 0.05\n",
"\n",
"activity = np.array([[32, 12], \n",
" [14, 22], \n",
" [6, 9]])\n",
"\n",
"chiscquare_contigency_result = st.chi2_contingency(activity)\n",
"\n",
"if chiscquare_contigency_result.pvalue < alpha:\n",
" print(\"We can reject the null hypothesis\")\n",
"else:\n",
" print(\"We can not reject the null hypothesis\")\n",
" \n",
"print(\"p-value:\", chiscquare_contigency_result.pvalue)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -103,7 +232,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down