Skip to content

[lab-goodfit-indeptests] Carlos Silva #155

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
Patchila wants to merge 1 commit into
base: master
Choose a base branch
from
Open

[lab-goodfit-indeptests] Carlos Silva #155

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
86 changes: 75 additions & 11 deletions your_code/main.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 25,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fail to reject the null hypothesis. The data follows a Poisson distribution.\n"
]
}
],
"source": [
"# your answer here"
"import numpy as np\n",
"from scipy.stats import poisson, chi2\n",
"\n",
"observed_frequencies = np.array([35, 99, 104, 110, 62, 25, 10, 3])\n",
"total_times = 448\n",
"mean_parameter = np.sum(np.arange(len(observed_frequencies)) * observed_frequencies) / total_times\n",
"expected_frequencies = poisson.pmf(np.arange(len(observed_frequencies)), mean_parameter) * total_times\n",
"chi_squared_statistic = np.sum((observed_frequencies - expected_frequencies)**2 / expected_frequencies)\n",
"degrees_of_freedom = len(observed_frequencies) - 1\n",
"critical_value = chi2.ppf(0.95, degrees_of_freedom)\n",
"\n",
"\n",
"if chi_squared_statistic > critical_value:\n",
" print(\"Reject the null hypothesis. The data does not follow a Poisson distribution.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis. The data follows a Poisson distribution.\")"
]
},
{
Expand Down Expand Up @@ -60,11 +83,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 26,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reject the null hypothesis. The data does not fit a binomial distribution.\n"
]
}
],
"source": [
"# your answer here"
"observed_frequencies = np.array([138, 53, 9])\n",
"defective_item_counts = np.array([0, 1, 2])\n",
"\n",
"\n",
"total_samples = np.sum(observed_frequencies)\n",
"p = 0.05\n",
"expected_frequencies = np.array([total_samples * (1 - p)**2, total_samples * 2 * p * (1 - p), total_samples * p**2])\n",
"chi_squared_statistic = np.sum((observed_frequencies - expected_frequencies)**2 / expected_frequencies)\n",
"degrees_of_freedom = len(observed_frequencies) - 1\n",
"critical_value = chi2.ppf(0.95, degrees_of_freedom)\n",
"\n",
"\n",
"if chi_squared_statistic > critical_value:\n",
" print(\"Reject the null hypothesis. The data does not fit a binomial distribution.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis. The data fits a binomial distribution.\")"
]
},
{
Expand All @@ -79,17 +125,35 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 27,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reject the null hypothesis. There is a significant association between patterns of physical activity and sugary drink consumption.\n"
]
}
],
"source": [
"#your answer here"
"from scipy.stats import chi2_contingency\n",
"\n",
"\n",
"observed_frequencies = np.array([[32, 14, 6],\n",
" [12, 22, 9]])\n",
"chi2, p, dof, expected = chi2_contingency(observed_frequencies)\n",
"alpha = 0.05\n",
"if p < alpha:\n",
" print(\"Reject the null hypothesis. There is a significant association between patterns of physical activity and sugary drink consumption.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis. There is no significant association between patterns of physical activity and sugary drink consumption.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -103,7 +167,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
"version": "3.10.9"
}
},
"nbformat": 4,
Expand Down