Skip to content

Instantly share code, notes, and snippets.

@yogeshchappewar

yogeshchappewar/Confidence Interval.ipynb

Created January 17, 2023 09:03
Show Gist options
  • Save yogeshchappewar/86460817767b9a82763059aca2037c3a to your computer and use it in GitHub Desktop.
Save yogeshchappewar/86460817767b9a82763059aca2037c3a to your computer and use it in GitHub Desktop.
Download ZIP
Confidence Interval
Display the source blob
Display the rendered blob
Raw
Confidence Interval.ipynb
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.276322Z",
"end_time": "2023-01-16T11:56:42.292582Z"
},
"trusted": true
},
"cell_type": "code",
"source": "from scipy import stats\nimport pandas as pd\nimport numpy as np\n",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.292582Z",
"end_time": "2023-01-16T11:56:42.325748Z"
},
"trusted": true
},
"cell_type": "code",
"source": "glaxo_df=pd.read_csv('glaxo_df.csv')\nprint(glaxo_df) ",
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": " Date Date.1 Close gain\n0 2010-01-05 2010-01-05 1616.80 -0.005444\n1 2010-01-06 2010-01-06 1638.50 0.013422\n2 2010-01-07 2010-01-07 1648.70 0.006225\n3 2010-01-08 2010-01-08 1639.80 -0.005398\n4 2010-01-11 2010-01-11 1629.45 -0.006312\n... ... ... ... ...\n1733 2016-12-26 2016-12-26 2723.50 -0.001283\n1734 2016-12-27 2016-12-27 2701.75 -0.007986\n1735 2016-12-28 2016-12-28 2702.15 0.000148\n1736 2016-12-29 2016-12-29 2727.90 0.009529\n1737 2016-12-30 2016-12-30 2729.80 0.000697\n\n[1738 rows x 4 columns]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.327462Z",
"end_time": "2023-01-16T11:56:42.340862Z"
},
"trusted": true
},
"cell_type": "code",
"source": "glaxo_df_ci = stats.norm.interval(0.95,\nglaxo_df.gain.mean(), glaxo_df.gain.std())\nprint( 'Gain at 95% confidence interval is:', np.round(glaxo_df_ci, 4)) ",
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": "Gain at 95% confidence interval is: [-0.0258 0.0266]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:58:33.376953Z",
"end_time": "2023-01-16T11:58:33.393451Z"
},
"trusted": true
},
"cell_type": "code",
"source": "glaxo_df_ci = stats.norm.interval(0.90,glaxo_df.gain.mean(), glaxo_df.gain.std())\nprint( 'Gain at 90% confidence interval is:', np.round(glaxo_df_ci, 4)) ",
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": "Gain at 90% confidence interval is: [-0.0216 0.0224]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:58:51.677889Z",
"end_time": "2023-01-16T11:58:51.693767Z"
},
"trusted": true
},
"cell_type": "code",
"source": "glaxo_df_ci = stats.norm.interval(0.99,glaxo_df.gain.mean(), glaxo_df.gain.std())\nprint( 'Gain at 99% confidence interval is:', np.round(glaxo_df_ci, 4)) ",
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": "Gain at 99% confidence interval is: [-0.034 0.0348]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T12:00:00.826738Z",
"end_time": "2023-01-16T12:00:00.859057Z"
},
"trusted": true
},
"cell_type": "code",
"source": "beml_df=pd.read_csv('beml_df.csv')\n\nbeml_df_ci = stats. norm.interval(0.95,beml_df.gain.mean(),beml_df.gain.std())\nprint( 'Gain at 95% confidence interval is:', np.round(beml_df_ci, 4)) ",
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": "Gain at 95% confidence interval is: [-0.0515 0.0521]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.428487Z",
"end_time": "2023-01-16T11:56:42.457732Z"
},
"trusted": true
},
"cell_type": "code",
"source": "# to find z-score value\nstats.norm.ppf(0.975)",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": "1.959963984540054"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.460250Z",
"end_time": "2023-01-16T11:56:42.474207Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.norm.ppf(0.995) ",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": "2.5758293035489004"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.476731Z",
"end_time": "2023-01-16T11:56:42.490260Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.norm.ppf(0.95) ",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": "1.6448536269514722"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.495946Z",
"end_time": "2023-01-16T11:56:42.506594Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.norm.ppf(0.90) ",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 16,
"data": {
"text/plain": "1.2815515655446004"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.507932Z",
"end_time": "2023-01-16T11:56:42.529037Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.t.ppf(0.975,139) ",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": "1.977177724476122"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.531608Z",
"end_time": "2023-01-16T11:56:42.540036Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.t.interval(0.95,1737 ,glaxo_df.gain.mean() , glaxo_df.gain.std()) ",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": "(-0.025818392673533995, 0.026590474838718584)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2023-01-16T11:56:42.542132Z",
"end_time": "2023-01-16T11:56:42.555836Z"
},
"trusted": true
},
"cell_type": "code",
"source": "stats.t.interval(0.95,1737 ,beml_df.gain.mean() , beml_df.gain.std()) ",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": "(-0.05156885177136771, 0.052110347929482194)"
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3 (ipykernel)",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.9.13",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "",
"data": {
"description": "Confidence Interval",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment