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369ec06
Add some examples
PedroBSB Sep 27, 2023
a255f52
added julio work folder and all done with the excercies
bobo305 Sep 29, 2023
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176 changes: 176 additions & 0 deletions JULIOs_work/problems.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "ac622319",
"metadata": {},
"source": [
"# Descriptive statistics problems"
]
},
{
"cell_type": "markdown",
"id": "aa8993e4",
"metadata": {},
"source": [
"### Exercise 1"
]
},
{
"cell_type": "markdown",
"id": "5e0ab0d5",
"metadata": {},
"source": [
"We will use Numpy to obtain information to describe statistically.\n",
"\n",
"- Generate an array of 100 elements following a normal distribution.\n",
"- Generate an array of 100 elements following a chi-square distribution with 3 degrees of freedom.\n",
"- Calculate the main metrics and statistical measures that best describe the two vectors."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "34720ab6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stats metrics for normal disturbution\n",
"mean: -0.06865936071598043\n",
"stander deviation: 0.9697609573818616\n",
"meadian: -0.12619537311221996\n",
"min: -1.9361951382884228\n",
"max: 2.277103875562215\n",
"variances: 0.9404363144621848\n",
"\n",
"stats metrics for chi square disturbution\n",
"mean: 3.14695578992872\n",
"stander deviation: 2.467973134872231\n",
"meadian: 2.1340973003611277\n",
"min: 0.18770455963586785\n",
"max: 10.0705079466792\n",
"variances: 6.0908913944510665\n"
]
}
],
"source": [
"# TODO\n",
"import numpy as np \n",
"\n",
"normal_distribution= np.random.normal(loc=0, scale=1,size=100)\n",
"\n",
"chi_square_distribution=np.random.chisquare(df=3,size=100)\n",
"\n",
"mean_normal=np.mean(normal_distribution)\n",
"std_normal=np.std(normal_distribution)\n",
"meadian_normal=np.median(normal_distribution)\n",
"min_normal=np.min(normal_distribution)\n",
"max_normal=np.max(normal_distribution)\n",
"variances_normal=np.var(normal_distribution)\n",
"\n",
"\n",
"\n",
"mean_chi_square=np.mean(chi_square_distribution)\n",
"std_chi_square=np.std(chi_square_distribution)\n",
"meadian_chi_square=np.median(chi_square_distribution)\n",
"min_chi_square=np.min(chi_square_distribution)\n",
"max_chi_square=np.max(chi_square_distribution)\n",
"variances_chi_square=np.var(chi_square_distribution)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"print(\"stats metrics for normal disturbution\")\n",
"print(\"mean:\",mean_normal)\n",
"print(\"stander deviation:\",std_normal)\n",
"print(\"meadian:\",meadian_normal)\n",
"print(\"min:\",min_normal)\n",
"print(\"max:\",max_normal)\n",
"print(\"variances:\",variances_normal)\n",
"\n",
"print(\"\")\n",
"\n",
"print(\"stats metrics for chi square disturbution\")\n",
"print(\"mean:\",mean_chi_square)\n",
"print(\"stander deviation:\",std_chi_square)\n",
"print(\"meadian:\",meadian_chi_square)\n",
"print(\"min:\",min_chi_square)\n",
"print(\"max:\",max_chi_square)\n",
"print(\"variances:\",variances_chi_square)"
]
},
{
"cell_type": "markdown",
"id": "46c70c3d",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Write a Python program to calculate the standard deviation of the following data:\n",
"\n",
"```py\n",
"data = [4, 2, 5, 8, 6]\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d590308e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stander diviation is : 2.0\n"
]
}
],
"source": [
"# TODO\n",
"import numpy as np\n",
"\n",
"data = [4, 2, 5, 8, 6]\n",
"\n",
"mean_value=np.mean(data)\n",
"\n",
"squared_diffrince=[(x - mean_value)** 2 for x in data]\n",
"\n",
"mean_squared_diffrence=np.mean(squared_diffrince)\n",
"\n",
"std=np.sqrt(mean_squared_diffrence)\n",
"\n",
"print(\"stander diviation is :\", std)"
]
}
],
"metadata": {
"interpreter": {
"hash": "9248718ffe6ce6938b217e69dbcc175ea21f4c6b28a317e96c05334edae734bb"
},
"kernelspec": {
"display_name": "Python 3.9.12 ('ML-BOOTCAMP')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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