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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
794e489
New changes
PedroBSB Sep 29, 2023
85eb10b
descriptive stats problems
idellinger Oct 2, 2023
6cec258
Questions 1-2 answered
Skcodingacademy Oct 2, 2023
4d67842
1-2 answered
Skcodingacademy Oct 2, 2023
4b0d26c
Another way I found to solve it using stats.stdev
Skcodingacademy Oct 2, 2023
a1094dd
Finished Exercises
Ricoou Oct 2, 2023
4b13e4c
Merge pull request #4 from 4GeeksAcademy/Julio/project
sbarcelona11 Oct 4, 2023
bae64a0
Merge pull request #1 from 4GeeksAcademy/Seth/project
sbarcelona11 Oct 4, 2023
30a804f
Merge pull request #3 from 4GeeksAcademy/Israel
sbarcelona11 Oct 4, 2023
15623ef
Merge pull request #2 from 4GeeksAcademy/Ricardo/project
sbarcelona11 Oct 4, 2023
686f136
Added problems to notebook folder
Rukawuba Oct 5, 2023
2fa212e
Both Problems finished
Rukawuba Oct 20, 2023
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270 changes: 270 additions & 0 deletions Israel/problems.ipynb
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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
}
134 changes: 134 additions & 0 deletions Ricardo/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": 1,
"id": "34720ab6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean of element 1 is 0.059808015534485\n",
"The Standard Deviation of element 1 is 1.0078822447165796\n",
"The mean of element 2 is 3.001840622161374\n",
"The Standard Deviation of element 2 is 2.3916810312486882\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"np.random.seed(0)\n",
"\n",
"elements1 = np.random.normal(size=100)\n",
"elements2 = np.random.chisquare(df=3, size=100)\n",
"\n",
"elements1_mean = np.mean(elements1)\n",
"elements2_mean = np.mean(elements2)\n",
"\n",
"elements1_std = np.std(elements1)\n",
"elements2_std = np.std(elements2)\n",
"\n",
"print(f\"The mean of element 1 is {elements1_mean}\")\n",
"print(f\"The Standard Deviation of element 1 is {elements1_std}\")\n",
"print(f\"The mean of element 2 is {elements2_mean}\")\n",
"print(f\"The Standard Deviation of element 2 is {elements2_std}\")\n"
]
},
{
"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": 2,
"id": "d590308e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Standard Deviation: 2.0\n"
]
}
],
"source": [
"data = [4, 2, 5, 8, 6]\n",
"\n",
"\n",
"mean = sum(data) / len(data)\n",
"\n",
"variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
"\n",
"standard_deviation = variance ** 0.5\n",
"\n",
"print(\"Standard Deviation:\", standard_deviation)"
]
}
],
"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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