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Ejercicio de estadistica resuelto #8

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236 changes: 230 additions & 6 deletions notebook/problems.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,12 +30,213 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"id": "34720ab6",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vector normal: [-0.13981665 3.27642772 0.41495447 -0.94947911 0.01230883 -1.59841484\n",
" 0.14503588 -0.18295834 -1.1813453 0.13567271 1.2245349 0.82177615\n",
" -0.35559264 0.16886222 -0.7799625 1.03570004 -0.80818192 -0.40033731\n",
" -1.41779659 0.40061763 -0.49412013 -0.47741689 -0.57009159 0.14137868\n",
" 0.00633298 -0.16271211 -0.83820593 -1.01545907 -1.62566956 -0.06404521\n",
" 0.38777274 0.21552142 -0.6303858 -0.58360702 1.13908369 -0.44960161\n",
" -0.29557928 -0.17653418 0.21395149 -0.76213997 -1.02575906 -0.81069971\n",
" -2.11160462 -0.61151094 0.08388871 0.05529554 -0.09173184 -0.06682591\n",
" 0.6788379 2.06377681 0.57038307 0.74668228 -0.74707044 0.8853769\n",
" -2.00151533 0.96468643 -2.09877542 -0.13495309 0.17394017 1.8438733\n",
" -0.63025428 -0.45991567 -0.59596786 0.83649136 0.0980591 0.83771535\n",
" 0.57932802 0.94648941 -1.706124 -1.10103427 2.40741733 -2.73921832\n",
" -0.70693843 -0.29019909 0.14051028 -0.46095132 0.9631452 0.22349802\n",
" 1.41602007 0.09541905 -0.73212559 -0.65620409 1.19966057 0.97902427\n",
" -0.14403832 -0.29102366 0.63833587 -0.63823059 -0.99211308 -0.43178822\n",
" -0.52174175 1.10765967 -0.39172841 -0.86042976 0.7612823 -0.62582515\n",
" -1.59349533 -0.73977405 1.0780916 0.58353696]\n",
"Vector chi: [ 2.79776252 15.4188415 0.75161486 0.31586758 1.2367305 3.07778272\n",
" 3.60184995 2.30326312 2.1318715 1.48597231 3.81263391 3.33445484\n",
" 0.9749434 3.3892638 1.46390446 0.75605669 4.00811586 1.78076159\n",
" 3.69396446 1.39294758 6.10014106 5.36188253 2.70758433 8.10727245\n",
" 7.37455107 0.70801959 1.92914718 3.37949774 1.96363066 1.11308871\n",
" 3.30499427 5.01814896 7.966394 3.33836864 3.5035974 4.77611806\n",
" 2.82355169 2.64530565 6.21148077 6.55519548 1.48031313 0.9201735\n",
" 2.67083777 4.90577331 6.08431492 3.48696815 6.83064601 2.81134485\n",
" 0.74830845 2.14202517 2.00581818 2.02545146 6.89109992 1.47392385\n",
" 1.23229621 7.07083325 4.27351681 0.54460691 0.04985409 1.15398384\n",
" 6.65606617 1.93676989 3.13019177 4.71223957 0.22640199 5.1696933\n",
" 2.46104687 2.75671682 3.53390691 3.07540574 2.9381396 1.91524673\n",
" 0.6480549 2.64941911 0.57675636 0.93993087 0.7316045 3.38670708\n",
" 3.37209597 1.17183639 1.9162541 4.12120361 1.15100382 0.75660436\n",
" 0.70507591 3.25794101 6.05599761 6.52134932 3.75157451 0.87006518\n",
" 0.86984476 1.03175059 5.32122735 4.30845073 1.25308723 1.89272559\n",
" 4.35480243 0.72047634 4.07132952 1.45918759]\n"
]
}
],
"source": [
"# TODO"
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"normal = np.random.normal(size=100)\n",
"\n",
"chi_valor = np.random.chisquare(3, 100)\n",
"\n",
"print(f'Vector normal: {normal}')\n",
"print(f'Vector chi: {chi_valor}')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e8eccb84",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Media de valor normal: -0.10270664081657574\n",
"Media de valor chi: 3.0779684523334394\n"
]
}
],
"source": [
"import statistics as stats\n",
"\n",
"#Calculamos la media con la libreria stats\n",
"\n",
"print(f'Media de valor normal: {stats.mean(normal)}')\n",
"print(f'Media de valor chi: {stats.mean(chi_valor)}')\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a198af3f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Media de valor normal: -0.15337521734352497\n",
"Media de valor chi: 2.7321505766767054\n"
]
}
],
"source": [
"#Calculamos la mediana con la libreria stats\n",
"\n",
"print(f'Mediana de valor normal: {stats.median(normal)}')\n",
"print(f'Mediana de valor chi: {stats.median(chi_valor)}')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "30310167",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Media de valor normal: -0.10270664081657574\n",
"Media de valor chi: 3.0779684523334394\n"
]
}
],
"source": [
"#Calculamos la moda con la libreria stats\n",
"\n",
"print(f'Moda del valor normal: {stats.mean(normal)}')\n",
"print(f'Moda del valor chi: {stats.mean(chi_valor)}')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e6011c0f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Varianza del valor normal: 0.9569786967163646\n",
"Desviacion valor normal: 0.9782528797383448\n",
"Varianza del valor chi: 5.527016223019786\n",
"Desviacion valor chi: 2.350960702142804\n"
]
}
],
"source": [
"#Calculamos la varianza y la desviacion con la libreria stats\n",
"\n",
"print(f'Varianza del valor normal: {stats.variance(normal)}')\n",
"print(f'Desviacion valor normal: {stats.stdev(normal)}')\n",
"\n",
"print(f'Varianza del valor chi: {stats.variance(chi_valor)}')\n",
"print(f'Desviacion valor chi: {stats.stdev(chi_valor)}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "26a24398",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cuurtosis del valor normal: 0.33655658137222344\n",
"Curtosis valor chi: 1.8052937930069857\n"
]
}
],
"source": [
"from scipy.stats import kurtosis\n",
"\n",
"#Dentro de la libreria scipy.stats importamos la funcion skew, que nos devolvera la curtosis\n",
"#La curtosis mide la concentracion de valores fuera de la zona central.\n",
"\n",
"curtosis_nomral = kurtosis(normal)\n",
"curtosis_chi = kurtosis(chi_valor)\n",
"\n",
"print(f'Cuurtosis del valor normal: {curtosis_nomral}')\n",
"print(f'Curtosis valor chi: {curtosis_chi}')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c39b3ec9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Asimetria del valor normal: 0.33655658137222344\n",
"Asimetria valor chi: 1.8052937930069857\n"
]
}
],
"source": [
"from scipy.stats import skew\n",
"\n",
"#Dentro de la libreria scipy.stats importamos la funcion skew, que nos devolvera la asimetria\n",
"#La asimetria mide la concentracion de valores en los laterales de la media, diciendonos si son simetricos los datos.\n",
"\n",
"curtosis_nomral = skew(normal)\n",
"curtosis_chi = skew(chi_valor)\n",
"\n",
"print(f'Asimetria del valor normal: {curtosis_nomral}')\n",
"print(f'Asimetria valor chi: {curtosis_chi}')"
]
},
{
Expand All @@ -54,12 +255,35 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 30,
"id": "d590308e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La desviación estandar para los valores introducidos es de: 2.0\n"
]
}
],
"source": [
"# TODO"
"import math\n",
"\n",
"def standar_deviation(valores):\n",
"\n",
" media = sum(valores)/len(valores)\n",
"\n",
" desviacion_suma = 0\n",
"\n",
" for n in valores:\n",
" desviacion_suma += (float(n) - media)**2\n",
"\n",
" desviacion_final = math.sqrt(desviacion_suma/len(valores))\n",
"\n",
" return desviacion_final\n",
"\n",
"print(f'La desviación estandar para los valores introducidos es de: {standar_deviation([4, 2, 5, 8, 6])}')\n"
]
}
],
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7 changes: 2 additions & 5 deletions notebook/solutions.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -346,9 +346,6 @@
],
"source": [
"import math\n",
"import sys\n",
"\n",
"# Define the standard deviation function\n",
"\n",
"def sd_calc(data):\n",
" n = len(data)\n",
Expand Down Expand Up @@ -378,8 +375,8 @@
"\n",
"\n",
"data = [4, 2, 5, 8, 6]\n",
"print(f\"Sample Data: {data}\")\n",
"print(f\"Standard Deviation: {sd_calc(data)}\")"
"print(f\"Datos simple: {data}\")\n",
"print(f\"Derivacion estandar: {sd_calc(data)}\")"
]
}
],
Expand Down