Skip to content

Probando el análisis de distribución #7

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
wants to merge 2 commits into
base: main
Choose a base branch
from
Open

Probando el análisis de distribución #7

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6541333
Probando el analisis de distribución
Produpro Sep 29, 2023
e9f3100
No encuentro donde se perdieron los decimales
Produpro Sep 29, 2023
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
286 changes: 280 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 +30,253 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 70,
"id": "34720ab6",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Distribución normal: [ 0.76923379 0.5742393 0.61787694 -1.25960609 -1.18796285 -0.51655045\n",
" -1.65295695 0.81234273 -0.27679669 0.67230432 1.76461913 -1.1943756\n",
" -0.71244534 3.39787764 -0.68562495 0.13774652 1.41070156 1.93043831\n",
" -0.09586705 1.39247829 -0.03802866 1.43082466 2.2244982 -1.9689281\n",
" 0.1169756 1.03679649 -1.0399285 0.63399117 1.10422503 1.74412925\n",
" -0.91550008 0.62648192 -0.17457414 -0.28229639 0.7840591 -0.49722555\n",
" -1.1575354 1.77429502 0.22046353 -0.80180236 -1.43093518 0.69026848\n",
" 0.13359328 -1.36046362 0.29375706 -0.26288265 1.17698581 -0.9708398\n",
" 1.32513297 1.082896 0.3021874 -1.20955256 0.93245155 -0.69695085\n",
" 0.95084985 0.33787732 0.74634937 0.12886077 0.86325993 -0.54542479\n",
" -0.17937873 1.24230814 0.98076027 0.916095 0.63076411 -0.76070569\n",
" -0.42935864 -0.51311463 0.6756503 0.33009041 0.95485391 -2.20632037\n",
" 0.02516643 0.64948077 -1.76768963 -0.63422072 -0.07722846 0.75829187\n",
" 0.71745379 0.55177117 0.25772971 -0.58271268 -0.18584666 0.53977674\n",
" -0.28355789 0.51176452 -0.79847977 0.48018215 -1.07691838 0.46770468\n",
" -1.34785359 1.05655776 1.25385879 0.38917759 1.406252 -0.06549675\n",
" 0.73628921 -0.44968933 1.1703974 -0.09216874]\n",
"Distribución chi-cuadrado es con 3 grados de libertad: [ 0.52734956 3.75862327 1.23064711 0.33420303 5.56695447 1.65493478\n",
" 1.52754586 1.12979424 0.68888472 4.08718533 1.32689203 0.4734402\n",
" 1.9726741 1.91599351 4.19902202 4.13897831 0.70665793 4.98771617\n",
" 2.56093374 5.84846077 1.16461339 3.34977895 1.83857623 1.99964518\n",
" 7.71105586 5.47017721 0.19491651 9.75873762 0.68584115 5.37009207\n",
" 3.83258126 1.22061088 4.35337885 0.54429102 2.91844783 1.25637666\n",
" 3.81771828 3.61445735 2.38919776 3.67091969 7.22595453 2.13287835\n",
" 7.99530474 3.54897835 0.41076604 1.93410231 3.77463961 1.89462415\n",
" 2.01111769 8.42892382 0.93666622 3.87649666 0.60145367 2.4198831\n",
" 0.63074142 1.10793658 4.70275238 2.14315906 2.71522951 3.40225268\n",
" 4.85752249 3.19008161 0.87886036 0.34523186 0.30377644 0.59075496\n",
" 6.97172931 10.4593847 0.14463328 2.90834733 6.76047497 1.87252829\n",
" 1.27567564 2.73383529 1.03768819 0.28205652 0.33869548 5.32293204\n",
" 1.47128429 8.33161889 2.60983373 0.66286337 3.43755294 0.06897339\n",
" 1.82848917 5.68503521 1.62661971 3.13693646 0.74205916 3.20443731\n",
" 0.84648486 1.88896345 0.25787453 0.8510519 3.15699978 0.8451778\n",
" 0.44442569 7.51093113 0.45148601 2.43991118]\n"
]
}
],
"source": [
"# TODO"
"import numpy as np\n",
"normal = np.random.normal(0,1,size=100)\n",
"chi_cuadrado = np.random.chisquare(3,100)\n",
"print (f'Distribución normal: {normal}')\n",
"print (f'Distribución chi-cuadrado es con 3 grados de libertad: {chi_cuadrado}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "d47d8423",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Media normal: 0.18457649755684793\n",
"Media Chi_cuadrado: 2.7746538452487592\n"
]
}
],
"source": [
"#Medidas de tendencia central\n",
"#Media\n",
"\n",
"import statistics as stats\n",
"\n",
"print(f'Media normal: {stats.mean(normal)}')\n",
"print(f'Media Chi_cuadrado: {stats.mean(chi_cuadrado)}')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "4097877c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mediana normal: 0.29797223026352515\n",
"Mediana Chi_cuadrado: 2.005381435350604\n"
]
}
],
"source": [
"#Medidas de tendencia central\n",
"#Mediana\n",
"\n",
"print(f'Mediana normal: {stats.median(normal)}')\n",
"print(f'Mediana Chi_cuadrado: {stats.median(chi_cuadrado)}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "3a6a80fd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Moda normal: 0.7692337884640131\n",
"Moda Chi_cuadrado: 0.5273495575894294\n"
]
}
],
"source": [
"#Medidas de tendencia central\n",
"#Moda\n",
"\n",
"print(f'Moda normal: {stats.mode(normal)}')\n",
"print(f'Moda Chi_cuadrado: {stats.mode(chi_cuadrado)}')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "e4762bf8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rango normal: 5.60419800612307\n",
"Rango chi-cuadrado: [ 0.52734956 3.75862327 1.23064711 0.33420303 5.56695447 1.65493478\n",
" 1.52754586 1.12979424 0.68888472 4.08718533 1.32689203 0.4734402\n",
" 1.9726741 1.91599351 4.19902202 4.13897831 0.70665793 4.98771617\n",
" 2.56093374 5.84846077 1.16461339 3.34977895 1.83857623 1.99964518\n",
" 7.71105586 5.47017721 0.19491651 9.75873762 0.68584115 5.37009207\n",
" 3.83258126 1.22061088 4.35337885 0.54429102 2.91844783 1.25637666\n",
" 3.81771828 3.61445735 2.38919776 3.67091969 7.22595453 2.13287835\n",
" 7.99530474 3.54897835 0.41076604 1.93410231 3.77463961 1.89462415\n",
" 2.01111769 8.42892382 0.93666622 3.87649666 0.60145367 2.4198831\n",
" 0.63074142 1.10793658 4.70275238 2.14315906 2.71522951 3.40225268\n",
" 4.85752249 3.19008161 0.87886036 0.34523186 0.30377644 0.59075496\n",
" 6.97172931 10.4593847 0.14463328 2.90834733 6.76047497 1.87252829\n",
" 1.27567564 2.73383529 1.03768819 0.28205652 0.33869548 5.32293204\n",
" 1.47128429 8.33161889 2.60983373 0.66286337 3.43755294 0.06897339\n",
" 1.82848917 5.68503521 1.62661971 3.13693646 0.74205916 3.20443731\n",
" 0.84648486 1.88896345 0.25787453 0.8510519 3.15699978 0.8451778\n",
" 0.44442569 7.51093113 0.45148601 2.43991118]\n"
]
}
],
"source": [
"#Medidas de dispersión\n",
"#Rango\n",
"\n",
"rango_normal = max(normal) - min(normal)\n",
"rango_chi = max(chi_cuadrado) - min(chi_cuadrado)\n",
"\n",
"print(f'Rango normal: {rango_normal}')\n",
"print(f'Rango chi-cuadrado: {chi_cuadrado}')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "88bdb0a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Varianza normal: 0.998433488126238\n",
"Desviación estandar normal: 0.9992164370776924\n",
"Varianza chi-cuadrado: 5.601732482368567\n",
"Desviación estandar chi-cuadrado: 2.3667979386438054\n"
]
}
],
"source": [
"#Medidas de dispersión\n",
"#Varianza\n",
"\n",
"print(f'Varianza normal: {stats.variance(normal)}')\n",
"print(f'Desviación estandar normal: {stats.stdev(normal)}')\n",
"print(f'Varianza chi-cuadrado: {stats.variance(chi_cuadrado)}')\n",
"print(f'Desviación estandar chi-cuadrado: {stats.stdev(chi_cuadrado)}')"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "016feb6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Asimetría normal: 0.02516110971230026\n",
"asimetria chi: 1.1786899662470063\n"
]
}
],
"source": [
"#Medidas de forma\n",
"#Asimetría\n",
"\n",
"from scipy.stats import skew\n",
"asimetría_normal = skew(normal)\n",
"asimetria_chi = skew(chi_cuadrado)\n",
"\n",
"print(f\"Asimetría normal: {asimetría_normal}\")\n",
"print(f\"asimetria chi: {asimetria_chi}\")"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "02724eb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"kurtosis normal: 0.11269082692295251\n",
"kurtosis chi: 0.8735969479986272\n"
]
}
],
"source": [
"#Medidas de forma\n",
"#Curtosis\n",
"\n",
"from scipy.stats import kurtosis\n",
"kurtosis_normal = kurtosis(normal)\n",
"kurtosis_chi = kurtosis(chi_cuadrado)\n",
"\n",
"print(f\"kurtosis normal: {kurtosis_normal}\")\n",
"print(f\"kurtosis chi: {kurtosis_chi}\")\n"
]
},
{
Expand All @@ -54,12 +295,45 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 100,
"id": "d590308e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0\n",
"2.23606797749979\n"
]
}
],
"source": [
"# TODO"
"import math \n",
"import statistics as stats\n",
"data = [4,2,5,8,6]\n",
"\n",
"def calculo_desviacon(data):\n",
" mean = sum(data) / len(data)\n",
" dev = []\n",
" for i in data:\n",
" x = (i - mean)**2\n",
" dev.append(x)\n",
" y = sum(dev)/len(data)\n",
" \n",
" \n",
" \n",
" return math.sqrt(y) \n",
" \n",
" \n",
"print(calculo_desviacon(data))\n",
"\n",
"print(stats.stdev(data))\n",
"\n",
"\n",
" \n",
"\n",
"\n"
]
}
],
Expand Down