From 65413336d055bf0c2506445a8b1ab548d84f11ef Mon Sep 17 00:00:00 2001 From: Produpro <136916627+Produpro@users.noreply.github.com> Date: Fri, 29 Sep 2023 17:02:47 +0000 Subject: [PATCH 1/2] =?UTF-8?q?Probando=20el=20analisis=20de=20distribuci?= =?UTF-8?q?=C3=B3n?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebook/problems.ipynb | 268 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 262 insertions(+), 6 deletions(-) diff --git a/notebook/problems.ipynb b/notebook/problems.ipynb index ff2c594b..fa09d147 100644 --- a/notebook/problems.ipynb +++ b/notebook/problems.ipynb @@ -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" ] }, { @@ -54,12 +295,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 100, "id": "d590308e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Desviasión estandar: 2.23606797749979\n" + ] + } + ], "source": [ - "# TODO" + "import statistics as stats\n", + "\n", + "data = [4,2,5,8,6]\n", + "\n", + "def desviasion_standar(datos):\n", + " return stats.stdev(datos)\n", + "\n", + "print(f'Desviasión estandar: {desviasion_standar(data)}')\n" ] } ], From e9f310024f434c200ed6c8454d4e24313e7e7a04 Mon Sep 17 00:00:00 2001 From: Produpro <136916627+Produpro@users.noreply.github.com> Date: Fri, 29 Sep 2023 19:44:07 +0000 Subject: [PATCH 2/2] No encuentro donde se perdieron los decimales --- notebook/problems.ipynb | 28 +++++++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/notebook/problems.ipynb b/notebook/problems.ipynb index fa09d147..a91702d0 100644 --- a/notebook/problems.ipynb +++ b/notebook/problems.ipynb @@ -303,19 +303,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "Desviasión estandar: 2.23606797749979\n" + "2.0\n", + "2.23606797749979\n" ] } ], "source": [ + "import math \n", "import statistics as stats\n", - "\n", "data = [4,2,5,8,6]\n", "\n", - "def desviasion_standar(datos):\n", - " return stats.stdev(datos)\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", - "print(f'Desviasión estandar: {desviasion_standar(data)}')\n" + " \n", + "\n", + "\n" ] } ],