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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "                                          Carlos Andres Pinzon Osorio\n",
+    "                                         Maestria en Ingenieria Fisica\n",
+    "                                                      UAN\n",
+    "                                                Bogota-Colombia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PROYECTO CIENCIA DE DATOS\n",
+    "\n",
+    "Los datos con los cuales se va a trabajar fueron obtenidos de un repositorio en Gitlab.\n",
+    "Cabe aclarar que el archivo contiene datos de cuatro variables que relacionan el individuo, pais de procedencia, el peso y el tamaño. Estos datos aunque no dan una clara razón sobre su objeto de estudio.para este ejercicio voy a tomarlos como información que se obtuvo de diferentes individuos de una especie no especificada que han sido criados en diferentes países con diferentes condiciones de alimentación.\n",
+    "\n",
+    "Para este estudio se tuvo encuenta 32 individuos de Alemania, Inglaterra, Italia y Francia.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Informacion recolectada"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt\n",
+    "import statistics\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para el archivo analisiss.csv genero la lectura de los datos al mismo tiempo que creo una imagen de la tabla de datos para ser usada en el informe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   indviduo      origen  peso  tamano\n",
+      "0     cam01    Alemania    18    26.0\n",
+      "1     cam02    Alemania    38    62.0\n",
+      "2     cam03    Alemania    39    59.0\n",
+      "3     cam04    Alemania    28    41.0\n",
+      "4     cam05    Alemania     7    13.5\n",
+      "5     cam06    Alemania    29    46.5\n",
+      "6     cam07    Alemania    19    25.5\n",
+      "7     cam08    Alemania    25    40.5\n",
+      "8     cam09    Alemania    40    59.0\n",
+      "9     cam10    Alemania    16    19.0\n",
+      "10    tur01  Inglaterra    26    39.0\n",
+      "11    tur02  Inglaterra    17    21.5\n",
+      "12    tur03  Inglaterra    32    49.0\n",
+      "13    tur04  Inglaterra    21    31.5\n",
+      "14    tur05  Inglaterra    21    27.5\n",
+      "15    pet01  Inglaterra     6    11.0\n",
+      "16    pet02      Italia     8    12.0\n",
+      "17    pet03      Italia    28    40.0\n",
+      "18    pet04      Italia    10    15.0\n",
+      "19    pet05      Italia    19    28.5\n",
+      "20    pet06      Italia    13    17.5\n",
+      "21    pet07      Italia    16    29.0\n",
+      "22    pet08      Italia    15    20.5\n",
+      "23    pet09      Italia    36    54.0\n",
+      "24    pet10     Francia    39    53.5\n",
+      "25    tur06     Francia    33    49.5\n",
+      "26    tur07     Francia    31    43.5\n",
+      "27    tur08     Francia     9    15.5\n",
+      "28    tur09     Francia    10    17.0\n",
+      "29    tur10     Francia     5    11.5\n",
+      "30    tur11     Francia    20    26.0\n",
+      "31    tur12     Francia    27    43.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "e= pd.read_csv('analisiss.csv',skiprows=1,sep=';')\n",
+    "print(e)\n",
+    "\n",
+    "e.to_html(\"Datos_Individuos.htm\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "La muestra de individuos que se tomo de cada pais\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Cantidad de Individuos\n",
+      "Alemania                        10\n",
+      "Francia                          8\n",
+      "Inglaterra                       6\n",
+      "Italia                           8\n"
+     ]
+    }
+   ],
+   "source": [
+    "import collections\n",
+    "poblacion=np.asarray(e)\n",
+    "pais=poblacion[:,1]\n",
+    "individuos= collections.Counter(pais)\n",
+    "\n",
+    "df = pd.DataFrame({'Cantidad de Individuos' : individuos})  \n",
+    "print(df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analisis por Pais"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para cada uno de paises de referencia se construye una grafica de dispersion con el fin de obtener una expresion que permita describir la tendencia de cada grupo de individuos en cada lugar  y ademas predecir el comportamiento para cualquier peso en funcion de la estatura."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alemania=e[0:8]\n",
+    "ale=np.array(alemania)\n",
+    "\n",
+    "inglaterra=e[10:16]\n",
+    "ing=np.array(inglaterra)\n",
+    "\n",
+    "italia=e[16:24]\n",
+    "ita=np.array(italia)\n",
+    "\n",
+    "francia=e[24:32]\n",
+    "fra=np.array(francia)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Alemania\n",
+    "\n",
+    "La poblacion que se analizo en Alemania es de 10 individuos.\n",
+    "\n",
+    "cuyo peso varia dentro del intervalo de 7 a 39 Newtons o 0.7 a 3.9 Kg.\n",
+    "\n",
+    "El tamano refiriendose a la altura oscila entre los 13 a 62 cm.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se establece una relacion lineal entre los datos de individuos en cuanto a su peso y tamano, esto de acuerdo a la grafica de dispersion de datos obtenida. La funcion se encuentra que 'y' corresponde al peso y 'X' a su tamano."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  indviduo    origen  peso  tamano\n",
+      "0    cam01  Alemania    18    26.0\n",
+      "1    cam02  Alemania    38    62.0\n",
+      "2    cam03  Alemania    39    59.0\n",
+      "3    cam04  Alemania    28    41.0\n",
+      "4    cam05  Alemania     7    13.5\n",
+      "5    cam06  Alemania    29    46.5\n",
+      "6    cam07  Alemania    19    25.5\n",
+      "7    cam08  Alemania    25    40.5\n",
+      "\n",
+      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
+      "[1.56070813]\n",
+      "y=1.5607081309728965*x+-0.35296882343725144\n",
+      "El valor de r^2: 0.9753936261450438\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(alemania)\n",
+    "pa=ale[:,2]\n",
+    "ta=ale[:,3]\n",
+    "print()\n",
+    "regr=linear_model.LinearRegression()\n",
+    "\n",
+    "xa=alemania['peso']\n",
+    "ya=alemania['tamano']\n",
+    "\n",
+    "Xa=xa[:,np.newaxis]\n",
+    "print(regr.fit(Xa,ya))\n",
+    "print(regr.coef_)\n",
+    "m=regr.coef_[0]\n",
+    "b=regr.intercept_\n",
+    "Ya=m*Xa+b\n",
+    "print('y={0}*x+{1}'.format(m,b))\n",
+    "print(\"El valor de r^2:\",r2_score(ya,Ya))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Tamano en Funcion del Peso - Alemania\") \n",
+    "plt.scatter(pa, ta,color= 'red',edgecolors='black',marker=\"o\",alpha=1)\n",
+    "plt.plot(xa,Ya,color='blue')\n",
+    "plt.xlim(0,40,5)\n",
+    "plt.ylim(0,70,5)\n",
+    "plt.xlabel('Peso N')\n",
+    "plt.ylabel('Tamano cm')\n",
+    "plt.legend((\"Tendencia\",\"Individuos\"),loc=\"upper left\")\n",
+    "plt.grid()\n",
+    "plt.savefig('alpt.png')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estadistica\n",
+    "\n",
+    "Las caracteristicas de la dispersion se calculan a continuacion:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PESO\n",
+      "Media del peso: 25.375\n",
+      "\n",
+      "Varianza del peso: 113.98214285714286\n",
+      "\n",
+      "Desviacion estandar del peso: 10.676241981949588\n",
+      "\n",
+      "TAMANO\n",
+      "Media del tamano: 39.25\n",
+      "\n",
+      "Varianza del tamano: 284.64285714285717\n",
+      "\n",
+      "Desviacion estandar del tamano: 16.871362041721977\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PESO\")\n",
+    "\n",
+    "mpa=statistics.mean(pa)\n",
+    "print(\"Media del peso:\",mpa)\n",
+    "print()\n",
+    "vpa=statistics.variance(pa)\n",
+    "print(\"Varianza del peso:\",vpa)\n",
+    "print()\n",
+    "depa=statistics.stdev(pa)\n",
+    "print(\"Desviacion estandar del peso:\",depa)\n",
+    "print()\n",
+    "print(\"TAMANO\")\n",
+    "\n",
+    "mta=statistics.mean(ta)\n",
+    "print(\"Media del tamano:\",mta)\n",
+    "print()\n",
+    "vta=statistics.variance(ta)\n",
+    "print(\"Varianza del tamano:\",vta)\n",
+    "print()\n",
+    "deta=statistics.stdev(ta)\n",
+    "print(\"Desviacion estandar del tamano:\",deta)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inglaterra\n",
+    "\n",
+    "La poblacion que se analizo en Inglaterra es de 6 individuos.\n",
+    "\n",
+    "cuyo peso varia dentro del intervalo de 6 a 32 Newtons o 0.6 a 3.2 Kg.\n",
+    "\n",
+    "El tamano refiriendose a la altura oscila entre los 11 a 49 cm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   indviduo      origen  peso  tamano\n",
+      "10    tur01  Inglaterra    26    39.0\n",
+      "11    tur02  Inglaterra    17    21.5\n",
+      "12    tur03  Inglaterra    32    49.0\n",
+      "13    tur04  Inglaterra    21    31.5\n",
+      "14    tur05  Inglaterra    21    27.5\n",
+      "15    pet01  Inglaterra     6    11.0\n",
+      "\n",
+      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
+      "[1.48573281]\n",
+      "y=1.4857328145265885*x+-0.5408560311283956\n",
+      "El valor de r^2: 0.9629347573427549\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(inglaterra)\n",
+    "pi=ing[:,2]\n",
+    "ti=ing[:,3]\n",
+    "print()\n",
+    "regr=linear_model.LinearRegression()\n",
+    "\n",
+    "xi=inglaterra['peso']  \n",
+    "yi=inglaterra['tamano']\n",
+    "\n",
+    "Xi=xi[:,np.newaxis]\n",
+    "print(regr.fit(Xi,yi))\n",
+    "print(regr.coef_)\n",
+    "m=regr.coef_[0]\n",
+    "b=regr.intercept_\n",
+    "Yi=m*Xi+b\n",
+    "print('y={0}*x+{1}'.format(m,b))\n",
+    "print(\"El valor de r^2:\",r2_score(yi,Yi))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nnnmGSZMm1bn8DTfcwKuvvlrndkREmlKL6OYiFZ166qmceuqpdS5T2SuqiEhzaTVnCtOmTSE7uy9t27YhO7sv06ZNqX+lGFXeyHL++edzwAEHMG7cuEj3ES+++CIHHHAAw4YN48knn4ys8/DDD3P55Zezfv16+vTpw44dOwDYuHEjvXv3Ztu2bVxwwQU8/vjjdW7npptu4tZbb41MZ2dns3jxYgBuv/12srOzyc7O5s6wg6iNGzdy5plnMmTIELKzs/WcBRGpIq5nCmbWFXgQyAYcuAj4BHgU6AssBs5297XxjGPatCkUFEygqGgTublQUrKE/PwJAIwdO65J9vGf//yH2bNnk5WVxRFHHME//vEPDj74YC6++GJef/119ttvvxqfftalSxdycnJ44403yMvL49lnn+WEE04gLS0tskx5eXm926luzpw5/PnPf450u33ooYdy9NFHs2jRInr06MFLL70EBH0hiYhUiveZwl3Ai+5+ADAEKAUmAq+5e3/gtXA6rgoLCygq2kReHqSlQV4eFBVtorCwoMn2MXz4cHr27EmbNm3Iyclh8eLFfPzxx/Tr14/+/ftjZpx33nk1rjtmzJjIL/bp06d/60s/1u1EKykp4YwzzmD33XcnIyOD0aNHM2vWLAYNGkRxcTHXXnsts2bNokuXLrv+4kWkxYhbUjCzLsBRQBGAu29193XAacDkcLHJwOnxiqFSaelScnOrluXmBuVNpbFdWUPQvvDiiy+yZs0a5syZw4gRI2Jet127dpGqJyDyBLbaZGVl8eabbzJo0CCuv/56tVuISBXxrD7qB3wN/NnMhgBzgCuB7u6+PFxmBdC9ppXNbAIwASAzM5OZM2dWmd+lSxc2bNgQUyD779+LkpLPycvbWVZSEpTHuo3abNiwgU2bNlFRUcH27dvZsGEDW7dupby8nJ49e/LZZ5/x3nvv8d3vfpdHHnmEiooKNmzYQHl5OVu3bo3sf+jQoVx66aUcf/zxke6ut23bxubNm+vcTvfu3XnxxRfZsGEDc+fO5bPPPqOsrIxhw4bx4x//mMsuuwx354knnuD+++9n/vz5dO7cmdNOO4327dvzyCOP1HgMysvLv3XMm1NZWVlC9x8rxdm0FGfixTMptAOGAVe4+2wzu4tqVUXu7mZWYzet7n4/cD8EvaRW75GwtLQ05h5Jf/nLW8jPj25TgPz8jhQW3rLLvZp26tSJjh070q5dO9q2bUunTp1o37496enpZGZm8sADDzBmzBg6duzIkUceycKFC+nUqRPp6em0b98+sv9x48Zx1llnMXPmzEhZWloaHTp0qHM75513Ho899hiHHXYYhx56KFlZWWRkZJCdnc1FF13EyJEjAZgwYQK5ubm89NJL/PSnP6Vdu3akpaVx77331ngM0tPTI098S4RU6YVScTYtxZkE3D0uA7APsDhq+kjgOYKG5h5hWQ/gk/q2lZWV5dXNmzfvW2V1mTr1rz5wYB9v08Z84MA+PnXqXxu0fiy++eabJt9mPMQSZ0OPb1MrLi5O6P5jpTibluJsWsA73sDv7ridKbj7CjP73Mz2d/dPgJHAvHAYD0wK/z4drxiijR07rsmuNBIRaaniffPaFcAUM2sPLAIuJGjcnmFm+cAS4Ow4xyAiIjGKa1Jw97lATU/9GdlE26/xgfeyazwFnsYnIvGRsnc0p6ens3r1an2BNTF3Z/Xq1aSnpyc6FBFJgJTt+6hXr14sW7bsWw+mT6Ty8vKU+DKtL8709HR69erVjBGJSLJI2aSQlpZGv379Eh1GFTNnzkzoZZyxSpU4RaT5pWz1kYiIND0lBRERiVBSEBGRCCUFERGJUFIQEZEIJQUREYlQUhARkQglBRERiVBSEBGRCCUFERGJUFIQEZEIJQUREYlQUhARkQglBRERiVBSEBGRCCUFERGJUFIQEZEIJQUREYlQUhARkYi4PqPZzBYDG4DtQIW7H2xm3YBHgb7AYuBsd18bzzhERCQ2zXGmkOfuOe5+cDg9EXjN3fsDr4XTIiKSBBJRfXQaMDkcnwycnoAYRESkBubu8du42WfAWsCBP7n7/Wa2zt27hvMNWFs5XW3dCcAEgMzMzINmzJgRtzibSllZGRkZGYkOo16pEGcqxAiKs6kpzqaVl5c3J6qWJjbuHrcB6Bn+3Rt4DzgKWFdtmbX1bScrK8tTQXFxcaJDiEkqxJkKMborzqa2K3FOnfpXHziwj7dpYz5wYB+fOvWvTRdYNalyPIF3vIHf23FtaHb3L8K/K83sKWA48JWZ9XD35WbWA1gZzxhEpOWbNm0KBQUTKCraRG4ulJQsIT9/AgBjx45LcHSpJW5tCma2u5l1qhwHjgc+BJ4BxoeLjQeejlcMItI6FBYWUFS0ibw8SEuDvDwoKtpEYWFBokNLOfE8U+gOPBU0G9AOmOruL5rZv4EZZpYPLAHOjmMMItIKlJYuJTe3allublAuDRO3pODui4AhNZSvBkbGa78i0voMGLAvJSVLyMvbWVZSEpRLw+iOZhFJeQUFheTnd6S4GLZtg+JiyM/vSEFBYaJDSzlxbWgWEWkOlY3JV1xRQGnpUgYM2JfCwkI1MjeCkoKItAhjx45TEmgCqj4SkZS3aRMMHAhm8PbbiY4mtSkpiEjKcofx42H33WHevKCsf//ExpTqlBREJCXddRe0aQOPPBJMX3op7NgBe+2V2LhSndoURCSlvPIKHH/8zulDDoFZs2C33RIXU0uipCAiKWH+fNh//6ply5fDPvskJp6WStVHIpLU1q+HvfeumhDmzAnaE5QQmp6Sgogkpe3b4fvfh65d4euvg7IZM4JkMGxYYmNryZQURCTp3HgjtGsHzz8fTBcUBMngrLMSG1droDYFEUkab7yRWaX/ohNPhL//PUgQ0jx0qEUk4ebOhaFDAQYCwWWln34aVB1J81JSEJGEWbkSevQI7i+o9PHH377KSJpPvUnBzPYFLgf6Ri/v7qPjF5aItGRbtsBRR8G//rWz7IUXID19Jvvvf0zC4pLYGpqfAVYADwB/iBpERBrEHa64AtLTdyaE224Lyk88MbGxSSCW6qOt7n573CMRkRatqAh++MOd0+edF3RRETycUZJFLEnhHjO7HngJ2FJZ6O7vxy0qEWkxSkrgyCN3TmdlwbvvBp3YSfKJJSlkAT8ETgIqm4McOCpeQYlI6luyBPr2/XbZvnpCZlKLJSmMBfq6+5Z6lxSRVm/jRsjJgQULdpaVlMARRyQuJoldLA3NHwGd4h2IiKS2HTtg7FjIyNiZEB56KGhEVkJIHbGcKXQCPjaz2VRtU9AlqSICwK23ws9/vnP6f/4H7rxTjcipKJakULgrOzCztsA7wBfufrKZ9QOmA3sCc4Dz3X3rruxDRBLjhRdg1Kid0//1X1BcDO3bJy4m2TWxJIVPgZXuXg5gZh2Ahjzb6EqgFOgcTv8OuMPdp5vZfUA+cG8DticiCfbxxzBgwM7p9u1h2TLIzExcTNI0YmlTeJKdVx0Rjj8Ry8bNrBfwfeDBcNqAEcDj4SKTgdNjDVZEEmvtWthjj6oJYe7c4A5lJYSWIZak0C66eie8CinWB9/dCVzDzqSyJ7DO3SvC6WVAzxi3JSIJUlEBxx0H3brBunVB2RNPBI3IQ4YkNjZpWrFUH602s1Hu/jyAmZ0MrKlvpXC5le4+x8yOaWhgZjYBmACQmZnJzJkzG7qJZldWVqY4m0gqxAipH+eaNWtYseILNm/eSocO7dlnn55069atyjIPPtiPKVP6RKYvuOAzxo9fAkBTv/RUP54tgrvXOQD9CRqKPwuHfwH9Y1jvFoIzgcUEfSdtAqYAqwjOPgAOB16qb1tZWVmeCoqLixMdQkxSIc5UiNE9teOcOvWv3q9fR3/9dXzrVvz11/F+/Tr61Kl/dXf36dPdg3OBYDjlFPeKiuaPMxmlSpzAO17P92v1od4zBXf/FDjYzLqG0+tiTDbXAdcBhGcKP3P3cWb2GHAmwRVI44GnY9meiDStwsICioo2RR5qk5cHRUWb+OEPp3HuueMiy+2zT9Cw3KVLggKVZhXz8xRiTQYxuBaYbma/Af4DFDXRdkWkAUpLl5Kbu3N6xYrujBixosoy8+dD//7NHJgkVLM8ZMfdZwIzw/FFwPDm2K+I1G7AgH0pKVnC8OEdycjYWGXeK6/AsccmKDBJqFiuPhKRFugXvyjkuOO+rJIQunX7KVOnTlFCaMXqTQpm1s7MLjWz6eHwYzPTYzxFUtjFF8O4cePYvr1HWLKWAw/sy+9/P4yxY8fVua60bLF8uf8B2B14KJw+DxhKeLmoiKSOyZPhgguqlq1fD50770FwoaC0drEkhcPcPfr2lJfN7L14BSQiTe/TTzMiVxlVmjev6p3JIhBbUthhZn3dfTGAmfWlarcXIpKkVq+GvfYCODhS9vjj8IMfJCwkSXKxJIVrgVlm9glgwH4EndiJSJLavh3aVft0/+xn8H//l5h4JHXEcvPay2aWBVSeaJa6++b4hiUijXXyyfDcczunBw2Cu++eyTHHHJOwmCR1xHpJ6iCCM4QDgDPM7Nz4hSQijXHbbcFDbaITQnk5vP9+4mKS1FPvmYKZPQwcCMwFtofFDkyNX1giEqviYhgxomrZ0qXQu3di4pHUFtPVR8CB7q7GZZEksmzZt7/4X3vt2wlCpCFiqT76CNDjM0SSxJYtQTVRdEL4v/8L+jJVQpBdFcuZQhdgnpn9E9hSWejuo+MWlYjUKCcH3ou6S+jEE4PnJIs0lViSwi1xj0JE6nTNNd++nLSiAtq2TUw80nLFcknqa80RiIh821NPwehq5+SrVsGeeyYmHmn5YukQ7xAz+6eZrTezcjPbYmbfNEdwIq3Vxx8H7QbRCWHOnKDdQAlB4imWhuY/EjwhbRHQCbgcuDueQYm0Vhs2BMkguk+ihx4KksGwYYmLS1qPWJJCG3f/hOC5ytvc/QHg+3GOS6RVqTwD6Nx5Z9lFFwXlF16YuLik9YmloXmjmbUH3jOz3wLLATVviTSR8ePhkUd2TvfoAV98EZwxiDS3WM4ULgiXu5zgjub+wJlxjEmkRZg2bQrZ2X1p27YN2dl9mTZtSpX5Dz4YfPFHJ4QNG+DLL5UQJHFiufpoUThaDvwyvuGItAzTpk2hoGACRUWbyM2FkpIl5OcHz6Xab79xDK/2lPJPPoGsrAQEKlJNLH0fnQj8GugTLm+Au3u3OMcmkrIKCwsoKtoUebBNXh7cdltHRo+u+qjLp5+GU09NQIAitYilTeH3wNnAB+jhOiIxKS1dSm5uMF5R0Za0tIoq86+7Dn772wQEJlKPWNoUlgFzwyuPtlcO9a1kZulm9i8ze8/MPjKzX4Xl/cxstpktMLNHw0ZskRZlwIB9KSmB4457uUpCSE9/D3clBElesZwpXAP83cxmUrXvo/ruVdgCjHD3MjNLA0rM7AXgauAOd59uZvcRPMXt3kZFL5KkBg16ihEjhlYp69u3K7/97R+AITWvJJIEYjlT+BXBVUddCXpLrRzq5IGycDItHBwYATwelk8GTm9gzCJJ69VXgyuHpk/fmRDMejJwYF9++9s/MHbsuDrWFkm8WM4Uert7dmM2bmZtgTkET237A7AQWOfulefTy4Cejdm2SDJZsgT69q1a9sYbcNRRAF8kICKRxjF3r3sBs9uA59z99UbvxKwr8BTBJa0Pu/t+YXlv4IWako6ZTQAmAGRmZh40Y8aMxu6+2ZSVlZGRkZHoMOqVCnGmQowAq1dv4swzR1Upu+yyBZx55rIERVSzVDmeirNp5eXlzXH3gxu0krvXOQBrCa46KgPWhNNr6luvhu3cAPwcWEXQZQbA4cBL9a2blZXlqaC4uDjRIcQkFeJM9hh37HDff3/3oCOKYDj99ERHVbtkP56VFGfTAt7xBn5Xx9KmsBdBe0AXgraEvYihTcHMMsMzBMysA3AcUAoUs/OO6PHA0zHEIJI0fvITaNMmuOEMgmcabN8edHMtkurqTQoeXH6aQXDJxKFRQ316AMVm9j7wb+AVd38WuBa42swWAHsCRY2MXaRZzZgRNCLfeefOsqefLqGiIkgSIi1BLM9TyAfeAl4Hfhf+rfcqa3d/392HugHiAwcAABFWSURBVPtgd89295vD8kXuPtzd93P3s9x9S33bEkmkjz4KksGYMTvL5s4NKo06d66odb36+j4SSUax/L65CjgYWOzuRwIHAavjGpVIEli/PkgG2VGXQfz1r0EyGFLPrQaVfR/dc88Sysude+5ZQkHBBCUGSXqxJIVyd98MYGbt3f0jYP/4hiWSODt2wO67Q9euO8suuSRIBuNivM0guu+jtLSg76Oiok0UFhbEJ2iRJlLrfQpm1s6D+wmWhw3GfwdeMrM1BPcXiLQ455wDjz66c7pPH/jss4Z3ZR3d91Gl3NygXCSZ1XWm8C8Adz/V3de5+y+B3wBTgNOaIziR5nLffcEXf3RC2LgRFi9u3LMNKvs+ilZSEpSLJLO6ksK3Pgru/pq7P6nGYWkp3n47+NL/8Y93li1cGFQVdezY+O0WFBSSn9+R4mLYtg2KiyE/vyMFBYW7HrRIHNXVzUWmmV1d20x3vz0O8Yg0ixUrgsdeRnvuORg1qublG6qyj6MrriigtHQpAwbsS2Fhofo+kqRXV1JoS3B/gh4MKC3Gtm3Qvlpn7TfdBDfe2PT7Gjt2nJKApJy6ksLyynsLRFqCo46CWbN2TufmVp0WkQa2KYikoptvDtoNohPA1q1KCCI1qetMYWSzRSESBy+88O02guXLYZ99EhOPSCqo9UzB3dc0ZyAiTaWsDL73vaoJ4a23giuKlBBE6qZuvKTFqKiABx6A/v1h0aKg7I9/DJLB4YcnNjaRVKGkICnPPbicdMgQmDABvvvdnWcG0fcfiEj9lBQkpb37LowcCSefHFxu+sQTwZ3DOjMQaRwlBUlJS5fC+efDQQfBBx/APfcEXVyPHt24bilEJFDX1UciSWfdOrjlFrjrruDLf+LEYOjSJdGRibQMSgqSErZuhXvvhV//GtasCc4SfvMb6N070ZGJtCyqPpKk5g6PPw4HHghXXQVDhwbtCJMnKyGIxIOSgiStt96CI46As86CDh2Cm9FefhlycupeT4/BFGk8VR9J0vn0U7jhhoHMmhX0ZPrgg3DBBdC2bf3rVj4Gs6hoE7m5UFKyhPz8CQDqnE4kBjpTkKTx9ddwxRVBVdE77+zBzTcHCSI/P7aEAHoMpsiu0pmCJNzmzcHVRLfcEjzt7OKL4bjjZjN69BEN3pYegymya3SmIAmzYwc88gjsvz9cdx0cfXRwz8G990K3btsatU09BlNk18QtKZhZbzMrNrN5ZvaRmV0Zlnczs1fM7NPw7x7xikGS16uvBjeejR8P3bvDzJnwzDMwYMCubVePwRTZNfGsPqoAfuru75pZJ2COmb0CXAC85u6TzGwiMBG4No5xSBL58EO45prgSqK+fWHqVBgzBto00c8TPQZTZNfELSm4+3JgeTi+wcxKgZ7AacAx4WKTgZkoKbR4X34JN9wAf/4zdO4Mt94Kl18Ou+3W9PvSYzBFGs/cPf47MesLvAlkA0vdvWtYbsDayulq60wAJgBkZmYeNGPGjLjHuavKysrIyMhIdBj1as44N21qy/TpvXnssd5UVBinn/4F55+/hM6dK5Imxl2hOJuW4mxaeXl5c9z94Aat5O5xHYAMYA4wOpxeV23+2vq2kZWV5amguLg40SHEpDni3LbN/b773Lt3dwf3MWPcFy6MfX0dy6alOJtWqsQJvOMN/M6O69VHZpYGPAFMcfcnw+KvzKxHOL8HsDKeMUjzcoe//x0GD4ZLLoGsLJg9G6ZPD55zICLJLZ5XHxlQBJS6++1Rs54Bxofj44Gn4xWDNK933gluFjv1VNi+Hf72N3jjDRg+PNGRiUis4nmmcARwPjDCzOaGwyhgEnCcmX0KHBtOSwpbvBjOPRcOOQTmzYM//CG4yui00/RsA5FUE8+rj0qA2r4SRsZrv9J81q6F3/4W7r476IaioCC43LRz50RHJiKNpW4upMG2bIE//jF4tsG6dUFndTffDL16JToyEdlV6uZCYuYOjz4a3HV89dVBddF//gMPPaSEINJSKClITGbNgsMOg3POgU6d4KWXgmHIkERHJiJNSUlB6vTJJ3DGGXDUUfDFF8Edye++C8cfn+jIRCQelBSkRitXwmWXwcCB8NprUFgI8+fH/rAbEUlNamiWKjZtgjvugN/9Lhj/0Y/gxhth770THZmINAclBQGCm83+8he4/vqgmuj002HSpOBZByLSeqj6SHj5ZRg2DC68EHr2hDffhKeeUkIQaY2UFFqx99+HE04Ihg0bgv6J/vlPOPLIREcmIomi6qNW6Ouvd+PCC2HyZOjaFW6/HS69ND7PNhCR1KKk0Ip88w387//CrbcOxx1++lP4xS9gDz0QVURCSgqtwLZt8MADcNNN8PXXMHLkKh58sDt9+yY6MhFJNkoKLZg7PP00TJwY3IR29NHBYzDLykrp27d7osMTkSSkhuYWavbsIAmccUbQffUzz0BxMRzcsAfziUgro6TQwixaFPRPdNhhwR3I990HH3wAp5yiZxuISP1UfdRCrFkDv/kN/P73kJYGv/wl/PznQed1IiKxUlJIceXlQSIoLAyuLrrwwuDZBt/5TqIjE5FUpOqjFLVjB0ybFjzb4Oc/h8MPh7lz4cEHlRBEpPGUFFLQG2/AoYcGz0Xu2hVefRWefx4GDUp0ZCKS6pQUmsG0aVPIzu5L27ZtyM7uy7RpUxq1ndJSOPVUOOYYWLEiuCN5zhwYqSdei0gTUZtCnE2bNoWCggkUFW0iNxdKSpaQnz8BgLFjx8W0ja++Cm48e+AB2H13uOUWuPJK6NAhjoGLSKukM4U4KywsoKhoE3l5wVVBeXlQVLSJwsKCetfduBF+/WvYb7+greDSS2HBguBmNCUEEYmHuCUFM3vIzFaa2YdRZd3M7BUz+zT82+J73SktXUpubtWy3NygvDbbt0NREWRlwQ03BI++nDcP7r4bMjPjHLCItGrxPFN4GDixWtlE4DV37w+8Fk63aAMG7EtJSdWykpKgvDp3eOEFyMmBH/4Q9t03WPaJJ6B//2YKWERatbglBXd/E1hTrfg0YHI4Phk4PV77TxYFBYXk53ekuDjomK64GPLzO1JQUFhlublzgzOCUaNg82Z47DF46y044ogEBS4irZK5e/w2btYXeNbds8Ppde7eNRw3YG3ldA3rTgAmAGRmZh40Y8aMuMXZVMrKysjIyPhW+Zo1a1ix4gs2b95Khw7t2WefnnTr1g2AlSt3o6ioH6+80p1OnSr47/9ezKmnfklaWvz+L7XFmUxSIUZQnE1NcTatvLy8Oe7esB7P3D1uA9AX+DBqel21+Wtj2U5WVpanguLi4piXXbfOfeJE9/R09912c7/mGve1a+MXW7SGxJkoqRCju+JsaoqzaQHveAO/t5v7ktSvzKyHuy83sx7Aymbef8Jt2wZ/+hP86lewahWcd17QZ1GfPomOTESk+S9JfQYYH46PB55u5v0njDs8+SQMHAhXXAGDBwc3nv3lL0oIIpI84nlJ6jTgbWB/M1tmZvnAJOA4M/sUODacbhVGj4Yf/CC4V+G554KuKYYNS3RUIiJVxa36yN3H1jKrVXbKcMYZwZVFF14I7XQfuYgkKX09NZP//u9ERyAiUj91cyEiIhFKCiIiEqGkICIiEUoKIiISoaQgIiIRSgoiIhKhpCAiIhFKCiIiEqGkICIiEUoKIiISoaQgIiIRSgoiIhKhpCAiIhFKCiIiEqGkICIiEUoKIiISoaQgIiIRSgoiIhKhpCAiIhFKCiIiEqGkICIiEQlJCmZ2opl9YmYLzGxiImIQEZFva/akYGZtgT8AJwEHAmPN7MDmjkNERL4tEWcKw4EF7r7I3bcC04HTEhCHiIhU0y4B++wJfB41vQw4tPpCZjYBmBBObjGzD5shtl21F7Aq0UHEIBXiTIUYQXE2NcXZtPZv6AqJSAoxcff7gfsBzOwddz84wSHVS3E2nVSIERRnU1OcTcvM3mnoOomoPvoC6B013SssExGRBEtEUvg30N/M+plZe+Ac4JkExCEiItU0e/WRu1eY2eXAS0Bb4CF3/6ie1e6Pf2RNQnE2nVSIERRnU1OcTavBcZq7xyMQERFJQbqjWUREIpQUREQkIqmTQqp0h2Fmi83sAzOb25hLwOLFzB4ys5XR93iYWTcze8XMPg3/7pHIGMOYaorzJjP7Ijymc81sVCJjDGPqbWbFZjbPzD4ysyvD8qQ5pnXEmFTH08zSzexfZvZeGOevwvJ+ZjY7/Mw/Gl6MkoxxPmxmn0Udz5xExlnJzNqa2X/M7NlwuuHH092TciBohF4IfBdoD7wHHJjouGqJdTGwV6LjqCGuo4BhwIdRZf8LTAzHJwK/S9I4bwJ+lujYqsXZAxgWjncC5hN01ZI0x7SOGJPqeAIGZITjacBs4DBgBnBOWH4f8OMkjfNh4MxEH8ca4r0amAo8G043+Hgm85mCusPYRe7+JrCmWvFpwORwfDJwerMGVYNa4kw67r7c3d8NxzcApQR36CfNMa0jxqTigbJwMi0cHBgBPB6WJ/z9WUecScfMegHfBx4Mp41GHM9kTgo1dYeRdG/ukAMvm9mcsHuOZNbd3ZeH4yuA7okMph6Xm9n7YfVSwqu5oplZX2AowS/HpDym1WKEJDueYVXHXGAl8ApBzcA6d68IF0mKz3z1ON298ngWhsfzDjPbLYEhVroTuAbYEU7vSSOOZzInhVSS6+7DCHp+vczMjkp0QLHw4JwyKX/1APcC3wNygOXAbYkNZyczywCeAK5y92+i5yXLMa0hxqQ7nu6+3d1zCHo1GA4ckOCQalQ9TjPLBq4jiPcQoBtwbQJDxMxOBla6+5xd3VYyJ4WU6Q7D3b8I/64EniJ4gyerr8ysB0D4d2WC46mRu38Vfhh3AA+QJMfUzNIIvmynuPuTYXFSHdOaYkzW4wng7uuAYuBwoKuZVd5Um1Sf+ag4Twyr6dzdtwB/JvHH8wjgVDNbTFDVPgK4i0Ycz2ROCinRHYaZ7W5mnSrHgeOBZO7R9RlgfDg+Hng6gbHUqvJLNnQGSXBMwzraIqDU3W+PmpU0x7S2GJPteJpZppl1Dcc7AMcRtH8UA2eGiyX8/VlLnB9H/Qgwgnr6hB5Pd7/O3Xu5e1+C78rX3X0cjTmeiW4tr6clfRTB1RMLgYJEx1NLjN8luDLqPeCjZIoTmEZQVbCNoD4xn6Ce8TXgU+BVoFuSxvkX4APgfYIv3R5JEGcuQdXQ+8DccBiVTMe0jhiT6ngCg4H/hPF8CNwQln8X+BewAHgM2C1J43w9PJ4fAn8lvEIpGQbgGHZefdTg46luLkREJCKZq49ERKSZKSmIiEiEkoKIiEQoKYiISISSgoiIRCgpSKtmZtvDXi4/NLPHzKxjnPZzk5ltMrO9o8rK6lpHJBGUFKS12+zuOe6eDWwFLonjvlYBP43j9kV2mZKCyE6zgP0AzOy8sB/9uWb2p7BTtLZhP/ofWvD8jJ+Ey+aY2T/DztGeqqOzuYeAMWbWrZlej0iDKSmIAGH/MCcBH5jZAGAMcIQHHaFtB8YRdCbX092z3X0QQZ83AI8A17r7YIK7XG+sZTdlBInhyvi9EpFdo6QgrV2HsFvkd4ClBP0GjQQOAv4dzhtJ0F3AIuC7ZnaPmZ0IfGNmXYCu7v5GuL3JBA8Nqs3dwPjK/rJEkk27+hcRadE2h2cDEWEnZ5Pd/brqC5vZEOAEgraHs4GfNGRn7r7OzKYClzU+ZJH40ZmCyLe9BpxZeaWQBc9g7mNmewFt3P0J4HqCx16uB9aa2ZHhuucDb9S41Z1uB36EfpRJEtKbUqQad59nZtcTPE2vDUHvrZcBm4E/h2UQPGgFgi6J7wsvZ10EXFjP9leZ2VM08CxDpDmol1QREYlQ9ZGIiEQoKYiISISSgoiIRCgpiIhIhJKCiIhEKCmIiEiEkoKIiET8P6TxvglbKkOZAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Tamano en Funcion del Peso - Inglaterra\") \n",
+    "plt.scatter(pi, ti,color= 'yellow',edgecolors='black',marker=\"o\",alpha=1)\n",
+    "plt.plot(xi,Yi,color='blue')\n",
+    "plt.xlim(0,40,5)\n",
+    "plt.ylim(0,70,5)\n",
+    "plt.xlabel('Peso N')\n",
+    "plt.ylabel('Tamano cm')\n",
+    "plt.legend((\"Tendencia\",\"Individuos\"),loc=\"upper left\")\n",
+    "plt.grid()\n",
+    "plt.savefig('ingpt.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estadistica\n",
+    "\n",
+    "Las caracteristicas de la dispersion se calculan a continuacion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PESO\n",
+      "Media del peso: 20.5\n",
+      "\n",
+      "Varianza del peso: 77.1\n",
+      "\n",
+      "Desviacion estandar del peso: 8.780660567406077\n",
+      "\n",
+      "TAMANO\n",
+      "Media del tamano: 29.916666666666668\n",
+      "\n",
+      "Varianza del tamano: 176.74166666666667\n",
+      "\n",
+      "Desviacion estandar del tamano: 13.294422389358127\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PESO\")\n",
+    "\n",
+    "mpi=statistics.mean(pi)\n",
+    "print(\"Media del peso:\",mpi)\n",
+    "print()\n",
+    "vpi=statistics.variance(pi)\n",
+    "print(\"Varianza del peso:\",vpi)\n",
+    "print()\n",
+    "depi=statistics.stdev(pi)\n",
+    "print(\"Desviacion estandar del peso:\",depi)\n",
+    "print()\n",
+    "print(\"TAMANO\")\n",
+    "\n",
+    "mti=statistics.mean(ti)\n",
+    "print(\"Media del tamano:\",mti)\n",
+    "print()\n",
+    "vti=statistics.variance(ti)\n",
+    "print(\"Varianza del tamano:\",vti)\n",
+    "print()\n",
+    "deti=statistics.stdev(ti)\n",
+    "print(\"Desviacion estandar del tamano:\",deti)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Italia\n",
+    "\n",
+    "La poblacion que se analizo en Inglaterra es de 8 individuos.\n",
+    "\n",
+    "cuyo peso varia dentro del intervalo de 8 a 36 Newtons o 0.8 a 3.6 Kg.\n",
+    "\n",
+    "El tamano refiriendose a la altura oscila entre los 12 a 54 cm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   indviduo  origen  peso  tamano\n",
+      "16    pet02  Italia     8    12.0\n",
+      "17    pet03  Italia    28    40.0\n",
+      "18    pet04  Italia    10    15.0\n",
+      "19    pet05  Italia    19    28.5\n",
+      "20    pet06  Italia    13    17.5\n",
+      "21    pet07  Italia    16    29.0\n",
+      "22    pet08  Italia    15    20.5\n",
+      "23    pet09  Italia    36    54.0\n",
+      "\n",
+      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
+      "[1.4778664]\n",
+      "y=1.4778664007976074*x+0.27617148554336524\n",
+      "El valor de r^2: 0.9739863352387761\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(italia)\n",
+    "pt=ita[:,2]\n",
+    "tt=ita[:,3]\n",
+    "print()\n",
+    "regr=linear_model.LinearRegression()\n",
+    "\n",
+    "xt=italia['peso']  \n",
+    "yt=italia['tamano']\n",
+    "\n",
+    "Xt=xt[:,np.newaxis]\n",
+    "print(regr.fit(Xt,yt))\n",
+    "print(regr.coef_)\n",
+    "m=regr.coef_[0]\n",
+    "b=regr.intercept_\n",
+    "Yt=m*Xt+b\n",
+    "print('y={0}*x+{1}'.format(m,b))\n",
+    "print(\"El valor de r^2:\",r2_score(yt,Yt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Tamano en Funcion del Peso - Italia\") \n",
+    "plt.scatter(pi, ti,color= 'green',edgecolors='black',marker=\"o\",alpha=1)\n",
+    "plt.plot(xt,Yt,color='blue')\n",
+    "plt.xlim(0,40,5)\n",
+    "plt.ylim(0,70,5)\n",
+    "plt.xlabel('Peso N')\n",
+    "plt.ylabel('Tamano cm')\n",
+    "plt.legend((\"Tendencia\",\"Individuos\"),loc=\"upper left\")\n",
+    "plt.grid()\n",
+    "plt.savefig('itapt.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estadistica\n",
+    "\n",
+    "Las caracteristicas de la dispersion se calculan a continuacion:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PESO\n",
+      "Media del peso: 18.125\n",
+      "\n",
+      "Varianza del peso: 89.55357142857143\n",
+      "\n",
+      "Desviacion estandar del peso: 9.463274878633264\n",
+      "\n",
+      "TAMANO\n",
+      "Media del tamano: 27.0625\n",
+      "\n",
+      "Varianza del tamano: 200.81696428571428\n",
+      "\n",
+      "Desviacion estandar del tamano: 14.170990236596534\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PESO\")\n",
+    "\n",
+    "mpt=statistics.mean(pt)\n",
+    "print(\"Media del peso:\",mpt)\n",
+    "print()\n",
+    "vpt=statistics.variance(pt)\n",
+    "print(\"Varianza del peso:\",vpt)\n",
+    "print()\n",
+    "dept=statistics.stdev(pt)\n",
+    "print(\"Desviacion estandar del peso:\",dept)\n",
+    "print()\n",
+    "print(\"TAMANO\")\n",
+    "\n",
+    "mtt=statistics.mean(tt)\n",
+    "print(\"Media del tamano:\",mtt)\n",
+    "print()\n",
+    "vtt=statistics.variance(tt)\n",
+    "print(\"Varianza del tamano:\",vtt)\n",
+    "print()\n",
+    "dett=statistics.stdev(tt)\n",
+    "print(\"Desviacion estandar del tamano:\",dett)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Francia\n",
+    "\n",
+    "La poblacion que se analizo en Inglaterra es de 6 individuos.\n",
+    "\n",
+    "cuyo peso varia dentro del intervalo de 5 a 39 Newtons o 0.5 a 3.9 Kg.\n",
+    "\n",
+    "El tamano refiriendose a la altura oscila entre los 11 a 53 cm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   indviduo   origen  peso  tamano\n",
+      "24    pet10  Francia    39    53.5\n",
+      "25    tur06  Francia    33    49.5\n",
+      "26    tur07  Francia    31    43.5\n",
+      "27    tur08  Francia     9    15.5\n",
+      "28    tur09  Francia    10    17.0\n",
+      "29    tur10  Francia     5    11.5\n",
+      "30    tur11  Francia    20    26.0\n",
+      "31    tur12  Francia    27    43.5\n",
+      "\n",
+      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)\n",
+      "[1.31520285]\n",
+      "y=1.315202853321445*x+3.89433794025857\n",
+      "El valor de r^2: 0.9775380240106478\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(francia)\n",
+    "pf=fra[:,2]\n",
+    "tf=fra[:,3]\n",
+    "print()\n",
+    "regr=linear_model.LinearRegression()\n",
+    "\n",
+    "xf=francia['peso']  \n",
+    "yf=francia['tamano']\n",
+    "\n",
+    "Xf=xf[:,np.newaxis]\n",
+    "print(regr.fit(Xf,yf))\n",
+    "print(regr.coef_)\n",
+    "m=regr.coef_[0]\n",
+    "b=regr.intercept_\n",
+    "Yf=m*Xf+b\n",
+    "print('y={0}*x+{1}'.format(m,b))\n",
+    "print(\"El valor de r^2:\",r2_score(yf,Yf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Tamano en Funcion del Peso - Francia\") \n",
+    "plt.scatter(pf, tf,color= 'purple',edgecolors='black',marker=\"o\",alpha=1)\n",
+    "plt.plot(xf,Yf,color='blue')\n",
+    "plt.xlim(0,40,5)\n",
+    "plt.ylim(0,70,5)\n",
+    "plt.xlabel('Peso N')\n",
+    "plt.ylabel('Tamano cm')\n",
+    "plt.legend((\"Tendencia\",\"Individuos\"),loc=\"upper left\")\n",
+    "plt.grid()\n",
+    "plt.savefig('frapt.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estadistica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PESO\n",
+      "Media del peso: 21.75\n",
+      "\n",
+      "Varianza del peso: 160.21428571428572\n",
+      "\n",
+      "Desviacion estandar del peso: 12.65757819309388\n",
+      "\n",
+      "TAMANO\n",
+      "Media del tamano: 32.5\n",
+      "\n",
+      "Varianza del tamano: 283.5\n",
+      "\n",
+      "Desviacion estandar del tamano: 16.837458240482736\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"PESO\")\n",
+    "\n",
+    "mpf=statistics.mean(pf)\n",
+    "print(\"Media del peso:\",mpf)\n",
+    "print()\n",
+    "vpf=statistics.variance(pf)\n",
+    "print(\"Varianza del peso:\",vpf)\n",
+    "print()\n",
+    "depf=statistics.stdev(pf)\n",
+    "print(\"Desviacion estandar del peso:\",depf)\n",
+    "print()\n",
+    "print(\"TAMANO\")\n",
+    "\n",
+    "mtf=statistics.mean(tf)\n",
+    "print(\"Media del tamano:\",mtf)\n",
+    "print()\n",
+    "vtf=statistics.variance(tf)\n",
+    "print(\"Varianza del tamano:\",vtf)\n",
+    "print()\n",
+    "detf=statistics.stdev(tf)\n",
+    "print(\"Desviacion estandar del tamano:\",detf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Observaciones\n",
+    "De acuerdo a lo que se observa en las graficas se puede decir que la mejor relación y mayor homogeneidad en el desarrollo (peso/tamaño) de individuos se encuentra en Francia, esto debido a que el peso y el tamaño de cada individuo esta mucho mejor distribuida.\n",
+    "\n",
+    "La mayor homogeneidad de tamanos se encuentra en Inglaterra, donde se pudo evidenciar que su dispersion es la mas pequena con respecto a la media de su tamano.\n",
+    "\n",
+    "En cuanto al peso la homogeneidad se encuentra en Inglaterra , donde de nuevo al igual que su tamano registra la dispersion mas baja.\n",
+    "\n",
+    "Italia posee los individuos menos homogeneos debido a sus altas dispersiones tanto en peso como en tamano.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analisis de la Poblacion Global\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Relacion Tamano/Peso\n",
+    "\n",
+    "La grafica representa los datos de tamano y peso para una poblacion de 32 individuos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.title(\"Relacion Entre Tamano/Peso Poblacion Global\") \n",
+    "plt.scatter(pa, ta, color= 'red',edgecolors='black',marker=\"o\")\n",
+    "plt.scatter(pi, ti, color = 'yellow',edgecolors='black',marker=\"o\")\n",
+    "plt.scatter(pt, tt, color='green' ,edgecolors='black',marker=\"o\")\n",
+    "plt.scatter(pf, tf, color = 'purple',edgecolors='black',marker=\"o\")\n",
+    "plt.xlim(0,40)\n",
+    "plt.ylim(0,70)\n",
+    "plt.xlabel('Peso N')\n",
+    "plt.ylabel('Tamano cm')\n",
+    "plt.legend((\"Alemania\",\"inglaterra\",\"Italia\",\"Francia\"),loc=\"upper left\")\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estadistica\n",
+    "\n",
+    "Las caracteristicas de la dispersion se calculan a continuacion:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PESO PROMEDIO GLOBAL\n",
+      "Media del peso: 21.90625\n",
+      "\n",
+      "Varianza del peso: 113.44254032258064\n",
+      "\n",
+      "Desviacion estandar del peso: 10.650940818659198\n",
+      "\n",
+      "TAMANO\n",
+      "Media del tamano: 32.75\n",
+      "\n",
+      "Varianza del tamano: 251.29032258064515\n",
+      "\n",
+      "Desviacion estandar del tamano: 15.852139369203298\n"
+     ]
+    }
+   ],
+   "source": [
+    "E=np.array(e)\n",
+    "\n",
+    "peso=E[:,2]\n",
+    "tamano=E[:,3]\n",
+    "\n",
+    "\n",
+    "print(\"PESO PROMEDIO GLOBAL\")\n",
+    "\n",
+    "mp=statistics.mean(peso)\n",
+    "print(\"Media del peso:\",mp)\n",
+    "print()\n",
+    "vp=statistics.variance(peso)\n",
+    "print(\"Varianza del peso:\",vp)\n",
+    "print()\n",
+    "dep=statistics.stdev(peso)\n",
+    "print(\"Desviacion estandar del peso:\",dep)\n",
+    "print()\n",
+    "print(\"TAMANO\")\n",
+    "\n",
+    "mt=statistics.mean(tamano)\n",
+    "print(\"Media del tamano:\",mt)\n",
+    "print()\n",
+    "vt=statistics.variance(tamano)\n",
+    "print(\"Varianza del tamano:\",vt)\n",
+    "print()\n",
+    "det=statistics.stdev(tamano)\n",
+    "print(\"Desviacion estandar del tamano:\",det)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Observaciones\n",
+    "La poblacion global de individuos representada en la grafica \"relacion Entre tamano/Peso Poblacion Global\" indica que los italianos poseen el mayor porcentaje de individuos (75%) con menor tamano y peso al ubicarse por debajo de los 25 N y 40 cm.\n",
+    "\n",
+    "Por otro lado los individuos Alemanes poseen la poblacion mas alta (62.5%) ubicada en peso superiores a los 25 N y 40 cm de tamano.\n",
+    "\n",
+    "Cabe mencionar que existe una franja donde no se registran ningun individuo 22 N y 38 cm.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "paises = ['Alemania', 'Inglaterra', 'Italia', 'Francia','Prom Global']\n",
+    "\n",
+    "peso = [mpa, mpi, mpt, mpf, mp]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax = plt.axes()\n",
+    "ax.set_ylabel('Peso')\n",
+    "plt.ylabel('Peso Promedio')\n",
+    "ax.set_title('Promedio de Peso Por Pais con respecto al Promedio Global')\n",
+    "\n",
+    "\n",
+    "plt.bar(paises, peso , color=['red','yellow','green','purple','brown'])\n",
+    "plt.axis([-1,5,0,35])\n",
+    "ax.yaxis.grid(True, which='major') \n",
+    "plt.savefig('pesoprom.png')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paises = ['Alemania', 'Inglaterra', 'Italia', 'Francia','Prom Global']\n",
+    "\n",
+    "tamano = [mta, mti, mtt, mtf, mt]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax = plt.axes()\n",
+    "ax.set_ylabel('Tamano')\n",
+    "plt.ylabel('Tamano Promedio')\n",
+    "ax.set_title('Promedio de Tamano Por Pais con respecto al Promedio Global')\n",
+    "\n",
+    "\n",
+    "plt.bar(paises, tamano , color=['red','yellow','green','purple','brown'])\n",
+    "plt.axis([-1,5,0,50])\n",
+    "ax.yaxis.grid(True, which='major') \n",
+    "plt.savefig('tamanoprom.png')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Observaciones\n",
+    "\n",
+    "El peso de los individuos tanto en Italia como en Inglaterra es inferior al promedio global.\n",
+    "\n",
+    "El peso de individuos de Alemania es el unico que supera el promedio global.\n",
+    "\n",
+    "En cuanto al tamano al igual que en el peso los registros mas bajos con respecto al promedio se encuentran en Italia e Inglaterra.\n",
+    "\n",
+    "Los Alemanes tienen el mayor tamano con respecto al promedio.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}