solucion1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/marsgr6/EN-online/8a1cee296279c274d8f05f3905fce97c1f7636a2/data/UX_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "###                          Solucion pregunta 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solucion pregunta 1.\n",
    "data.info()  Usando la funcion info() podemos obsever todas las variables numericas en este caso: SUS y Time son float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   SUS     30 non-null     float64\n",
      " 1   Time    30 non-null     float64\n",
      " 2   ID      30 non-null     object \n",
      " 3   Gender  30 non-null     object \n",
      " 4   Age     30 non-null     object \n",
      " 5   Kinect  30 non-null     object \n",
      " 6   NUI     30 non-null     object \n",
      " 7   Games   30 non-null     object \n",
      "dtypes: float64(2), object(6)\n",
      "memory usage: 2.0+ KB\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUS</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>76.083333</td>\n",
       "      <td>157.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.616997</td>\n",
       "      <td>22.399415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>112.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>68.125000</td>\n",
       "      <td>140.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.750000</td>\n",
       "      <td>159.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>87.500000</td>\n",
       "      <td>177.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>188.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SUS        Time\n",
       "count  30.000000   30.000000\n",
       "mean   76.083333  157.500000\n",
       "std    14.616997   22.399415\n",
       "min    40.000000  112.700000\n",
       "25%    68.125000  140.025000\n",
       "50%    78.750000  159.700000\n",
       "75%    87.500000  177.050000\n",
       "max    95.000000  188.300000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "\n",
    "data[['SUS','Time']].describe() #escogemos aquellos valores que son numericos y usando la funcion describe obtenemos el resultado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretacion de los cuartiles.\n",
    "SUS:\n",
    "Los cuartiles obtenidos dividen a la muestra en cuatro partes, el primer cuartil significa que 25% de los datos es menor o igual a 68.18. En nuestro estudio significa que el 25% de los individuos calificaron la usabilidad de ePHoRt con un valor menor o igual a 68.18.\n",
    "\n",
    "El tercer cuartil nos informa que un 25% de los individuos dieron una calificacion mayor o igual a 87.5.\n",
    "\n",
    "Ademas poemos apreciar que en la usabilidad del sistema la mediana es de 78.5 y esta muy cerca de la media. Basandome en la devivacion standar(14.61), los datos tienen un distribucion normal.\n",
    "\n",
    "\n",
    "Time:(tiempo de ralizacion de las tareas)\n",
    "\n",
    "En el tiempo utilizado para realizar las tareas podemos aprecias que a la mitad de la poblacion le tomo entre 78.75 y 95 en completar el trabajo. Ademas el 25% de los individuos duraron menos de 68.125, siendo el mejor tiempo de 40 y el maximo de 95.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. (1 pt) Variables categóricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "twenty    17\n",
       "thirty     7\n",
       "forty      6\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ID'].value_counts()\n",
    "data['Gender'].value_counts()\n",
    "data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. (1 pt) Descripción de variables numéricas agrupadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4954/1356082860.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data.groupby('Gender')['SUS','Time'].describe()\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">SUS</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>10.0</td>\n",
       "      <td>74.000</td>\n",
       "      <td>17.328525</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66.250</td>\n",
       "      <td>77.5</td>\n",
       "      <td>87.5</td>\n",
       "      <td>92.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>172.190</td>\n",
       "      <td>15.656341</td>\n",
       "      <td>144.0</td>\n",
       "      <td>165.975</td>\n",
       "      <td>175.50</td>\n",
       "      <td>184.75</td>\n",
       "      <td>188.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>20.0</td>\n",
       "      <td>77.125</td>\n",
       "      <td>13.432948</td>\n",
       "      <td>42.5</td>\n",
       "      <td>69.375</td>\n",
       "      <td>80.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>150.155</td>\n",
       "      <td>21.893582</td>\n",
       "      <td>112.7</td>\n",
       "      <td>135.600</td>\n",
       "      <td>148.65</td>\n",
       "      <td>167.30</td>\n",
       "      <td>185.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SUS                                                     Time  \\\n",
       "       count    mean        std   min     25%   50%   75%   max count   \n",
       "Gender                                                                  \n",
       "F       10.0  74.000  17.328525  40.0  66.250  77.5  87.5  92.5  10.0   \n",
       "M       20.0  77.125  13.432948  42.5  69.375  80.0  87.5  95.0  20.0   \n",
       "\n",
       "                                                                   \n",
       "           mean        std    min      25%     50%     75%    max  \n",
       "Gender                                                             \n",
       "F       172.190  15.656341  144.0  165.975  175.50  184.75  188.3  \n",
       "M       150.155  21.893582  112.7  135.600  148.65  167.30  185.3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Gender')['SUS','Time'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Medias de Centralidad\n",
    "Media y la media(mean)\n",
    "Como primer resultado podemos apreciar que el 33 de los individuos pertenece al genero femenino y el 66% al genero masculino.\n",
    "Ambos generos comparten un resultado similar en la media y mediana respecto a la usabilidad del sistema es decir no existe una diferencia significativa respecto a la calificacion de la usabilidad del sistema, esto se ve reflefajado en la desvicion standar al igual que sus cuartiles. A diferencia de los resultados obtenidos en el tiempo requerido para completar la tarea podemos apreciar que la media y la median del genero masculino difiere con un valor considerablemente menor al del genero femenino. Las medidas de dispercion sujieren que el tiempo empleado por los varores fue menor en relacion a las mujeres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#######                                4. (1 pt) Descripción de variables numéricas agrupadas por \"Age\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4954/2439437661.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data.groupby(\"Age\")['SUS','Time'].describe()\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">SUS</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>forty</th>\n",
       "      <td>6.0</td>\n",
       "      <td>72.083333</td>\n",
       "      <td>14.354152</td>\n",
       "      <td>52.5</td>\n",
       "      <td>63.125</td>\n",
       "      <td>71.25</td>\n",
       "      <td>85.00</td>\n",
       "      <td>87.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>177.550000</td>\n",
       "      <td>5.766368</td>\n",
       "      <td>171.0</td>\n",
       "      <td>173.65</td>\n",
       "      <td>176.0</td>\n",
       "      <td>182.325</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thirty</th>\n",
       "      <td>7.0</td>\n",
       "      <td>74.642857</td>\n",
       "      <td>12.535663</td>\n",
       "      <td>57.5</td>\n",
       "      <td>66.250</td>\n",
       "      <td>75.00</td>\n",
       "      <td>83.75</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>166.214286</td>\n",
       "      <td>22.118350</td>\n",
       "      <td>128.3</td>\n",
       "      <td>154.65</td>\n",
       "      <td>176.3</td>\n",
       "      <td>181.800</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twenty</th>\n",
       "      <td>17.0</td>\n",
       "      <td>78.088235</td>\n",
       "      <td>15.898344</td>\n",
       "      <td>40.0</td>\n",
       "      <td>75.000</td>\n",
       "      <td>80.00</td>\n",
       "      <td>87.50</td>\n",
       "      <td>95.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>146.835294</td>\n",
       "      <td>20.266350</td>\n",
       "      <td>112.7</td>\n",
       "      <td>135.70</td>\n",
       "      <td>148.0</td>\n",
       "      <td>157.700</td>\n",
       "      <td>188.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         SUS                                                          Time  \\\n",
       "       count       mean        std   min     25%    50%    75%   max count   \n",
       "Age                                                                          \n",
       "forty    6.0  72.083333  14.354152  52.5  63.125  71.25  85.00  87.5   6.0   \n",
       "thirty   7.0  74.642857  12.535663  57.5  66.250  75.00  83.75  90.0   7.0   \n",
       "twenty  17.0  78.088235  15.898344  40.0  75.000  80.00  87.50  95.0  17.0   \n",
       "\n",
       "                                                                     \n",
       "              mean        std    min     25%    50%      75%    max  \n",
       "Age                                                                  \n",
       "forty   177.550000   5.766368  171.0  173.65  176.0  182.325  185.0  \n",
       "thirty  166.214286  22.118350  128.3  154.65  176.3  181.800  186.0  \n",
       "twenty  146.835294  20.266350  112.7  135.70  148.0  157.700  188.3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"Age\")['SUS','Time'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos apreciar que existen tres categorias de edad siendo la mayoria personas  con 20 años.  Respecto a la usabilidad del sistema podemoes aprecia que no existe una diferencia significativa en las mediadas de centralidad, aunque si podemos observar una diferencia en la dedia entre las personas con 40 años y las de 20. Las medidas de dispercion muestran valores similares el unico valor a destacar es el minimo.\n",
    "Respecto al tiempo vemos que si existe una gran diferencia en la media y la mediana con las personas de 20 años y el grupo de 30 y 40 lo que sugiere que las personas mayores o iguales a 30 años necesitan mayor tiempo para realizar la actividad. Las medidas de despercion muestran iguales resultados a la poblacion mayor o igual a 30 les toma mas tiempo ejecutar el trabajo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir del análsis descriptivo agrupado que ha realizado, construya los siguientes\n",
    "gráficos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='SUS'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "                                    ##################    5. (1 pt) Diagrama de cajas Gender vs SUS################3\n",
    "\n",
    "sns.boxplot(x = data['Gender'],y = data['SUS']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los resultados mostrados en el box plot sugieren que los resultados obtenidos en la usabilidad de la muestra son muy identicos, es decir que tanto el genero masculino como fenmenino opinan muy similar en cuanto a la usabilidad, incluso los bigotes sigieren lo mismo. Respecto a los cuartiles podemos apreciar que en el genero femenino existe una pequeña diferencia en el primer cuartil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='Time'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mqnq+q94kScN1FgpVtXFI+Y4zzN8GbOuqH0nS2XlHsySpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp1VkoJNmZ5HiS/QO1DyY5lOThJB9P8u0DY7ckmU5yOMkNXfUlSVpYl3sKdwI3zqt9BnhjVV0D/A1wC0CStcAG4Opmm9uSLOmwN0nSEJ2FQlXtAZ6cV/t0VZ1qVu8HVjTL64G7q+pkVR0BpoF1XfUmSRquz3MK7wI+1SwvBx4fGJtpaqdJsinJviT7Tpw40XGLkjReegmFJO8DTgEfnSsNmVbDtq2q26tqqqqmJiYmumpRksbSRaP+wiTvBH4c+JGqmvuLfwZYOTBtBXBs1L1J0rgb6Z5CkhuBXwVuqqpnBobuATYkuTjJFcAaYO8oe5MkdbinkGQXcB2wLMkMcCuzVxtdDHwmCcD9VfXzVXUgyW7gILOHlTZX1fNd9SZJGq6zUKiqjUPKd5xh/jZgW1f9SJLOzjuaJUktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1Br5+xR0uh07djA9Pd1rD3Pfv3Xr1l77AJicnGTLli19tyGNJUNBACxdurTvFiQtAobCIuC/iiUtFp5TkCS1DAVJUquzUEiyM8nxJPsHaj+V5ECSF5JMzZt/S5LpJIeT3NBVX5KkhXW5p3AncOO82n7g7cCewWKStcAG4Opmm9uSLOmwN0nSEJ2FQlXtAZ6cV/vrqjo8ZPp64O6qOllVR4BpYF1XvUmShlss5xSWA48PrM80tdMk2ZRkX5J9J06cGElzkjQuFksoZEithk2sqturaqqqpiYmJjpuS5LGy2IJhRlg5cD6CuBYT71I0thaLDev3QPcleTDwOXAGmDv2TZ64IEHvpzk0a6bGyPLgC/33YQ0hD+b59d3LzTQWSgk2QVcByxLMgPcyuyJ5x3ABPDHSR6qqhuq6kCS3cBB4BSwuaqeP9t3VJXHj86jJPuqaursM6XR8mdzdFI19NC9xpD/42mx8mdzdBbLOQVJ0iJgKGjQ7X03IC3An80R8fCRJKnlnoIkqWUoSJJahsKYS1JJ/sfA+kVJTiT5ZJ99SXOSPJ/koYFfq/vu6UK2WG5eU3++BrwxydKqehb4UeBozz1Jg56tqmv7bmJcuKcggE8BP9YsbwR29diLpB4ZCgK4G9iQ5DXANcBf9NyPNGjpwKGjj/fdzIXOw0eiqh5ujtNuBO7tuR1pPg8fjZChoDn3AB9i9nlV39VvK5L6Yihozk7gqar6QpLreu5FUk8MBQFQVTPA9r77kNQvH3MhSWp59ZEkqWUoSJJahoIkqWUoSJJahoIkqWUoSEMkuSzJXUn+NskDST6f5CfOw+de5xNotZgZCtI8SQL8EbCnqr6nqt4MbABW9NCL9xJppAwF6XRvBZ6rqt+ZK1TVo1W1I8mSJB9M8n+SPJzk56DdA/izJP8ryaEkH23ChSQ3NrX/Dbx97jOTvDbJzuaz/jLJ+qb+M0n+MMkngE+P9Heusee/QqTTXQ08uMDYzcw+DuT7k1wMfC7J3F/c39dsewz4HPDPkuwDfpfZoJkG/mDgs94H/GlVvSvJtwN7k3y2GftB4JqqevI8/r6kszIUpLNI8tvAPweeAx4Frknyk83wtwFrmrG9zeNCSPIQsBr4KnCkqh5p6v8T2NRsez1wU5JfbtZfA6xqlj9jIKgPhoJ0ugPAO+ZWqmpzkmXAPuAxYEtV3Te4QfMQwZMDped58f+vhZ4lE+AdVXV43mf9E2bfiCeNnOcUpNP9KfCaJO8eqF3S/Pc+4N1JvgUgyZVJXnuGzzoEXJHkDc36xoGx+4AtA+cevu+8dC+9DIaCNE/NPiXyXwJvSXIkyV7gvwO/CvwecBB4MMl+4L9yhj3uqvo6s4eL/rg50fzowPD7gW8BHm4+6/0d/Hakb4pPSZUktdxTkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1/j8gBVR/k1fmKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "                                            ##############   6. (1 pt) Diagrama de cajas Gender vs Time:\n",
    "\n",
    "sns.boxplot(x = data['Gender'],y = data['Time']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mediante los gráficos boxplot podemos apreciar que realmente existe una diferencia en el tiempo requerido para realizar la actividad ya que la media y la mediana,ya que se puede apreciar un desfase en ambos gráficos vemos que la mediana en en los varones apunta cercano a los 150 en tiempo y en la mujer en 179. Mediante los bigotes también puede se puede apreciar que en el género masculino existe una mayor dispersión de los datos en comparación al género femenino un dato curioso es que el valor mínimo en los valor en los varones es de 102 y en las mujeres el valor mínimo que el individuo utilizó para completar la actividad fue de 142."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='SUS'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " ##############   7. (1 pt) Diagrama de cajas Gender vs Time:\n",
    "sns.boxplot(x = data['Age'],y = data['SUS']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el estudio de las edades podemos observar la primera caja de aquellas personas con 20 años que la usabilidad del sistema tiene una puntuación más alta en comparación a las personas de 30 y 40 años ademas ligeramente las personas de 40 años escogieron una usabilidad más baja en comparación a sus otros miembros. Mediante la observación de los bigotes podemos hacer apreciar que el valor máximo se encuentra en las personas de 20 años siendo este cercano a 100 por otra parte tenemos que el valor de usabilidad más bajo se encuentra en las personas de 40 años.R especto a los cuartile s los tres grupos mantienen una distribución semejante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Time'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " ##############   8. (1 pt) Diagrama de cajas Gender vs Time:\n",
    "sns.boxplot(x = data['Age'],y = data['Time']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El tiempo nos muestra que las personas de 40 años necesitaron un mayor tiempo en completar la tarea asignada. Mediante los cuartiles y bigotes  podemos apreciar que la distribución de los datos es menor en comparación a los de 20 y 30 años. Aquellas personas de 20 y 30 años muestran una mayor dispersión en los resultados obtenidos , ya que las personas de 20 años contienen los bigotes más largos siendo el valor máximo de 190 y el mínimo de 110 sin embargo los cuartile muestran una distribución un poco más compacta. Por otra parte en  las personas de 30 años el boxplot sujiere que el 25% de su población requirió un tiempo mayor a 178 y  otro 25% se mantuvo en un rango de 160 y 180. Los boxplot sugieren que las personas de 20 años les toma menos tiempo realizar la actividad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. (2 pts) Diagrama de dispersión SUS vs Time agrupados por Gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f53b9d07fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 404.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"SUS\", y='Time', hue=\"Gender\", data=data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    #Qúe relación observa entre las varibles SUS y Time.\n",
    "    #Discuta la relación de estas variables de acuerdo al género\n",
    "Primero, sino dividiéramos a la población en el género masculino y femenino podemos apreciar que no existe ninguna relación entre el tiempo y la variable de usabilidad, sin embargo divdiendo en dos géneros masculino y femenino podemos apreciar que existe una débil relación entre el tiempo y la usabilidad (a mayor usabilidad menor tiempo requerido para completar la actividad). Segundo, Realmente existe una diferencia entre el género y el tiempo requerido para completar la actividad, ya que se puede apreciar como la mayoría de puntos azules que representan el género masculino se encuentran debajo de un grupo de puntos que representan el género femenino (tomate). Es decir podemos apreciar dos grupos en la representacion grafica de la muestra dispersa.\n",
    "\n",
    "##Se corresponde este análisis con el observado en los diagramas de caja.\n",
    "###Justifique su respuesta.\n",
    "\n",
    "Efectivamente este análisis lo podemos apreciar en los boxplot  donde el grupo de elementos del género masculino mostraban un menor tiempo de ejecución en las tareas respecto al genero fememnino. En nuestra gráfica de dispersión se puede apreciar dos grupos en la marca de agua de color azul el grupo masculino y  de color tomate el grupo femenino. Ademas, muestran una regresión qué difiere con una distancia de aproximadamente 23 en el tiempo en el eje y.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.violinplot(x=\"SUS\", y='Time', hue=\"Gender\", data=data)"
   ]
  }
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