{"cells":[{"cell_type":"markdown","source":"# ¿Qué factores están impulsando la discriminación salarial entre hombres y mujeres en su organización?","metadata":{"id":"KuwOrzF2oHiW","cell_id":"e5b076ad9d5b45d49db2db9a5b32dc34","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"! pip install chart_studio","metadata":{"id":"jxE1Y7zpoHic","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"cfa4d1711649488bb43d8d209a959822","outputId":"a3b7fe77-f908-4d59-ff04-06acde5ab1da","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":3042,"user_tz":300,"timestamp":1640982496433},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Requirement already satisfied: chart_studio in /usr/local/lib/python3.7/dist-packages (1.1.0)\nRequirement already satisfied: plotly in 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(2.10)\n"}],"execution_count":3},{"cell_type":"code","source":"! pip install bqplot\n! pip install pingouin","metadata":{"id":"1ZF9C1LNoHie","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"cadc1b90d7ad400c9d72de3c80589385","outputId":"2bda2585-343b-4a2f-9433-e8811172f7c3","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":26549,"user_tz":300,"timestamp":1640982467601},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Collecting bqplot\n  Downloading bqplot-0.12.31-py2.py3-none-any.whl (1.2 MB)\n\u001b[?25l\r\u001b[K     |▎                               | 10 kB 21.5 MB/s eta 0:00:01\r\u001b[K     |▌                               | 20 kB 26.1 MB/s eta 0:00:01\r\u001b[K     |▉                               | 30 kB 31.4 MB/s eta 0:00:01\r\u001b[K     |█                    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Downloading pingouin-0.5.0.tar.gz (182 kB)\n\u001b[K     |████████████████████████████████| 182 kB 8.0 MB/s \n\u001b[?25hRequirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.19.5)\nCollecting scipy>=1.7\n  Downloading scipy-1.7.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.1 MB)\n\u001b[K     |████████████████████████████████| 38.1 MB 1.3 MB/s \n\u001b[?25hRequirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.1.5)\nRequirement already satisfied: matplotlib>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from pingouin) (3.2.2)\nRequirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from pingouin) (0.11.2)\nCollecting statsmodels>=0.12.0\n  Downloading statsmodels-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n\u001b[K     |████████████████████████████████| 9.8 MB 46.8 MB/s \n\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.0.1)\nCollecting pandas_flavor>=0.2.0\n  Downloading pandas_flavor-0.2.0-py2.py3-none-any.whl (6.6 kB)\nCollecting outdated\n  Downloading outdated-0.2.1-py3-none-any.whl (7.5 kB)\nRequirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from pingouin) (0.8.9)\nRequirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (1.3.2)\nRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (0.11.0)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (3.0.6)\nRequirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (2.8.2)\nRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->pingouin) (2018.9)\nRequirement already satisfied: xarray in /usr/local/lib/python3.7/dist-packages (from pandas_flavor>=0.2.0->pingouin) (0.18.2)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.0.2->pingouin) (1.15.0)\nRequirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.12.0->pingouin) (0.5.2)\nCollecting littleutils\n  Downloading littleutils-0.2.2.tar.gz (6.6 kB)\nRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from outdated->pingouin) (2.23.0)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (1.24.3)\nRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (3.0.4)\nRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (2.10)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (2021.10.8)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pingouin) (3.0.0)\nRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pingouin) (1.1.0)\nRequirement already satisfied: setuptools>=40.4 in /usr/local/lib/python3.7/dist-packages (from xarray->pandas_flavor>=0.2.0->pingouin) (57.4.0)\nBuilding wheels for collected packages: pingouin, littleutils\n  Building wheel for pingouin (setup.py) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for pingouin: filename=pingouin-0.5.0-py3-none-any.whl size=193661 sha256=59b2dd01b8178f4df34fda405dc0d6a6192410552298ee6000de4a2480c558d3\n  Stored in directory: /root/.cache/pip/wheels/14/46/f9/cedd81d68d2515c24bbbd000d5b347e4fe092ccc4b568f7f70\n  Building wheel for littleutils (setup.py) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for littleutils: filename=littleutils-0.2.2-py3-none-any.whl size=7048 sha256=f6da31dc27ae1801a1dcc181d3324cc9e0b0cda9e9a41b76466fdd256fb67ea8\n  Stored in directory: /root/.cache/pip/wheels/d6/64/cd/32819b511a488e4993f2fab909a95330289c3f4e0f6ef4676d\nSuccessfully built pingouin littleutils\nInstalling collected packages: scipy, littleutils, statsmodels, pandas-flavor, outdated, pingouin\n  Attempting uninstall: scipy\n    Found existing installation: scipy 1.4.1\n    Uninstalling scipy-1.4.1:\n      Successfully uninstalled scipy-1.4.1\n  Attempting uninstall: statsmodels\n    Found existing installation: statsmodels 0.10.2\n    Uninstalling statsmodels-0.10.2:\n      Successfully uninstalled statsmodels-0.10.2\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nalbumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\nSuccessfully installed littleutils-0.2.2 outdated-0.2.1 pandas-flavor-0.2.0 pingouin-0.5.0 scipy-1.7.3 statsmodels-0.13.1\n"}],"execution_count":1},{"cell_type":"code","source":"### Load relevant packages\nimport pandas                  as pd\nfrom   scipy import stats\nimport numpy                   as np\nimport matplotlib.pyplot       as plt\nimport seaborn                 as sns\nimport statsmodels.formula.api as sm\nimport chart_studio.plotly     as py\n# https://community.plot.ly/t/solved-update-to-plotly-4-0-0-broke-application/26526/2\nimport os\n\n#%matplotlib inline\n#plt.style.use('ggplot')\nfrom bokeh.resources import INLINE\nimport bokeh.io\nfrom bokeh import *\nimport pingouin\nbokeh.io.output_notebook(INLINE)","metadata":{"id":"9Xyfyry_oHif","cell_id":"acf9c8cb902c49ae885e9e8a3d5b494f","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":1986,"user_tz":300,"timestamp":1640982509583},"deepnote_cell_type":"code"},"outputs":[],"execution_count":4},{"cell_type":"markdown","source":"## Objetivos(2 min)\n\nEn este caso, estableceremos una comprensión básica de las estadísticas necesarias para la regresión lineal y luego introduciremos la regresión lineal comenzando con 2 parámetros. Esperamos que los estudiantes comprendan a fondo los componentes funcionales de un modelo de regresión lineal, como la interpretación de los coeficientes y la comprensión de varias métricas para evaluar adecuadamente el rendimiento del modelo.","metadata":{"id":"asX_-5A7oHil","cell_id":"252f47a1692f420bbbaaa2a21aa91531","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"## Introducción(5 min)","metadata":{"id":"bZm9IN9voHin","cell_id":"20abaeb7f26643ceab69638a6e73847b","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"**Contexto empresarial**. Eres un científico de datos en una gran organización. Su empresa está pasando por una revisión interna de sus prácticas de contratación y compensación a los empleados. En los últimos años, su empresa ha tenido poco éxito en la conversión de candidatas de alta calidad que deseaba contratar. La gerencia plantea la hipótesis de que esto se debe a una posible discriminación salarial y quiere averiguar qué la está causando.","metadata":{"id":"NN9yrtjZoHin","cell_id":"049e291afbbb4f6d837996f984fc67de","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"**Problema empresarial** Como parte de la revisión interna, el departamento de recursos humanos se ha acercado a usted para investigar específicamente la siguiente pregunta: <b> \"En general, ¿se les paga más a los hombres que a las mujeres en su organización? Si es así, ¿qué conduciendo esta brecha? \"","metadata":{"id":"AaOz2F5doHio","cell_id":"c9e43923e31e442086f8bece2162e1ae","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"**Contexto analítico**. El departamento de recursos humanos le ha proporcionado una base de datos de empleados que contiene información sobre varios atributos como rendimiento, educación, ingresos, antigüedad, etc. Usaremos técnicas de regresión lineal en este conjunto de datos para resolver el problema comercial descrito anteriormente. Veremos cómo la regresión lineal cuantifica la correlación entre la variable dependiente (salario) y las variables independientes (por ejemplo, educación, ingresos, antigüedad, etc.)\n\nEl caso está estructurado de la siguiente manera: (1) realizaremos un análisis de datos exploratorio para investigar visualmente las diferencias salariales; (2) utilizar los conocimientos observados para ajustar formalmente los modelos de regresión; y finalmente (3) abordar el tema de la discriminación salarial.","metadata":{"id":"MNFocargoHip","cell_id":"6bb1e7830ea840eb87db7f534cbfa126","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"## Exploración de data (30 min)","metadata":{"id":"0hUKZa8cNs0n","cell_id":"127a91329b8444e9941db5823c16cb89","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/gdrive')\n# Establecer ruta de acceso en drive\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/gdrive/My Drive\")","metadata":{"id":"ohieAEj0Ns0p","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"ea09e2d1c8ee43eeab603a6945f6f519","outputId":"e52ea4bc-bc01-4346-c3c4-c6191edd01fd","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":26169,"user_tz":300,"timestamp":1640983214055},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Mounted at /content/gdrive\n/content\n"}],"execution_count":5},{"cell_type":"code","source":"Data = pd.read_csv('glassdoordata.csv')","metadata":{"id":"W-ouWV0NoHiq","cell_id":"1f3107f857434d3abeecc4e888b5a6fd","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":469,"user_tz":300,"timestamp":1640983216241},"deepnote_cell_type":"code"},"outputs":[],"execution_count":6},{"cell_type":"code","source":"Data.head()","metadata":{"id":"avJmGwoFoHir","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"9a10a23ed9264ae2948b4184a7f0018b","outputId":"32f72aa2-47c6-4518-ac1e-de7de65794ea","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":9,"user_tz":300,"timestamp":1640983218161},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/html":"\n  <div id=\"df-896dd2fa-7ced-46fe-8fc6-84e06d1d2ddb\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>jobtitle</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>performance</th>\n      <th>education</th>\n      <th>department</th>\n      <th>seniority</th>\n      <th>income</th>\n      <th>bonus</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Graphic Designer</td>\n      <td>Female</td>\n      <td>18</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Operations</td>\n      <td>2</td>\n      <td>42363</td>\n      <td>9938</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Management</td>\n      <td>5</td>\n      <td>108476</td>\n      <td>11128</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Warehouse Associate</td>\n      <td>Female</td>\n      <td>19</td>\n      <td>4</td>\n      <td>PhD</td>\n      <td>Administration</td>\n      <td>5</td>\n      <td>90208</td>\n      <td>9268</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>20</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Sales</td>\n      <td>4</td>\n      <td>108080</td>\n      <td>10154</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graphic Designer</td>\n      <td>Male</td>\n      <td>26</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Engineering</td>\n      <td>5</td>\n      <td>99464</td>\n      <td>9319</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-896dd2fa-7ced-46fe-8fc6-84e06d1d2ddb')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-896dd2fa-7ced-46fe-8fc6-84e06d1d2ddb button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-896dd2fa-7ced-46fe-8fc6-84e06d1d2ddb');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  ","text/plain":"              jobtitle  gender  age  ...  seniority  income  bonus\n0     Graphic Designer  Female   18  ...          2   42363   9938\n1    Software Engineer    Male   21  ...          5  108476  11128\n2  Warehouse Associate  Female   19  ...          5   90208   9268\n3    Software Engineer    Male   20  ...          4  108080  10154\n4     Graphic Designer    Male   26  ...          5   99464   9319\n\n[5 rows x 9 columns]"},"metadata":{},"execution_count":7}],"execution_count":7},{"cell_type":"code","source":"Data.shape","metadata":{"id":"IzrfsaeEoHir","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"abc73c9e0008462fad8dd87212bc6ecb","outputId":"e3bc9b4f-7e24-47ae-a32d-23232551fc31","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":345,"user_tz":300,"timestamp":1640983224766},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"(1000, 9)"},"metadata":{},"execution_count":8}],"execution_count":8},{"cell_type":"markdown","source":"Las variables disponibles son:\n* **job title**: el título del trabajo (por ejemplo, \"Diseñador gráfico\", \"Ingeniero de software\", etc.);\n* **gender**: hombre o mujer;\n* **age**: edad;\n* **performance**: en una escala del 1 al 5, siendo 1 el más bajo y 5 el más alto;\n* **education**: diferentes niveles de educación (por ejemplo, \"Universidad\", \"Doctorado\", \"Maestría\", \"Escuela secundaria\");\n* **department**: diferentes departamentos de la organización (por ejemplo, \"Operaciones\", \"Gestión\", etc.);\n* **seniority**: en una escala de 1 a 5, siendo 1 la más baja y 5 la más alta;\n* **income, bonus**: ambos expresados en dólares","metadata":{"id":"MgGJHvyvoHis","cell_id":"ba46916db4764c88bad7e1db0607ec5c","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"Como estamos interesados en la compensación total, creemos una nueva columna llamada <code>pay</code>:","metadata":{"id":"4pz9X2W1oHis","cell_id":"0745457b512b4cdfbb1e8ec3362d9875","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"Data['pay'] = Data['income'] + Data['bonus']# Crear una variable que tenga ","metadata":{"id":"zGdNvgChoHit","cell_id":"c79b54815f0941e3b6a7867f96c566c7","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":326,"user_tz":300,"timestamp":1640983329270},"deepnote_cell_type":"code"},"outputs":[],"execution_count":10},{"cell_type":"code","source":"Data.head()","metadata":{"id":"WzlrgU2ToHit","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"e090d06470044abfa482edc54d43dde0","outputId":"5d658b3d-bc90-4bc0-c6fb-b845a4549ea1","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":19,"user_tz":300,"timestamp":1640983330625},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/html":"\n  <div id=\"df-85eea3fb-b76f-44b0-b73a-cd92e963bc25\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>jobtitle</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>performance</th>\n      <th>education</th>\n      <th>department</th>\n      <th>seniority</th>\n      <th>income</th>\n      <th>bonus</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Graphic Designer</td>\n      <td>Female</td>\n      <td>18</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Operations</td>\n      <td>2</td>\n      <td>42363</td>\n      <td>9938</td>\n      <td>52301</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Management</td>\n      <td>5</td>\n      <td>108476</td>\n      <td>11128</td>\n      <td>119604</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Warehouse Associate</td>\n      <td>Female</td>\n      <td>19</td>\n      <td>4</td>\n      <td>PhD</td>\n      <td>Administration</td>\n      <td>5</td>\n      <td>90208</td>\n      <td>9268</td>\n      <td>99476</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>20</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Sales</td>\n      <td>4</td>\n      <td>108080</td>\n      <td>10154</td>\n      <td>118234</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graphic Designer</td>\n      <td>Male</td>\n      <td>26</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Engineering</td>\n      <td>5</td>\n      <td>99464</td>\n      <td>9319</td>\n      <td>108783</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85eea3fb-b76f-44b0-b73a-cd92e963bc25')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 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filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-85eea3fb-b76f-44b0-b73a-cd92e963bc25 button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-85eea3fb-b76f-44b0-b73a-cd92e963bc25');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  ","text/plain":"              jobtitle  gender  age  ...  income  bonus     pay\n0     Graphic Designer  Female   18  ...   42363   9938   52301\n1    Software Engineer    Male   21  ...  108476  11128  119604\n2  Warehouse Associate  Female   19  ...   90208   9268   99476\n3    Software Engineer    Male   20  ...  108080  10154  118234\n4     Graphic Designer    Male   26  ...   99464   9319  108783\n\n[5 rows x 10 columns]"},"metadata":{},"execution_count":11}],"execution_count":11},{"cell_type":"markdown","source":"# Algunos graficos descriptivos","metadata":{"id":"MUK4asVcOhpm","cell_id":"78f3f20cd072453c97a17119d2a794ff","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"sns.boxplot(x='gender', y = 'pay', data = Data) \n#Data.boxplot(grid= False, column = ['pay'], by = ['gender'])\nplt.title(\"Pay vs Gender\");","metadata":{"id":"B3z-JvqSOkdR","colab":{"height":295,"base_uri":"https://localhost:8080/"},"cell_id":"9d0ad60a401545358fe419513001cd6d","outputId":"f6574913-78ae-4196-f871-9ada79b091ff","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":12},{"cell_type":"code","source":"plt.scatter(Data['age'],Data['pay'])\nplt.title(\"Pay vs. Age\", fontsize=20, verticalalignment='bottom');\nplt.xlabel(\"Age\");\nplt.ylabel(\"Pay\");","metadata":{"id":"RXxBza4SPBcw","colab":{"height":304,"base_uri":"https://localhost:8080/"},"cell_id":"049ff94ef0c44996b44edbe3aef428dc","outputId":"fa85283c-78e2-4045-b8af-0aba99e646af","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":13},{"cell_type":"code","source":"sns.boxplot(x='education', y = 'pay', data = Data) \nplt.title(\"Pay vs. Education\", fontsize=20, verticalalignment='bottom');","metadata":{"id":"rXQMbqTSPpBx","colab":{"height":304,"base_uri":"https://localhost:8080/"},"cell_id":"0b3df2f194be418cb40d651b6ce2e5da","outputId":"c42c8fea-d8f8-4f04-9d17-89cbc2a06757","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":14},{"cell_type":"code","source":"sns.boxplot(x='seniority', y = 'pay', data = Data) \nplt.title(\"Pay vs. Seniority\", fontsize=20, verticalalignment='bottom');","metadata":{"id":"ne72z0ORQJbU","colab":{"height":304,"base_uri":"https://localhost:8080/"},"cell_id":"3b7a6a15914e43e4b6617c29e2a02db7","outputId":"4cd6869f-a64a-461e-c50f-5e997fedef3c","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":15},{"cell_type":"code","source":"sns.boxplot(x='education', y = 'pay', hue = 'gender', data = Data) \nplt.title(\"Pay vs. Education\", fontsize=20, verticalalignment='bottom');","metadata":{"id":"SAGQPeO-Qm9S","colab":{"height":304,"base_uri":"https://localhost:8080/"},"cell_id":"ecde5788fb0b4bad9e7314ef6b4c6509","outputId":"3a4fe13b-273e-4cc3-e6c9-7c84b339f853","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":16},{"cell_type":"code","source":"sns.boxplot(x='jobtitle', y = 'pay', hue = 'gender',data = Data) \nplt.title(\"Pay vs. Jobtitle\", fontsize=20, verticalalignment='bottom')\nplt.xticks(rotation=90);","metadata":{"id":"7Oduye1HQud0","colab":{"height":401,"base_uri":"https://localhost:8080/"},"cell_id":"27ad5315d9e8421eb54b25c0c2ea7472","outputId":"39b0f8c0-2963-40a6-a55b-9cab5449e13d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos 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aS5h1Nc5wGhJ7YGVwPB4+mTgWKAKWA6cCWBmiyT9HHghnndlpnMfOJcwIq0zoSP/4Vh+LXCXpLOB94GTi/QUnWuUv/3tb2mH4FziijlabGgdh/bLc64B36/jccYCY/OUzwD2yFO+EDi8ScE655wrKJ+h75xzruA8uTjnnCs4Ty6u7CxcuJALLriAhQsXph2Kc22WJxdXdsaPH89rr73GhAkTGj45JRUVFey9997rfpq7Mm1j9O7dm08++aRoj+/Kk29z7MrKwoULmTJlCmbGlClTGDZsGN26dav3Puf98Cd8/Mmies9pim222pIbf/vres/p3LlzncvDONcaeHJxZWX8+PGsXbsWCIs8TpgwgQsvvLDe+3z8ySL+0/2r9Z7TJB890ay7zZw5kx/+8IcsXbqUrbbainHjxrHtttsyaNAg9tlnH5566imWLVvGhAkTuOaaa3jttdc45ZRT1m32dMIJJzBnzhxWrlzJiBEjGD58eK1r/PWvf+X6669n9erVDBw4kD/+8Y9UVFS06Om68uTNYq6sTJs2bd3GWdXV1UydOjXliPJbsWLFuiaxb3zjGw0urd+xY0dmzJjBd7/7XYYMGcIf/vAHZs2axbhx49b1LY0dO5aZM2cyY8YMrr/++lp9Tm+++SZ33nknTz/9NC+//DIVFRXcfvvtiT5v13Z4zcWVlSOOOILJkydTXV1N+/bt1y1VX2pym8VmzZpV79L6xx9/PAB77rknu++++7pjO+64I3PmzKFbt25cf/313HfffQDMmTOHd955Z4Mmwccee4yZM2ey//77AyHBbbPNNsV9oq7N8uTiykplZSVTpkwBQqf5sGHDUo6ocRpaWj+zTH+7du02WLK/Xbt2VFdXM336dKZNm8YzzzxDly5dGDRoECtXrqx1jcrKSq655priPRFXNjy5uLLSrVs3Bg8ezAMPPMDgwYMb7MwvFdlL6x900EGsWbOGf//73+y+++6Nuv/ixYvZYost6NKlC2+99RbPPvtsrXMOP/xwhgwZwoUXXsg222zDokWLWLJkCTvssEOhn44rohtuuIGqqioA5s2bB0CPHj3WHe/bty/nn39+0ePwPhdXdiorK9lzzz1bTa0FWr60/uDBg6murmbXXXdl5MiRHHjggbXO2W233bjqqqs46qij6N+/P0ceeSTz58/P82iutVixYgUrVqxI5dpFXXK/NfEl911G7pLyaQxFLmW+5H7rMWLECABGjx5d73nZtR2oXeOpr7ZT15L73izmXANacyJwrjkKUdvx5OKcc2Uut1bS2BpPfbzPxTnnXMF5zaUNq2/USFIjRpxz5cmTS5lIa8SIc648eXJpw7JrJoVoQ3XOucYqWp+LpLGSPpY0K6f8fElvSXpd0q+yyi+WVCXpbUlHZ5UPjmVVkkZmlfeR9Fwsv1NSx1i+UbxdFY/3LtZzdK5YJHH66aevu11dXc3WW2/NcccdV+/9pk+f3uA5ziWhmDWXccCNwLpNMyQdCgwB9jKzVZK2ieW7AacCuwPbAdMk7RTv9gfgSGAu8IKkSWb2BvBL4HdmNlHSn4CzgZvi70/NrK+kU+N5pxTxebo27pIfncfiTz4q2ONtvlV3fvGbG+s9Z+ONN2bWrFmsWLGCzp07M3Xq1A1mWTtX6oqWXMzsyTy1hu8B15rZqnjOx7F8CDAxlr8nqQo4IB6rMrN3ASRNBIZIehM4DDgtnjMeuJyQXIbEvwHuAW6UJPPZoq6ZFn/yERd98a2CPd4v/9O484499lgeeughTjzxRO644w6GDh3KU089BcDzzz/PiBEjWLlyJZ07d+bWW29l55133uD+y5Yt4/zzz2fWrFmsWbOGyy+/nCFDhhTseThXn6SHIu8EfCU2Vz0haf9Y3gOYk3Xe3FhWV3k34DMzq84p3+Cx4vHF8XznWpVTTz2ViRMnsnLlSl599VUGDhy47tguu+zCU089xUsvvcSVV17JJZdcUuv+V199NYcddhjPP/88jz/+OD/5yU9YtmxZkk/BlbGkO/TbA1sCBwL7A3dJ2jHhGNaRNBwYDtCrV6+0wnAur/79+zN79mzuuOMOjj322A2OLV68mMrKSt555x0ksWbNmlr3f/TRR5k0aRLXXXcdACtXruSDDz7wpVtcIpJOLnOBv8cmquclrQW2AuYB22ed1zOWUUf5QqCrpPaxdpJ9fuax5kpqD2wez6/FzMYAYyCsLdbyp+dcYR1//PH8+Mc/Zvr06Rts7nXZZZdx6KGHct999zF79mwGDRpU675mxr333lurucy5JCTdLPZ/wKEAscO+I/AJMAk4NY706gP0A54HXgD6xZFhHQmd/pNicnocODE+biVwf/x7UrxNPP4P729xrdVZZ53FqFGj2HPPPTcoX7x48boO/nHjxuW979FHH80NN9xA5u3/0ksvFTVW57IVcyjyHcAzwM6S5ko6GxgL7BiHJ08EKi14HbgLeAOYAnzfzGpireQ84BHgTeCueC7ARcAPY+d/N+CWWH4L0C2W/xBYN3zZudamZ8+eXHDBBbXKf/rTn3LxxRezzz77rNu2Oddll13GmjVr6N+/P7vvvjuXXXZZscN1bp1ijhYbWseh0/MVmtnVwNV5yicDk/OUv8v6EWXZ5SuBk5oUrHP12Hyr7o0e4dXYx2vI0qVLa5UNGjRoXfPXQQcdxL///e91x6666qpa53Tu3Jmbb7655QE71ww+Q9+5BjQ0J8U5V5snF+dci/gCqS4fTy7OuYLxBVJdhicX5/IwMySlHUbJyTfw0hdIdfn4ZmHO5ejUqRMLFy7M+0FazsyMhQsX0qlTp7RDca2A11ycy9GzZ0/mzp3LggUL0g6l5HTq1ImePXumHYZrBTy5OJejQ4cO9OnTJ+0wnGvVvFnMOedcwXnNxTnnWrns4eDZMmWZgRa5ijlU3JOLcy20cOFCrrjiCkaNGkW3br67g0teVVUVL896k5ouW25Q3m51GJQy893am91VLF9U1Jg8uTjXQuPHj+e1115jwoQJXHjhhWmH48pUTZctWbHLsQ2fGHV+q9aqWgXlfS7OtcDChQuZMmUKZsaUKVM2WBbfuXLmycW5Fhg/fjxr164FoKamhgkTJqQckXOlwZOLcy0wbdq0dUveV1dXM3Xq1JQjcq40eHJxrgWOOOII2rcPXZft27fnyCOPTDki50qDd+g71wKVlZVMmTIFgIqKCoYNG5ZyRK1DfSspQ+tdTdlXiF7Pk4tzLdCtWzcGDx7MAw88wODBg+sditxWP1Bbqq2upNxWn1djeXJxroUqKyuZPXt2k2ot5f7B01ZXUm5Nz6vdys+pqlqSd4JlfZMvG/slyJOLK6py+LberVs3rr/++gbPa00fPK7t09o12KpVrHp/Rq1jHdeE7vjcYx8srWj04xctuUgaCxwHfGxme+Qc+xFwHbC1mX2isHHGaOBYYDlwhpm9GM+tBC6Nd73KzMbH8v2AcUBnYDIwwsxM0pbAnUBvYDZwspl9Wqzn6RovzW/r3ha+nr8WLqPXJjVcsu/njT7/Fy9u1uhzi1lzGQfcCGww8F/S9sBRwAdZxccA/eLPQOAmYGBMFKOAAYABMyVNisniJuAc4DlCchkMPAyMBB4zs2sljYy3LyrSc3QNKMVv6+XeJJXNXwtXLEVLLmb2pKTeeQ79DvgpcH9W2RBggoXdmZ6V1FXStsAgYKqZLQKQNBUYLGk6sJmZPRvLJwAnEJLLkHg/gPHAdBJOLuXQFNTalGKSS4u/Fi4Jifa5SBoCzDOzV3K2kO0BzMm6PTeW1Vc+N085QHczmx///hDoXrAn0Az+zdA5V44SSy6SugCXEJrEEhH7YOrcq1bScGA4QK9evfKe05z2af9m6Jwrd0nWXL4I9AEytZaewIuSDgDmAdtnndszls1jfRNXpnx6LO+Z53yAjyRta2bzY9Pax3UFZGZjgDEAAwYMaHDD9HKvhZRCR3CpNDn6a+Fc/RJLLmb2GrBN5rak2cCAOFpsEnCepImEDv3FMTk8AvxC0hbxbkcBF5vZIkmfSzqQ0KE/DLghnjMJqASujb+z+3aazGsh+ZVCoi2FGKA04iiFGJzLVsyhyHcQah1bSZoLjDKzW+o4fTJhGHIVYSjymQAxifwceCGed2Wmcx84l/VDkR+OPxCSyl2SzgbeB04u4NMqa6WQaEshhlKJoxRicK4uxRwtNrSB472z/jbg+3WcNxYYm6d8BrBHnvKFwOFNDNc514p5E2Hp8Rn6zrk2xZsIS4MnF+dcq+dNhKXH93NxzjlXcJ5cnHPOFZwnF+eccwXnfS7OOZeitjrSzZOLc86ViOaOdJs3bx4VyxfT+a3Jjb9TTTUfLS9e45UnlzYk+xtQrkLsLOecK7y2OtLNk0sbUlVVxTuvv0SvTWpqHSvEznLOudLUo0cPPlzVnhW7HNvo+2zy4m1077K6aDF5cmljirmznHOuDVlbw/tLKpr0GfD+kgo2njev4RPx0WLOOeeKwGsuzjlXjtpVsMOmq5rc0rFR1ki2eh++uXE555xzdfHk4pxzruA8uTjnnCs4Ty7OOecKzpOLc865gvPk4pxzruCKNhRZ0ljgOOBjM9sjlv0a+DqwGvgPcKaZfRaPXQycDdQAF5jZI7F8MDAaqAD+YmbXxvI+wESgGzAT+I6ZrZa0ETAB2A9YCJxiZrOL9TydKzdtZZmh3OeRu2hkqcXb2hRznss44EbCB33GVOBiM6uW9EvgYuAiSbsBpwK7A9sB0yTtFO/zB+BIYC7wgqRJZvYG8Evgd2Y2UdKfCInppvj7UzPrK+nUeN4pRXyeRddWV011rVNVVRUvz3qTmi5b1jrWbrUBMPPdjzYor1i+KJHYWsK3Ry6soiUXM3tSUu+cskezbj4LnBj/HgJMNLNVwHuSqoAD4rEqM3sXQNJEYIikN4HDgNPiOeOBywnJZUj8G+Ae4EZJMjMr2JNLkf8HcKWgpsuWTVrHqkmr9SYk9wtZW1o0shSkOUP/LODO+HcPQrLJmBvLAObklA8kNIV9ZmbVec7vkblPrCEtjud/UugnkJS2umqqc67tSiW5SPoZUA3cnsb1s+IYDgwH6NWrV5qhOOdaobbS/1QMiScXSWcQOvoPz2qqmgdsn3Vaz1hGHeULga6S2sfaS/b5mceaK6k9sHk8vxYzGwOMARgwYECbaDZzziWnrfY/FUKiySWO/Pop8FUzW551aBLwN0m/JXTo9wOeBwT0iyPD5hE6/U8zM5P0OKHPZiJQCdyf9ViVwDPx+D/aSn+La33q+mZb37daKI9vtm1FW+h/KoZiDkW+AxgEbCVpLjCKMDpsI2CqJIBnzey7Zva6pLuANwjNZd83s5r4OOcBjxCGIo81s9fjJS4CJkq6CngJuCWW3wLcFgcFLCIkJOdSUdcGbnVt3ga+gZtLzgdL8+/nktn+uHuXtbXO79fIx25UcpE0ExgL/M3MPm3MfcxsaJ7iW/KUZc6/Grg6T/lkoFaqjyPIDshTvhI4qTExOpcE38DNlSJr1wF17MhGO/StdWx1rFnnHutHqFU3RmNrLqcAZxLmmcwAbgUebYvNTc3toANvynDOtR5rO21G3x275x15WohRqY1KLmZWBfxM0mWEzvixQI2kW4HRZtZmeqia00EH5dNJ51xzzZs3L+8XM//SVhgVyxfV6s9ptzLUmNd2ql0bDp9Z3YsWT6P7XCT1J9RejgXuJQwjPhj4B7B3UaJLSVM76KDwnXTN6Qiuqqpi+w4FDaMkeKd4y5TKcNkVK1Z4/1OR1NVUVVW1JBzfMV8S6d7oJq7maEqfy2eEPpORcSY9wHOSvlys4MpZXTWoemtPy5ZD10TCS1SzXguvSa5T16ACqPuDvVgf6t7/VBx1fQlIc9J1Y2suJ2WWYMllZt8sYDwuS1NrUJu8eBthTdC2x4d7tox/qLukNbbP5V1JXyMsLNkpq/zKYgXW2rRb+TlVVUtqNS8k2nSztob3l+QfWliX95dUsPG8eXmP+eAG51xzNbZZ7E9AF+BQ4C+EyYnPFzGuVkdr12CrVtVqXmjN7ck+uME511yNbRb7kpn1l/SqmV0h6TfAw8UMrDVKvemhXQU7bLqqyTFslLV8f65SGNzgXFtTDiPnGptcMuu8L5e0HWGtrm2LE5Jr7UqiibDAmttEOG/ePLYqamSuNSqHkXONTS4PSuoK/Iqw6yOE5jHnavEmwqBi+SI26dQB2uDwcNdyqbd0FFljk8t1wPeArxAWhHyKsDGXc3ml/R+nrtoTNH9uR7NGrK1d0ujzG8PnP7nWorHJZTywBCsFVSsAACAASURBVLg+3j6NsH3xycUIyrmWqqv2BMnP7Sgkn//kGpL9BSTfl46kmp8bm1z2MLPdsm4/LumNYgTkXKGkXXsqFp//5Bqrc+fOqV27scnlRUkHmtmzAJIGArW/EjrnnEtVqQyKaWxy2Q/4l6QP4u1ewNuSXgPMzPoXJTrnnHOtUmOTy+CiRuGcc65NaezyL+8XO5BSMW/ePCqWL276RMCa6nW7tznnXLnzT0PnnHMF1+j9XMpFjx49+HBV+yYvebLJi7fRvUvhRuQ0qwbVRmtPrfm1WLVqFe+vLNxios61FkVLLpLGEnat/NjM9ohlWwJ3Ar2B2cDJZvapJAGjCRuRLQfOMLMX430qgUvjw15lZuNj+X7AOKAzMBkYYWZW1zWK9Tydc+WrOV98KpYvZJWsza/cUMyayzjgRsJky4yRwGNmdq2kkfH2RcAxQL/4M5Aw+39gTBSjgAGAATMlTYrJ4ibgHOA5QnIZTFhMs65rtCrNqUEVuvZUKlrza7HRRhuxfYcVBV1M1LnWoGjJxcyelNQ7p3gIMCj+PR6YTvjgHwJMMDMDnpXUVdK28dypZrYIQNJUYLCk6cBmWfNuJgAnEJJLXddwrjwVeJ8f/7a+XnO++HR+azKbrF3C+vWA26ak+1y6m9n8+PeHQGZj5x7AnKzz5say+srn5imv7xrOuRTV1BQ2ybnSllqHfuwfsTSvIWk4MBygV69exQylVfJh2W1Egff5ae639Q4rPgGrafjkRmrOIp5Q2ls7tCVJJ5ePJG1rZvNjs9fHsXwesH3WeT1j2TzWN3FlyqfH8p55zq/vGrWY2RhgDMCAAQOKmuicK3eF7n9q1iKevktqYpJOLpOASuDa+Pv+rPLzJE0kdOgvjsnhEeAXkraI5x0FXGxmiyR9LulAQof+MOCGBq5RXAVu1y4FpTIs27m6NHURz41fnkhVVVVBt2Jw+RVzKPIdhFrHVpLmEkZ9XQvcJels4H3WL9k/mTAMuYowFPlMgJhEfg68EM+7MtO5D5zL+qHID7N+2+W6ruHKSRtM9q7lSmUrhnKY/1TM0WJD6zh0eJ5zDfh+HY8zFhibp3wGsEee8oX5rlF0Rdi/3jlXeG11K4ZS4zP0Xdvkyd6VsHKY/+TJpY35YGn+qnZm9Fb3Lmtrnd8vkcicKwHeXJoYTy5tiLXrgDp2ZKMd+tY6tjp2VuYe60forHT184mDzjWNJ5c2ZG2nzei7Y3dGjx5d61hmBEy+Y86VDW8uTYwnF+cawZf5cK5pPLmUsIrli2o1w7RbGb5xre1Uu804TBDz1W6cc+nz5FKi6uoHqapaEo7vmC+JdC+N/hPvNHWu7HlyKVF1zQb2vhPnXGvgycXVK1/THNTfPIcZO2za9Ilq3mnqXNvhycXVqb4mtvqa58IxX1usGFrzls+uvHhycXWqb6G++prnRowYkXftJufyTfKta4Jv5nyf5Ns6eXJxrhVpzVs+d+7cmR55asN1TfAFn+Tbmnlyca6Rmtr/VLF8EXTq4N/Wox49evgE3zLiycW5Rmhe/1N3li1bxsYb+7d1V348uTjXCM3tfyrkfUpJs2pxPsG3ZOVuGZ27cVpzNkvz5OKca5Lm1uLaam2suc2lpaxz584tfgxPLq7N8u0HiqPQtbjWrLmJdt68eVD9WREja5pibOHsycW1Sb79gEtCy4brzy9aXKXAk4trk3z7gbbPJ5SWtlReZUkXSnpd0ixJd0jqJKmPpOckVUm6U1LHeO5G8XZVPN4763EujuVvSzo6q3xwLKuSNDL5Z+icc+Ut8ZqLpB7ABcBuZrZC0l3AqcCxwO/MbKKkPwFnAzfF35+aWV9JpwK/BE6RtFu83+7AdsA0STvFy/wBOBKYC7wgaZKZvdHYGJu1nlZNdWMf3jWDbz/QMm2x/6k1TygtB2k1i7UHOktaA3QB5gOHAafF4+OBywnJZUj8G+Ae4EZJiuUTzWwV8J6kKuCAeF6Vmb0LIGliPLdRycXX0yo9rXr7gSJoaqJlbQ3qsqn3P7lEJZ5czGyepOuADwhb9D0KzAQ+M7PM1/+5QGaJ3B7AnHjfakmLgW6x/Nmsh86+z5yc8oH5YpE0HBgO0KtXL8DX0ypFvv3Aei1JtPlex3J8DUtFW1+5IY1msS0INYk+wGfA3cDgpOMAMLMxwBiAAQMGWEsfr62/WVz6PNG2DXV9SWhLKzek0Sx2BPCemS0AkPR34MtAV0ntY+2lJ5DZlnAesD0wV1J7YHNgYVZ5RvZ96iovmnJ4szSFJ1rn6lYOXxLSSC4fAAdK6kJoFjscmAE8DpwITAQqgfvj+ZPi7Wfi8X+YmUmaBPxN0m8JHfr9gOcBAf0k9SEklVNZ35dTNOXwZmksT7TOuTT6XJ6TdA/wIlANvERomnoImCjpqlh2S7zLLcBtscN+ESFZYGavx5Fmb8TH+b6Z1QBIOg94BKgAxprZ60k9P+eJ1jmX0mgxMxsFjMopfpf1o72yz10JnFTH41wNXJ2nfDLQhJlVzjnnCsmnqjrnnCs4Ty7OOecKztcWawWy91rI3WcBmrfXgnPOFZMnl1amEPssOOdcsXlyaQW8VuKca228z8U551zBeXJxzjlXcJ5cnHPOFZz3uTjnykpb3NumFHlycc6VDWvXAXXs6HvbJMCTi3OubKzttBl9d+xe555M4GvfFYr3uTjnnCs4Ty7OOecKzpvFnHOtVsXyRXR+a8MF0Nut/BwITWD5zod8W0G7QvPk4pxLXCHWy6urk72qakk4vmO+JNLdO+cT4snFOZeq5q6X55vSlTZPLq7R6vu26Sszu6bw90rb58nFNYuvzuycq08qyUVSV+AvwB6AAWcBbwN3Ar2B2cDJZvapJAGjgWOB5cAZZvZifJxK4NL4sFeZ2fhYvh8wDuhM2O54hJlZEs+tLfNvm+t5Lc65+qU1FHk0MMXMdgH2At4ERgKPmVk/4LF4G+AYwiTZfsBw4CYASVsCo4CBwAHAKElbxPvcBJyTdb/BCTwnV6Y6d+7sNTnnciRec5G0OXAIcAaAma0GVksaAgyKp40HpgMXAUOACbHm8aykrpK2jedONbNF8XGnAoMlTQc2M7NnY/kE4ATg4QSenisTXjNxrn5pNIv1ARYAt0raC5gJjAC6m9n8eM6HrB+M3gOYk3X/ubGsvvK5ecrLjjfdOOfSkkazWHtgX+AmM9sHWMb6JjAAYi2l6H0kkoZLmiFpxoIFC4p9uVR5041zLklp1FzmAnPN7Ll4+x5CcvlI0rZmNj82e30cj88Dts+6f89YNo/1zWiZ8umxvGee82sxszHAGIABAwa0uQ5/r5k4V7fsmj2kV7svxITSUpR4zcXMPgTmSNo5Fh0OvAFMAipjWSVwf/x7EjBMwYHA4th89ghwlKQtYkf+UcAj8djnkg6MI82GZT2Wc87lVQq1+1KIoVDSmudyPnC7pI7Au8CZhER3l6SzgfeBk+O5kwnDkKsIQ5HPBDCzRZJ+DrwQz7sy07kPnMv6ocgP4535zrkcpVIbKJU4Ci2V5GJmLwMD8hw6PM+5Bny/jscZC4zNUz6DMIfGOedcCnzJfeeccwXnycU551zBeXJxzjlXcL5wpSsLPqHUuWR5cnFlp60M9XSulHlycWXBaybOJcuTi3OtVFud2e3aBk8urqj8AzAZ3tTnSo0nF5cY/wAsLE/KrpR5cnFF5R+ApcdHzrkkeHJxrox5bdIViyeXBvi3vPwWLlzIFVdcwahRo+jWrVva4bgmKPR71v+PrOevxXo+Q78J2tJy2C01fvx4XnvtNSZMmJB2KK6E+P+R9cr9tfCaSwPK6ZtGYy1cuJApU6ZgZkyZMoVhw4Z57aWM+f+R9fy1WM+Ti2uy8ePHs3btWgBqamqYMGECF154YSLX9qHNzrUO3izmmmzatGlUV1cDUF1dzdSpU1OJo9ybHZwrZV5zcU12xBFHMHnyZKqrq2nfvj1HHnlkYtduzbUSr3W5cuI1F9dklZWVtGsX3joVFRUMGzYs5YhaH691ubbOay6uybp168bgwYN54IEHGDx4sHfmN5LXSlw5Sa3mIqlC0kuSHoy3+0h6TlKVpDsldYzlG8XbVfF476zHuDiWvy3p6KzywbGsStLIpJ9bOaisrGTPPff0WotzLi+ZWToXln4IDAA2M7PjJN0F/N3MJkr6E/CKmd0k6Vygv5l9V9KpwDfM7BRJuwF3AAcA2wHTgJ3iw/8bOBKYC7wADDWzN+qLZ8CAATZjxoyCPLd8bet9+/Zdd9zb1p0rLP8/lx5JM81sQG55KjUXST2BrwF/ibcFHAbcE08ZD5wQ/x4SbxOPHx7PHwJMNLNVZvYeUEVINAcAVWb2rpmtBibGc1PhbevOJcv/z5WGtPpcfg/8FNg03u4GfGZm1fH2XKBH/LsHMAfAzKolLY7n9wCezXrM7PvMySkfWOgnUB//huRcsvz/XOlJvOYi6TjgYzObmfS188QyXNIMSTMWLFiQdjjOOddmpNEs9mXgeEmzCU1WhwGjga6SMjWpnsC8+Pc8YHuAeHxzYGF2ec596iqvxczGmNkAMxuw9dZbt/yZOeecA1JILmZ2sZn1NLPewKnAP8zs28DjwInxtErg/vj3pHibePwfFkYhTAJOjaPJ+gD9gOcJHfj94uizjvEakxJ4as4556JSmudyETBR0lXAS8AtsfwW4DZJVcAiQrLAzF6PI8zeAKqB75tZDYCk84BHgApgrJm9nugzcc65MpfaUORSU8ihyM45Vy5Kaiiyc865ts2Ti3POuYLz5OKcc67gvM8lkrQAeL+FD7MV8EkBwmntMUBpxFEKMUBpxFEKMUBpxFEKMUBpxFGIGHYws1pzOTy5FJCkGfk6tsothlKJoxRiKJU4SiGGUomjFGIolTiKGYM3iznnnCs4Ty7OOecKzpNLYY1JOwBKIwYojThKIQYojThKIQYojThKIQYojTiKFoP3uTjnnCs4r7k455wrOE8uzjnnCs6TSzMp2L7hM4saQ4Wk69KMwdVNUpe0Y0ibpC83psy1PZ5cmiku+z855RhqgIPTjCFD0k6SHpM0K97uL+nStONKg6QvSXoDeCve3kvSH1MOKy03NLKsqCTtIOmI+HdnSZs2dJ8ixHBbY8raCk8uLfOipP1TjuElSZMkfUfSNzM/KcTxZ+BiYA2Amb1K3B4hCZIeTepajfA74GjCpnaY2SvAIUkGEGu1jyd5zZzrHyTpR8DWkn6Y9XM5YSuMJGM5B7gHuDkW9QT+L8kYot2zb0iqAPZLMoAk3xeltJ9LazQQ+Lak94FlgAiVmv4JxtCJ8CF2WFaZAX9PMAaALmb2vKTssuoEr19SW4ma2Zyc16Im4evXSForaXMzW5zktaOOwCaEz5jsWsLnrN8UMCnfBw4AngMws3ckbZPUxSVdDFwCdJb0eaYYWE3Cw5GTfF94cmmZo9MOwMzOTDuG6BNJXyQkNiSdCMxP8Pqb11djM7Mkk+0cSV8CTFIHYATwZoLXz1gKvCZpKuHLDwBmdkGxL2xmTwBPSBpnZu8DSGoHbGJmn9d/74JbZWarM8k+bpee2BwMM7sGuEbSNWZ2cVLXrUci7wtPLi1gZu9LOhjoZ2a3Stqa8G0tMZJ2Am4CupvZHpL6A8eb2VVJxkH4djgG2EXSPOA94NsJXn9z4DjCN8JcSdfkvguMBnoA84BHgXMTvH7G30m+BpvrGknfJdTcXgA2kzTazH6dYAxPSMrUHI4k/Fs8kOD1Mx6UtLGZLZN0OrAvMDqTfBOUyPvCJ1G2gKRRwABgZzPbSdJ2wN1mlthoGElPAD8BbjazfWLZLDPbI6kY4jX7mNl7kjYG2pnZkkxZQtd/0cz2TeJaDZH0ZTN7uqGyhGLpDPQys7eTvna8/stmtrekbxM+TEcCM5NsOo41prOBowhfPh4xsz8ndf2sOF4F9gL6A+OAvwAnm9lXU4il6O8L79BvmW8AxxOrlmb2XzZsX05CFzN7Pqcsyb6OjHsBzGyZmS2JZfckeP18NZa0lMoIqa8DLwNT4u29JU1KOIwOsWnwBGCSma0hwSap6Hwz+7OZnWRmJ5rZnyWNSDgGgOo4ynQIcKOZ/YHkPy8Se194s1jLrDYzk5TpZ9g4hRhS7euQtAthFExun8dmhMEGSTk9wWvlJekg4EvEEVJZhzYj4RFS0eWEjuzpAGb2sqQdE47hZmA28ArwpKQdCJ36SaokNFNmOyNPWbEtiZ37pwOHxBpVh4RjgITeF55cWuYuSTcDXeNwx7MIQ3KTlK+vI8kP2p0JfR1dga9nlS8BzkkwjmczST5HZgTfZgnEUEojpADWmNninFFraxOO4Q9mdn3mhqQPgEOTuLCkocBpQJ+cb+abAouSiCHHKTGes83sQ0m9gCT7njISeV94n0sLxQ7C7LbcqSnFsa6vI6XrH2Rmz6Rx7VIjaYcUOmnzxXEL8Bihn+NbwAVABzP7boIxvEtoHr3VzBIdMRdrSX2AawivQcYS4FUzS6P5OHVJvS88ubRykroDvwC2M7NjJO0GHGRmtyQcRydCp+nuZDWHmdlZScZRCuKowZ9S+7U4rM47FSeOLsDPCF9+AB4BrjKzlQnGsClhMu2ZhD7escDEFIYjp0bSP83sYElL2LC/KcladXY82e8LEd4XPy/0+8KTSwvEPoZfAtsQ/pESf7NIehi4FfiZme0Vx/C/ZGZ7JhVDjONuwnInpwFXEoYhv2lmaXScpiquFnAn8GPCsORKYIGZXZRwHPua2YtJXrM+kr4K/I3QhHoP4QOtKoHrHkgYULEroemyAliW9Id6ufHk0gKSqoCvJ13dz4nhBTPbX9JLWUORXzazvROO4yUz20fSq2bWP44QesrMDkwyjlIgaaaZ7Zd5LWLZC2aW6FJBcZmPLxA+yO80s1lJXj/GUAF8jVBz6Q3cBtwOfAX4hZntlEAMMwi1p7sJUweGATslPaFR0m1m9p2GyhKI4wFqj9hbDMwgTGkoSA3GhyK3zEdpJpZomaRurB8tdiDhjZK0NfH3Z5L2IExqTGyJjRKTeS3mS/qapH2ALZMOwswOJXSeLwBulvSakl9M9B3C0Ntfm9k+ZvZbM/vIzO4hDoVNQqwhVZhZjZndCgxO6tpZctcWa0/Ca4tF7xJm6f85/nxO6IfaiQIOSPKaSwtIGk34Zvh/wKpMeZJLjUjal1Dl3wOYRVhj68S4cGRiJP0PYa5Lf0Iz3SbA/zOzPyUZRymQdBzwFLA94d9mM+AKM0t6jkl2THsS+oFOMbOOCV53EzNbmtT16ojhSeAIwqTFDwlD9c8ws70Suv66tcWA5Zli4tpiKdSgatWis1pAXjez3eu6b5Ou48ml+STdmqfYku7Ejt+Adia8Yd+OE9VcmZO0K2H467cIi5veCdxrZh8ncO0bqGeyZKHXsWoglh2AjwlzSi4k1Kr/mER/T04cJbG2mKQ3gaPN7IN4uxdhpOuu2c3rLb6OJ5fWT2GRxN5kzVsyswkJXft0M/trzqTBdczst0nEUQok/dTMflXXB2uSH6gxnmcICeWuuHpEkteurO+4mY1PKpZSIqkHsAMb/l99MuEYjgX+BPyH8IW0D2G9tenAOWb2+0JcxydRtoCk6/MULwZmmNn9CcVwG/BFwnIOmWXdDUgkuQCZVQkSX8aiBGX632akGkVkZgeleO3Uk4eku8zsZEmvkT/ZJ7k1BpKuJQwseIMN/68mmlzMbLKkfsAusejtrE78giQW8JpLi0gaQ/gHujsWfYswQ74b8K6Z/SCBGN4EdjP/h3RRPR+qie83FOf8XATsRsJzfiRta2bzY7NYLUlPdJX0NtDfzFY1eHLxY9mD2v8mBf1C6jWXlukPfNnCdsNIuonQkXsw8FpCMcwiDCpIcu+UWiSNB0aY2Wfx9hbAb8p0EuVU4KSc12KimSW1/09mbtFxCV2vPrcTmua+RtacnyQubGaZ/xPtgPmZb+cKKwJ3TyKGHO8S+n1STS4Kq7kPIiSXycAxwD8pcGuHJ5eW2YIwKioz9HdjYEsLu70l9QbaCnhD0vNsOGLt+ISun9E/82Ear/9pHIJbjrbO81okNiw7fluvAMbF4chp6mZmt0gaYes3EHsh4RjuJiwomlETy5Leonw58LKkx9jw/2qifXGEde72Iky2PjOu8vHXQl/Ek0vL/IrwZplOaHI4BPhFXOdrWkIxXJ7QdRrSTtIWZvYpgKQtKd/3V42kXlmjcXYg4WXmLf1tjjM2mPMD/Jfk5/y0N7PVmRsWdqVMbDh2lknxJ20rzGytpGpJmxFG0m1f6IuU63/+gojfyCYTlq8GuCRrVM5PEorhiSSu0wi/AZ6Jy8CI8O3o6nRDSs3PgH8qbOQmwmz04SnEkdo2x1mukrQ58CPWz/m5MMHrAyyQdHxmnpGkIcAnCceAmY1Xypu3RTMkdSVMmJxJeJ8UfNFZ79BvBkm7mNlbcQJjLUms51Rqi+HFmHYDDovxPG5mbyQdQ6mQtBWQWfrmWTNL/MOsruHApTCSK0kK+x3dTth2GmAO8B0z+0/CcXwduA7oaGZ9JO0NXJlCE3Z2TL2BzYox6dqTSzNIGmNmw+PaTbksiZEwpUjSXoSmQSOsK/ZKyiGlRtLxhNcCYLqZPZhSHFsDmFkineh1XP8cas/DSnygh6RN4rVTWTFA0kzCl6/pluKW5PG6RZ9v481izWBmw+PvtDtLM30buZYkPUtfYdvYcwhLwAj4a0zCiW/vm7Y4n2F/wrdlgBGSvmRmlyR0fQGjgPMII6UkqRq4wcyuTCKGLPcTRlBOY/3cjkTFZrlRxGQfmyuvTKEvqhQ2b0PSLwkrNxR1vo3XXFpA0knAFDNbEhcE3JewjPhLCcYwm9AZ9ynhQ70rYf2kjwizbWcmFMerhH1klsXbGwPPJD1RrRTE12JvM1sbb1cQRuYk8lrE1RKOAYab2XuxbEfgJsL79XdJxBGvm/gK3XliuJcwZD/THPgdYC8z+2bd9ypKHKlv3hbjSGS+ja+K3DKXxcRyMGFhvFsIyyokaSpwrJltZWbdCB8qDxKWc/hjgnGIDb+Z1sSyctU16+/NE772d4ChmcQCYGbvEra/HpZwLA/G5UbS9EUzG2Vm78afK4CC7xnfCOcTVkZeBdxBWI246BOt88jMtykqbxZrmcyH6dcIq5s+JOmqhGM40MzW7VVvZo9Kus7M/lfSRgnGcSvwnKT7CEllCCHZlqNrgJdin1xmiPrI+u9SUB3yDSAwswUK++wkaQRwSZz3tYZ0BpyskHSwmf0TQNKXgRUJXh8AM1tOGEn4s6SvnSOR+TaeXFpmnqSbgSOBX8YP86Rrg/MlXQRMjLdPAT6KTTGJteea2W/jfJ+DY9GZSTYPlhIzuyO+FplJeheZ2YcJhrC6mccKzsxKYc257wHjY9+LgEXAGUldXNLvzewHyr9JVxoTnhOZb+N9Li2gsBf1YOA1M3tH0rbAnmb2aIIxbEXorMx8qD8NXEFYNaCXJbSseBzuOdfMVkk6FNgTmJA9U71cxG/GL5vZMkmnE/riRie1lpWkGrLmtWQfAjqZWdK1l3Dx8B4ZSmiyK8ieIU28/mYAZvZ5wtfdz8xmKmzzXEsJzVUrKE8uLRT7W/qZ2a1x2OUm2W3d5ULSy4QtZHsDDxG+Ge1uZmm3tycudujvxfqN024BTjazvB8ubZmk7QgrAQ8lfOG4Bvi7mSW19l5mJOOthN0W/0xI9iOT/BIY49iYODs+3q4ANorNZUnG0Y/w75C7cGVB+6G8Q78F4gJwFwGZDYA6UIQ1ehqIYSdJYyQ9KukfmZ8kY4jWmlk18E3gRjP7CbBtCnGUgmoL39qGAH8wsz9QZlsSSMrMA5tOWO7lbMLikVckmViis2Jt5SjCiuXfAa5NOAYII8W6ZN3uTHLLRGW7lTBysJqwDfYEfG2xkvMNYB/gRQAz+6+kpD9E7iaMUPsLKc0jiNZIGkoYjfT1WJZK80sJWKKwte3pwCGS2lF+r8WNhCVFTjOzGQCS0momyYxaPJbQVPu6ciabJKRT9gROM1sam9aT1tnMHpOk2FR7eZzg+f8KeRFPLi2z2sws858mVnuTVm1mN6Vw3VxnEpZUv9rM3pPUB7gt5ZjScgpwGnC2mX2osI3sr1OOKWnbAicBv5H0BeAu0kuwMyU9Sthx8eL4BTDxyYvAMkn7ZpaHkrQfKYxaA1bFLzzvSDoPmEdY3b2gvM+lBST9GOhHGC12DXAW8LckZ6VLupywqul9bDiscFFSMeSJaQtg+2KsV9QaxC8ZKy2sTLwTYUO5h5NeNaFUSOpJSLhDCdtS3JfUagXx+u2AvQkb+H0WV7XomfT7U9L+hFGd/yXUpr4AnJLUROecON4kzMX6OWEe1q/M7NmCXseTS8tIOpLQlivgETObmvD18w0esEJ3zjUijunA8YTa8ExCwnvazH6YZBylIDYxfIWw38/TwAuEWu63Uw2sBMRke2qSy9CkPXovJ5YOwM7x5ttt+QuHJ5cWUFi2ul+8+e8U1ioqGZJeMrN9JP0PodYyStKrZbr8y4tmtq+k8wnt27+S9IqZ7ZV2bOUoZ/TeOEL/ZGKj92JNYU5mrpOkYYTlX94HLk+qlUFSvXNbCj3fxkeLNYOkjSSNA2YDNxOGN86WNFYJbUIk6adZf5+Uc+wXScSQo32c53MyYfmZciZJBwHfJgzLBv+/lqbs0Xs3pjB672bi5FVJhxBGqk0gzEUbk2AcBwE9CQuJXkfYgyn7p6D8Dd88PyN0Tm5vZvvGhfl6EZqELksohlOz/r4459jghGLIdiXwCFBlZi/EhRLfSSGOUjCC8G9yXxyZtCOQb3sGl4zs0XsPpTB6ryKrdnIKYamoe83sMqBvgnF8AbgE2AMYTegr/sTMnijGRE5vFmsGSbOAA3InPynsF/GsJbA/Q6YZKvfvfLfTIml/M0t6v/SSI6kT8HUzuzvtPqYE1QAAEo5JREFUWJKiOjbSy7AENtTLiuULhNF7L5jZU3H03iAzm5DQ9WcRVsmulvQWYbXqJzPHkvi8yBPTRoQBFr8GrjCzGwt9DR+K3Dxr882qjePWk8rWVsff+W4nRmE3yqHx5zPCrP2yE2dfH014HY4iNEWUTXKh/mYWI2yalYjY1/HbrKIdgIGEpqkk3AE8IekTwtDjpwAk9SU0jSUmJpWvEd6XvYHrCSNNC86TS/NYHG6bbyJWUuPn95L0eYyhc/ybeLtT3XcrPIWtUjMJZQ3hP+8AM5udZBylIK4fdRphwt7zwJeBPkkv8ZE2K4GN9LJJ2ofw73IS8B5hU7tEmNnVCisQbws8auubi9oRluFPhKQJhCaxyYTayqyiXs+bxZpOYYOuteRPLokPA06TpGeAzQjj9yfGBTzfM7M+KYeWOElzgQ8IS2v8n4W9fsrytcgmaQ9qr2NV9FpDHPac+dLzCXAn8GMz26HY1y5FktayfkHT7A/+omyD4DWXZjCz3mnHUEI+AnoA3YGtCZ345fqN5R7gBEKnbY2k+ynf1wJYt/7eIEJymUzYzO6fJNMk9RahCeq4zOrgki5M4LolycwSHcDlo8Vci5jZCYTVbmcS1ih6D9hC0gHpRpY8M/sBYYmR3xA+UN8GtpZ0chzsUY5OBA4HPjSzMwnzTZLamfObwHzgcUl/lnQ45b07aqK8WcwVlKRtCHNdhhL2k9k+5ZBSE2djZzr1jzazrVIOKXGSnjezA+KqBYcSlr1/08x2STCGjQlzXIYSBhJMIAwTT3TJ/XLjyaUNkLQDYU+ZaZI6A+3NbEkpxJXGEhulSFJnM0tjkcJUSfojYW7FqcCPgKWEpVjOTCmeLQid+qeY2eEJX/tA4AZgV6AjUAEsK3RfR6nw5NIC8c3yeuaDXGGnu13N7LkEYzgHGA5saWZfVNgI6E9J/8dxriFxVOFmZbyg6QxCkr2bMER/GLCTmeVOgm4TvM+lZW4ifBPLWBrLkvR9wnDXzwHM7B1gm4RjcG4DknaJv/fN/BA2DWvf0ATLtiwOLKgwsxozu5V0VtNIhI8WaxlljVnHzNZKSvo1XWVmqxX3PorX9+poiYhLjWxiCe/bXgJ+SKhR55tMmegkyhKyPK49+LKkXxEGG7TZL/ht9okl5F1JF0jqEH9GAO8mHMMTki4hTKQ8klDlfiDhGDLbLT8Wl7pAUn9JlyYdRymQ9DdJm8WO5FnAG9L/b+/eg+WuyzuOvz8JAcqdQirljrSVBuUeJOFSaFWGgsAIFUi9FSogIlMcBFr+EBovTKEWBRRCkAYrKIq0GoUiQnM1BAmQEGnoiKXgFOVaohQawqd/fL/L2Rz2hMzZ3e/3l7PPa+bMOfvbnP09yWz2+f2+l+fRp2rHVZLt0/P3Izp8DWJigdReeRxwNmm/yU6kFW1jUiSX7pwJTCV1cnuSVFLi9MIxXAA8DSwDziDtJajxoX4dqVjjKoA8rn7yWn9j7JqU71SOB24nLU/+YN2Q6pD08dyaovV4a0lnFY5hpaQXh309Iem2XFS0lONtv2z7RduX5F5HxxQ8f1Exob8ey/Wrlpdc1rmWWO6zPXlYQc0Hc8XogSJpOanz4U2kEu9zNKD9XDq9B0oXVpU0nXTxdxNpn8vJwO7AEuBjtg8vFMcS2/sNO9aIIrP9EHMuoyDp/NwA6ko6zG/YPqdEHE5tdFdI2tn2f5U451o8I2l38r+HpBNJY8qD6FpSr5+HgLl5qfigzbm0jJf0+txkviAq0vOozbHDEvuMnPQuyEPKfSXpFFJds920ZsOuzYFq7cj7LZLL6DySv/+kahTJ1sBySYsZqhvU865y6+DjpMZHe0j6Bak44AcKx9AItr9Eqjbb8rikRhVyLOgO4JuSrs2Pz8jHSnpJ0vtJ5XkgVQ14Of9cYuhmIelCa1vWXOCwEhizy7JjWGw9l6vwvkE/mv+sizyJPa4JmzhrkfQW4HPA9raPym0Ipti+vnJoxeXVcmeQSsAA/BCYaXt1wRjeSmqONYWUTBYB55LmSve3Pb9ULIMkkksXctXV80h9EV6/CxzE1TB5pdwNpKux64D9gAsHscSGpNtJ/xYX2d47Lw9/wPY7KocWKhq0HfoxLNadbwHXADOBYldi7Rr0hj3V9hclHQlsQ1od9TVg4JILsK3tW5Ra65I7EFZ5f9Qm6WDgYlKPnw0YKu9ebJWWpInAR3njReCppWLIrqLDDv3CMRQTyaU7r9ouvSN/uKa8YVvVZv8UuNGpd/ygVqD9jaRtGFrccBCFOw42yPWkIaj7qXQBBvwLqfT+XRVjANIOfUnj87DgDZIeIC3hH3MiuYyCpN/OP34vr9m/DXil9bztoitAGvKGvV/SnaQ9HX8taXPKdeVsmk8C3wV2l7SA1OfmxLohVfM/tm+vHMMmti+oHAMM2A79mHMZBaWeJaYBnSglzQXeRRqae4r0hv1I6T0VeeJ2H+Ax2y/kK/cdBrhI4QbA20jvkRW2V1UOqQpJl5KGar/DmhdgSwrG8Blgoe0flDrnCHHsQmqutyHpbm5L4MutRmZjTSSX9VxT3rCSDut03PbcknHUJGmtpTxsf6dULE0h6Z4Oh11y0YuklcCmpOS2ij619V3HWCaSTv506XOXFsmlC5I2Bs4CDiHdycwjlbt/ea2/2NsY/oR0VVa1V4ik9npmGwMHAvcP0so5STes5WlXmEAODZDnHj9Nqik2jpTcXgWutP23NWPrp0guXZB0C2np7T/lQ9OArWz/WcEYZpHW7z9HSm5zgfm2ny8Vwwhx7QRcYfuEmnGE+iQdDexJuugAoMSHqqQ9bP/7SCX+Sw3NSfokcBRwuu2f52NvJbXnuMP2P5SIo7RILl2Q9FPbk97sWKFYtidNGp9H2rxXdbFGvlpbXuPfoglqfaA2jaRrgE1ILY5nkt6ji22fVuDcM2yfXntoLi+webftZ4YdnwjcGbXFQidLJB1kexGApHdSuCSMpA8AhwLvAJ4hLU2eVzKGHEd7nbXW5H6xSdsmGekDtWpQ9Uy1vZekpbYvkfT3pErRfdcq+w8cNXyoOg9plzJheGKBNO8iaULBOIqK5NKd/YGFklpFI3cGVkhaRroy2qtADFcAPyNt5rzH9n8WOGcn7Un1VeBm2wsqxVJbtQ/UBmrNBb6U766fBX63cAwLSRUj3uxYv/zfKJ9br0Vy6U71FqW2t5W0J3AY8FlJv09a+lq0f4jtWXkNf2sD54qS52+YJnygNsVspX4ul5HuZE26m+s7SdsBO5Aa6e3L0NaBLUh3lqXsLalTVWzRNmw61kRy6YLtxwEk/Q5rjq0XK38vaQvSHdMupPIWW1KhzbGkw4FZpFLzAnaS9OFBWorcptMH6nV1Q6rD9vT8462SZgMb2y5VreBI4CPAjsAX2o6/CPS91H6L7fGlztUkMaHfBUnHkkpobw/8ivQB/4jtPQvGsBSYn7/m2n6y1LmHxXE/MM32ivz4D0hDY/vXiKcpJG1E2Q/UxpE0lTfW9bqx4PlPsH1rqfOFZMyWHihkOnAQ8Kjt3UhlxRcVjuEzts+yfVMrsUgqthS6zYRWYgGw/SgwZicrO5E0OQ/FtB5/CLgFmN5WMmigSPoacDlpL9jk/HVA4TAWSLo+V6tG0iRJfV+tNujizqULkn5i+wBJDwH72n5NhdvZqnPr1DccKxDHV0m1xFp7fv4cGD9IGwclLQHeZfu5XLHgG8AnSCvn/tD2wNUXk/QIMMkVP2iiBUIdMefSnRckbUbauPh1Sb+irRtkP0k6ilSBeAdJ7V0PtyCt1irtY6RulK0Wz/OAL1eIo6bxbUVLTwJm5OGYWyU9WDGumh4GtqNuy+togVBBJJfuHEdaGXQu6Up9S6DURrnnSMt/jyWVM29ZmeMpyvYrpEnTL7zZnx3DxkvawParpCHS09ueG9T/a9sCP1Vqw91euLJkG+5ogVDBoL7huyZpPDDb9hGk4aBZhUP4iu39JB1pu/S536BDUygASlaIboCbgTmSniFddMwDkPR7DO6H2cW1AyBaIFQRyWWUbK+W9JqkLSutBNpQ0jTgnZ2q8VaowNuEplBV2f6spB+R9rTc2TbPMI409zJwbM+pHQPwPPBHtLVAIM2DhT6K5NKdXwPLJP2QtrkW2+eM/Cs9cyZpKG4r4L3DnjOpf0ZJTWgKVV2rFNCwY4/WiKUmSfNtH5LL3bdP5tcod/9t4Fjby3NshwFXk0omhT6J1WJdkPThTsdLDlNJOs329aXO1+H8rVVp76dyU6jQHJJ2aW0yrk3SZNLikveSSr58HjjG9hNVAxvjIrms53LJlTNJ5V8A5pB6yhTpfDhCxdmWok2hQnO0L4eXdGvt1guSpgDXAi8DRw9Cs67aIrmMgqTjgB1tX50f30uaJAQ43/a3C8Yyk7RZsXW39EFgte2/LBVDCMNJeqBVSr7958IxfI81h+QmkZZEPw/FV6wNnJhzGZ3zgZPbHm9E2nm8KWmzVrHkAkwetmnz7rypsyhJbwE+R+olc5SkScCUmkN2oSqP8HNJl1c6byCSy2htOGy8dr7tZ4FnJW1aOJbVkna3/TN4vcNdjdVa/0jeBZ0fPwp8k7SKLAyeViVgkaoSt6oCF5vQtz0nbxm4K28ZCAVFchmdrdsf2D677eFEyvoUcI+kx0j/cXcB/qJwDBC7oEObplQCbsCWgYEVyWV07pX0UdtrlFGXdAaFOw7a/lHu4fK2fGhF3i1fWuyCDk1Vc8vAwIoJ/VHI/Vv+mbTktrXUdn/S3Mvxtn9ZIIbJwBO2n8qPPwScADwOXNxW46qIvCT5SuDtpHpSE4ETbS8tGUcIwzVhy8AgiuTSBUl/DLR6tyy3fXfBczemAm8e1z6HlFxe3wVdajl0CKF5Irmsp9pL+0u6Gnja9sX58YO2i5a3kLTY9oElzxnCusjDxp8nLUVu7xg7SHXviotmYeuv8bkvBaQKvO13TTXm0hZIukrSoZL2a31ViCOE4W4AvkJqRXEEcCNDfYdCn8Sdy3pK0kWkfi7PADsD+9l2rsA7y/bBhePptFM/duiH6iTdb3t/SctaDcJax2rHNpbFarH1VNMq8MY+gtBgr0gaB/yHpLOBXwCbVY5pzIs7l9Azko4mLXBoH9cu1TwthI7yyspHSBXEp5Oa+v1dpwrWoXciuYSekHQNsAlpTHsmqRnTYtunVQ0shFBFJJfQE5KW2t6r7ftmwO22D60dWxhMkr67tuejcGV/xZxL6JX/zd9fkrQ98CxpPiiEWqYAT5DaT99L2n8VConkEnpltqStgMtIVQsMXLf2Xwmhr7YD3g2cAkwDvg/c3OpIGforhsVCVyT9FbAQWGL71XxsI2DjKBQYmiK/J08hXfxcYvuqyiGNeXHnErq1I3AFsIekZcACUrJZWDWqEHg9qRxNSiy7Al8CbqsZ06CIO5fQE7nd8gHAVNJY9xTgBduTqgYWBpakG0mFVH8AfMP2w5VDGiiRXEJPSNqSlFAOzt+3ApbZrtFbJgQkvcZQif32D7piDcsGWSSX0BVJM0gbJ1eSVuQsAhbZfr5qYCGEqqJwZejWzqQ+Nk+Rymo8CbxQNaIQQnVx5xK6Jkmku5ep+evtwHPAj21/umZsIYQ6IrmEnpG0I2nOZSpwDLCN7a3qRhVCqCGSS+iKpHMYumNZxdAy5IWkCf3XKoYXQqgk9rmEbu0KfAs41/Z/V44lhNAQcecSQgih52K1WAghhJ6L5BJCCKHnIrmEUICkEWutSTpc0uwRnvubTq8jaVdJ09blNUKoIZJLCAXYnjrKX10jubS9zq6kMvIhNFIklxAKkPRrJZdJeljSMkkntf2RLSR9X9IKSddIGifpUuC3JD0o6eut18l//lLg0PzcucPOtamkr0paLOkBSceV+VuGMCSWIodQzvuAfYC9gW2B+yTNzc8dCEwCHgfuAN5n+0JJZ9vep8NrXQicZ/sYSMNibc9dBNxt+9TcwG2xpLts/6bD64TQF3HnEkI5h5A6Ia62/UtgDjA5P7fY9mO2V5Pa8h7SxXneA1wo6UHg34CNSTXgQigm7lxCaIbhG8662YAm4ATbK7p4jRC6EncuIZQzDzhJ0nhJE4HDgMX5uQMl7SZpHHASMD8fXyVpQofXWglsPsJ5/hX4RC4oiqR9e/Y3CGEdRXIJoQyT2usuBR4C7gbOt/1Ufv4+4CrgEeDnDLXinQEsbU3ot1kKrJb00PAJfWA6MCH/3vL8OISiovxLCH0maRtgie1dascSQilx5xJCH0naHvgxcHntWEIoKe5cQggh9FzcuYQQQui5SC4hhBB6LpJLCCGEnovkEkIIoeciuYQQQui5SC4hhBB67v8BqguvZigZF8gAAAAASUVORK5CYII=\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{"needs_background":"light"}}],"execution_count":17},{"cell_type":"markdown","source":"## Interpretar la salida de un modelo lineal (10 min)\nEl modelo lineal de salario versus edad se puede ajustar de la siguiente manera:","metadata":{"id":"ST46g8pVoHjA","cell_id":"08ffa0899a7b4135b331155a4c5f5370","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"Data.columns","metadata":{"id":"PPYs94lmaz4u","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"0a04be280c56453b9b6f79ef49f4bdc7","outputId":"5e951a14-cb5e-439c-93e7-2170ef638257","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":416,"user_tz":300,"timestamp":1640984072007},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"Index(['jobtitle', 'gender', 'age', 'performance', 'education', 'department',\n       'seniority', 'income', 'bonus', 'pay'],\n      dtype='object')"},"metadata":{},"execution_count":18}],"execution_count":18},{"cell_type":"code","source":"model1 = 'pay~age'\nlm1   = sm.ols(formula = model1, data = Data).fit()\nprint(lm1.summary())","metadata":{"id":"6WA5qm3LoHjA","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"a7c0b0fbd1f64eda9e9da01fb9e66937","outputId":"26e073a2-7d70-4fab-bb53-52a7512e21d3","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":12,"user_tz":300,"timestamp":1640984075881},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                    pay   R-squared:                       0.285\nModel:                            OLS   Adj. R-squared:                  0.284\nMethod:                 Least Squares   F-statistic:                     397.5\nDate:                Fri, 31 Dec 2021   Prob (F-statistic):           1.04e-74\nTime:                        20:54:35   Log-Likelihood:                -11384.\nNo. Observations:                1000   AIC:                         2.277e+04\nDf Residuals:                     998   BIC:                         2.278e+04\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept   6.206e+04   2062.885     30.085      0.000     5.8e+04    6.61e+04\nage          939.2501     47.109     19.938      0.000     846.806    1031.694\n==============================================================================\nOmnibus:                        6.360   Durbin-Watson:                   1.905\nProb(Omnibus):                  0.042   Jarque-Bera (JB):                6.421\nSkew:                           0.182   Prob(JB):                       0.0403\nKurtosis:                       2.853   Cond. No.                         134.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"}],"execution_count":19},{"cell_type":"markdown","source":"## Mirando age vs gender: Variables Categoricas (10 min)","metadata":{"id":"Sagouz94oHjF","cell_id":"34eb8e028da946e79258973561b7b341","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"Ahora que hemos visto que la edad explica parte de la relación con el salario, consideremos un modelo en el que tengamos en cuenta la edad y el género simultáneamente. La edad es una variable numérica (p. Ej., 26,5, 32). Por el contrario, el género solo toma dos valores: masculino y femenino. Estas variables se denominan <b> variables categóricas </b>. La forma en que interpretamos los coeficientes de las variables factoriales en el modelo lineal es ligeramente diferente de los de las variables numéricas:","metadata":{"id":"LRW4iPcUoHjG","cell_id":"84b01f360769476689c560c354bd13e3","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"Data","metadata":{"id":"rAGO8_TrrWVp","colab":{"height":414,"base_uri":"https://localhost:8080/"},"cell_id":"7767c931d4a740fdb0a2fc2e76ccd5ab","outputId":"643b6706-0ff2-4512-fd0a-2f9253df0a86","executionInfo":{"user":{"userId":"09471607480253994520","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjvGjd5VpSUEHTxlxXRYAinh8eCspL5nxvcW9wD=s64","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":292,"user_tz":300,"timestamp":1633375681949},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>jobtitle</th>\n      <th>gender</th>\n      <th>age</th>\n      <th>performance</th>\n      <th>education</th>\n      <th>department</th>\n      <th>seniority</th>\n      <th>income</th>\n      <th>bonus</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Graphic Designer</td>\n      <td>Female</td>\n      <td>18</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Operations</td>\n      <td>2</td>\n      <td>42363</td>\n      <td>9938</td>\n      <td>52301</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>5</td>\n      <td>College</td>\n      <td>Management</td>\n      <td>5</td>\n      <td>108476</td>\n      <td>11128</td>\n      <td>119604</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Warehouse Associate</td>\n      <td>Female</td>\n      <td>19</td>\n      <td>4</td>\n      <td>PhD</td>\n      <td>Administration</td>\n      <td>5</td>\n      <td>90208</td>\n      <td>9268</td>\n      <td>99476</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Software Engineer</td>\n      <td>Male</td>\n      <td>20</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Sales</td>\n      <td>4</td>\n      <td>108080</td>\n      <td>10154</td>\n      <td>118234</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Graphic Designer</td>\n      <td>Male</td>\n      <td>26</td>\n      <td>5</td>\n      <td>Masters</td>\n      <td>Engineering</td>\n      <td>5</td>\n      <td>99464</td>\n      <td>9319</td>\n      <td>108783</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>Marketing Associate</td>\n      <td>Female</td>\n      <td>61</td>\n      <td>1</td>\n      <td>High School</td>\n      <td>Administration</td>\n      <td>1</td>\n      <td>62644</td>\n      <td>3270</td>\n      <td>65914</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>Data Scientist</td>\n      <td>Male</td>\n      <td>57</td>\n      <td>1</td>\n      <td>Masters</td>\n      <td>Sales</td>\n      <td>2</td>\n      <td>108977</td>\n      <td>3567</td>\n      <td>112544</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>Financial Analyst</td>\n      <td>Male</td>\n      <td>48</td>\n      <td>1</td>\n      <td>High School</td>\n      <td>Operations</td>\n      <td>1</td>\n      <td>92347</td>\n      <td>2724</td>\n      <td>95071</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>Financial Analyst</td>\n      <td>Male</td>\n      <td>65</td>\n      <td>2</td>\n      <td>High School</td>\n      <td>Administration</td>\n      <td>1</td>\n      <td>97376</td>\n      <td>2225</td>\n      <td>99601</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>Financial Analyst</td>\n      <td>Male</td>\n      <td>60</td>\n      <td>1</td>\n      <td>PhD</td>\n      <td>Sales</td>\n      <td>2</td>\n      <td>123108</td>\n      <td>2244</td>\n      <td>125352</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 10 columns</p>\n</div>","text/plain":"                jobtitle  gender  age  ...  income  bonus     pay\n0       Graphic Designer  Female   18  ...   42363   9938   52301\n1      Software Engineer    Male   21  ...  108476  11128  119604\n2    Warehouse Associate  Female   19  ...   90208   9268   99476\n3      Software Engineer    Male   20  ...  108080  10154  118234\n4       Graphic Designer    Male   26  ...   99464   9319  108783\n..                   ...     ...  ...  ...     ...    ...     ...\n995  Marketing Associate  Female   61  ...   62644   3270   65914\n996       Data Scientist    Male   57  ...  108977   3567  112544\n997    Financial Analyst    Male   48  ...   92347   2724   95071\n998    Financial Analyst    Male   65  ...   97376   2225   99601\n999    Financial Analyst    Male   60  ...  123108   2244  125352\n\n[1000 rows x 10 columns]"},"metadata":{},"execution_count":18}],"execution_count":null},{"cell_type":"code","source":"model2 = 'pay~age + gender'\nlm2    = sm.ols(formula = model2, data = Data).fit()\nprint(lm2.summary())","metadata":{"id":"VBcadm1HoHjG","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"9ab5de7ca85940b39fb65e5b70ffaac3","outputId":"85c9b080-02c2-4a86-d2c3-3856f0e42a0e","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":559,"user_tz":300,"timestamp":1640984537508},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                    pay   R-squared:                       0.319\nModel:                            OLS   Adj. R-squared:                  0.317\nMethod:                 Least Squares   F-statistic:                     233.2\nDate:                Fri, 31 Dec 2021   Prob (F-statistic):           8.10e-84\nTime:                        21:02:16   Log-Likelihood:                -11359.\nNo. Observations:                1000   AIC:                         2.272e+04\nDf Residuals:                     997   BIC:                         2.274e+04\nDf Model:                           2                                         \nCovariance Type:            nonrobust                                         \n==================================================================================\n                     coef    std err          t      P>|t|      [0.025      0.975]\n----------------------------------------------------------------------------------\nIntercept       5.674e+04   2151.480     26.373      0.000    5.25e+04     6.1e+04\ngender[T.Male]  9279.3180   1317.787      7.042      0.000    6693.364    1.19e+04\nage              948.5266     46.022     20.610      0.000     858.216    1038.837\n==============================================================================\nOmnibus:                        9.898   Durbin-Watson:                   1.871\nProb(Omnibus):                  0.007   Jarque-Bera (JB):                9.345\nSkew:                           0.197   Prob(JB):                      0.00935\nKurtosis:                       2.737   Cond. No.                         148.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"}],"execution_count":20},{"cell_type":"markdown","source":"La interpretación del coeficiente de edad es la misma que antes: si la edad aumenta en un año, se espera que el salario aumente en 948,5 USD. Ahora, concéntrate en el coeficiente de género. Solo muestra masculino (T.male), porque la categoría femenina se toma como la categoría predeterminada. (Tenga en cuenta que la elección de la categoría predeterminada no importa; fácilmente podríamos haber elegido hacer masculino como categoría predeterminada y, por lo tanto, el coeficiente de género sería T.female). El coeficiente 9279.3180 se interpreta de la siguiente manera: para empleados de la misma edad, en promedio, los hombres ganan 9279,3180 USD más que las mujeres.","metadata":{"id":"X14wVM7uoHjG","cell_id":"e3946acb85e64f8c892362d72863139a","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"## Modelo integrado teniendo en cuenta todas las variables (10 min)","metadata":{"id":"dv2gKHcmoHjI","cell_id":"0590fe34b6ed4d39a7817c8068ad1c44","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"Consideremos todos los demás factores que podrían explicar las brechas salariales a la vez. ¿Qué puedes concluir ?:","metadata":{"id":"OWsNPzOAoHjJ","cell_id":"f28170fba9ed418099cee69b77477c09","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"model4 = 'pay~jobtitle + age+ performance + education+department + seniority + gender'\nlm4    = sm.ols(formula = model4, data = Data).fit()\nprint(lm4.summary())","metadata":{"id":"GKJAK6TvoHjJ","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"253b1e8d64e441158fb598469beb72b5","outputId":"c62d1965-4104-4424-fa51-ab27ce58bf0d","executionInfo":{"user":{"userId":"04741209928239412574","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi4e7mWJaOA2l-1KUn-omyigRGSrm83lG6XLzS5=s64","displayName":"david francisco bustos usta"},"status":"ok","elapsed":371,"user_tz":300,"timestamp":1640985111389},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                    pay   R-squared:                       0.841\nModel:                            OLS   Adj. R-squared:                  0.838\nMethod:                 Least Squares   F-statistic:                     259.6\nDate:                Fri, 31 Dec 2021   Prob (F-statistic):               0.00\nTime:                        21:11:50   Log-Likelihood:                -10631.\nNo. Observations:                1000   AIC:                         2.130e+04\nDf Residuals:                     979   BIC:                         2.141e+04\nDf Model:                          20                                         \nCovariance Type:            nonrobust                                         \n===================================================================================================\n                                      coef    std err          t      P>|t|      [0.025      0.975]\n---------------------------------------------------------------------------------------------------\nIntercept                        2.203e+04   1933.534     11.392      0.000    1.82e+04    2.58e+04\njobtitle[T.Driver]              -3928.9812   1447.166     -2.715      0.007   -6768.886   -1089.076\njobtitle[T.Financial Analyst]    3417.7090   1388.276      2.462      0.014     693.370    6142.048\njobtitle[T.Graphic Designer]    -2457.6992   1420.886     -1.730      0.084   -5246.031     330.633\njobtitle[T.IT]                  -2149.7022   1427.414     -1.506      0.132   -4950.846     651.442\njobtitle[T.Manager]               3.16e+04   1471.445     21.476      0.000    2.87e+04    3.45e+04\njobtitle[T.Marketing Associate] -1.701e+04   1385.795    -12.277      0.000   -1.97e+04   -1.43e+04\njobtitle[T.Sales Associate]       263.4456   1435.261      0.184      0.854   -2553.096    3079.988\njobtitle[T.Software Engineer]    1.339e+04   1413.182      9.473      0.000    1.06e+04    1.62e+04\njobtitle[T.Warehouse Associate]  -564.0171   1452.967     -0.388      0.698   -3415.305    2287.271\neducation[T.High School]        -1435.1693    908.773     -1.579      0.115   -3218.536     348.198\neducation[T.Masters]             4717.9971    914.421      5.160      0.000    2923.546    6512.448\neducation[T.PhD]                 6026.2867    929.705      6.482      0.000    4201.842    7850.731\ndepartment[T.Engineering]        3267.9358   1036.509      3.153      0.002    1233.901    5301.970\ndepartment[T.Management]         2957.8290   1033.031      2.863      0.004     930.619    4985.039\ndepartment[T.Operations]         -481.4968   1014.616     -0.475      0.635   -2472.570    1509.577\ndepartment[T.Sales]              6193.4962   1020.679      6.068      0.000    4190.526    8196.466\ngender[T.Male]                    392.3244    715.798      0.548      0.584   -1012.351    1797.000\nage                               948.9464     22.527     42.126      0.000     904.740     993.152\nperformance                      1156.8797    228.188      5.070      0.000     709.085    1604.674\nseniority                        9903.7103    231.490     42.782      0.000    9449.436    1.04e+04\n==============================================================================\nOmnibus:                        3.500   Durbin-Watson:                   1.982\nProb(Omnibus):                  0.174   Jarque-Bera (JB):                3.555\nSkew:                          -0.130   Prob(JB):                        0.169\nKurtosis:                       2.866   Cond. No.                         453.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"}],"execution_count":21},{"cell_type":"markdown","source":"\n## Conclusiones (5 min)\n\nUsamos las técnicas de regresión lineal para determinar si existía o no discriminación salarial basada en el género dentro de su organización. Modelamos el efecto de varias variables independientes (en este caso, antigüedad, edad, desempeño y cargo) para explicar la variación observada en una variable dependiente (en este caso, el salario). Observamos la estadística de $ R $ al cuadrado de nuestros modelos lineales para ayudarnos a medir qué porcentaje de la variación observada en el pago se explica por las variables independientes.\n\nVimos que la diferencia en el salario promedio entre hombres y mujeres es de unos 8500 USD en estos datos. Sin embargo, esta diferencia se convirtió en 400 USD y es estadísticamente indistinguible (valor $ p $ = 0.584) de cero después de controlar los otros factores correlacionados con el salario. Sin embargo, una exploración más profunda de los datos sugirió que las mujeres están desproporcionadamente sobrerrepresentadas en los trabajos peor pagados, mientras que los hombres están desproporcionadamente sobrerrepresentados en los trabajos mejor pagados.\n\nPor lo tanto, se justifica una investigación sobre las prácticas de contratación, promoción y colocación laboral de hombres y mujeres. En su informe al departamento de recursos humanos, debe pedirles que analicen las siguientes preguntas:\n1. ¿Las mujeres eligen o se ven obligadas a aceptar trabajos peor pagados?\n2. ¿Se discrimina a las mujeres en los procesos de contratación para trabajos mejor remunerados?","metadata":{"id":"b0bdj7SjoHjL","cell_id":"05c07c8e217a4940bd53d03823df0c77","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"## Para llevar (7 min)\nEn este caso, aprendió cómo aprovechar sus habilidades en el análisis de datos exploratorios para construir un modelo lineal efectivo que tuvo en cuenta varios factores relacionados con el resultado de interés (pago). Fundamentalmente, aprendimos que:\n\n1. No es suficiente mirar directamente la relación entre el resultado de interés y la variable independiente de interés; puede haber varios factores de confusión.\n2. Llevar a cabo la EDA antes de construir cualquier modelo es importante para descubrir y tener en cuenta estos factores de confusión que podrían estar impulsando las diferencias en el resultado de interés.\n3. $R$ al cuadrado es una cantidad importante que explica qué tan bien su modelo explicó la variación observada. Se puede utilizar para comparar diferentes modelos.\n4. Analizar los coeficientes de una regresión lineal para comprender cómo los diversos parámetros impactan en el resultado final es extremadamente importante; esta interpretabilidad es una parte clave de la traducción de datos en acciones comerciales.\n\nEn estos días, los medios destacan constantemente los algoritmos de aprendizaje automático más avanzados, como las redes neuronales. Es importante que reconozca el inmenso valor de la regresión lineal, en particular por sus capacidades de inferencia e interpretabilidad. Si bien las redes neuronales pueden superar la regresión lineal en ciertas tareas, es mucho más una caja negra y comprender cómo los datos hacen que el modelo reaccione es extremadamente importante en la mayoría de los escenarios comerciales.","metadata":{"id":"EPoo_HkgoHjM","cell_id":"96fb5ae84c274f76bd2e6addd8a2e5fc","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=4f9037d8-f2e9-4683-9ce3-67576f784ec7' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Clase_12.ipynb","provenance":[],"authorship_tag":"ABX9TyPXYwio5TgbYpY58rZchz6y","collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"deepnote_notebook_id":"6417bfc68f134725b72eeace81f32c5b","deepnote_execution_queue":[]}}