{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "5653e15184c548fda5d8f52e2e0d26ed": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ea1001530e35410986821729e2e888d5",
              "IPY_MODEL_76f600f1b26645e7994e01bb401b56af",
              "IPY_MODEL_2d9fd23b2e384dea98ac6c20c33c9607",
              "IPY_MODEL_2084bc5411c8461fb1f0951bd54d3b4c",
              "IPY_MODEL_0f3a79e4512449a8ae192321dec07c9e",
              "IPY_MODEL_b2797f9b7c954d02a8c1b7f075d59637",
              "IPY_MODEL_210595db7a8647c59869919327b37979"
            ],
            "layout": "IPY_MODEL_6665092e88dd40398416cae33335c55f"
          }
        },
        "ea1001530e35410986821729e2e888d5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": true,
            "description": "Niveles A",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_3bc584ec20ad411295503531916ee646",
            "max": 5,
            "min": 2,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_c3a9f09f03cd433d8553bd908526fa31",
            "value": 2
          }
        },
        "76f600f1b26645e7994e01bb401b56af": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": true,
            "description": "Niveles B",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_d0b8208304a443e8b2240b67e51d4295",
            "max": 5,
            "min": 2,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_a710758960104f4a903bc003d048d881",
            "value": 2
          }
        },
        "2d9fd23b2e384dea98ac6c20c33c9607": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": true,
            "description": "N° Muestra",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_c80c6aee4e7c4f52af80337aa9acdc31",
            "max": 20,
            "min": 3,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_588a82283d054b7f9ddcdd847ad23233",
            "value": 5
          }
        },
        "2084bc5411c8461fb1f0951bd54d3b4c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "Media mínima",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_2af770a32e674740b16071f63710f1fe",
            "max": 100,
            "min": 0,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_e40854aede5745bbb8e06598b24d5894",
            "value": 50
          }
        },
        "0f3a79e4512449a8ae192321dec07c9e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "Media máxima",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_2852c60a8993488ebd8d9bbdfd86e709",
            "max": 200,
            "min": 50,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_0c343d9c3f594b61866ba13dba97db41",
            "value": 100
          }
        },
        "b2797f9b7c954d02a8c1b7f075d59637": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "FloatSliderView",
            "continuous_update": true,
            "description": "Desviación",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_e4a73338648d49d2bd19302b1938ed31",
            "max": 50,
            "min": 1,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_dd4e560e841a47c392c0af0bbd44b324",
            "value": 10
          }
        },
        "210595db7a8647c59869919327b37979": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_b74b4ea0ee4d41c9825ee0b6d23ef3f1",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "=== Medias por combinación de factores ===\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "  Factor_A Factor_B      Valor\n0       A1       B1  49.927278\n1       A1       B2  70.432812\n2       A2       B1  87.876679\n3       A2       B2  97.690989",
                  "text/html": "\n  <div id=\"df-cb80321b-3ef7-461a-90e5-a98c49086e0f\" 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>Factor_A</th>\n      <th>Factor_B</th>\n      <th>Valor</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A1</td>\n      <td>B1</td>\n      <td>49.927278</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A1</td>\n      <td>B2</td>\n      <td>70.432812</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A2</td>\n      <td>B1</td>\n      <td>87.876679</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A2</td>\n      <td>B2</td>\n      <td>97.690989</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n    <div class=\"colab-df-buttons\">\n\n  <div class=\"colab-df-container\">\n    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb80321b-3ef7-461a-90e5-a98c49086e0f')\"\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 -960 960 960\">\n    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n  </svg>\n    </button>\n\n  <style>\n    .colab-df-container {\n      display:flex;\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    .colab-df-buttons div {\n      margin-bottom: 4px;\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-cb80321b-3ef7-461a-90e5-a98c49086e0f 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-cb80321b-3ef7-461a-90e5-a98c49086e0f');\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\n\n    </div>\n  </div>\n",
                  "application/vnd.google.colaboratory.intrinsic+json": {
                    "type": "dataframe",
                    "summary": "{\n  \"name\": \")\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Factor_A\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"A2\",\n          \"A1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Factor_B\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B2\",\n          \"B1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Valor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.987344156335183,\n        \"min\": 49.92727820836355,\n        \"max\": 97.69098876343662,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          70.43281226013177,\n          97.69098876343662\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
                  }
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "=== Tabla ANOVA factorial ===\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "                              sum_sq    df          F        PR(>F)\nC(Factor_A)              5315.035232   1.0  64.090539  5.494820e-07\nC(Factor_B)              1149.116133   1.0  13.856441  1.852535e-03\nC(Factor_A):C(Factor_B)   142.877856   1.0   1.722871  2.078413e-01\nResidual                 1326.881715  16.0        NaN           NaN",
                  "text/html": "\n  <div id=\"df-995f48bd-3a31-499a-9c98-2149c89b1d53\" 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>sum_sq</th>\n      <th>df</th>\n      <th>F</th>\n      <th>PR(&gt;F)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>C(Factor_A)</th>\n      <td>5315.035232</td>\n      <td>1.0</td>\n      <td>64.090539</td>\n      <td>5.494820e-07</td>\n    </tr>\n    <tr>\n      <th>C(Factor_B)</th>\n      <td>1149.116133</td>\n      <td>1.0</td>\n      <td>13.856441</td>\n      <td>1.852535e-03</td>\n    </tr>\n    <tr>\n      <th>C(Factor_A):C(Factor_B)</th>\n      <td>142.877856</td>\n      <td>1.0</td>\n      <td>1.722871</td>\n      <td>2.078413e-01</td>\n    </tr>\n    <tr>\n      <th>Residual</th>\n      <td>1326.881715</td>\n      <td>16.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n    <div class=\"colab-df-buttons\">\n\n  <div class=\"colab-df-container\">\n    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-995f48bd-3a31-499a-9c98-2149c89b1d53')\"\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 -960 960 960\">\n    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n  </svg>\n    </button>\n\n  <style>\n    .colab-df-container {\n      display:flex;\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    .colab-df-buttons div {\n      margin-bottom: 4px;\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-995f48bd-3a31-499a-9c98-2149c89b1d53 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-995f48bd-3a31-499a-9c98-2149c89b1d53');\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\n\n    </div>\n  </div>\n",
                  "application/vnd.google.colaboratory.intrinsic+json": {
                    "type": "dataframe",
                    "summary": "{\n  \"name\": \")\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"sum_sq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2281.4002198333555,\n        \"min\": 142.87785606552873,\n        \"max\": 5315.035232362507,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1149.116132618817,\n          1326.8817153097843,\n          5315.035232362507\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"df\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.5,\n        \"min\": 1.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.066634929911274,\n        \"min\": 1.7228707507773149,\n        \"max\": 64.090538543555,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          64.090538543555,\n          13.856440939506475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PR(>F)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11946588359974872,\n        \"min\": 5.49482003803898e-07,\n        \"max\": 0.20784130632304304,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.49482003803898e-07,\n          0.0018525347555059576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
                  }
                },
                "metadata": {}
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 800x600 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Factor A: tipo de inversión, Factor B: región o sucursal.\n",
                  "- Visualizar interacción ayuda a entender combinaciones óptimas.\n",
                  "- Medias y tabla ANOVA permiten detectar efectos significativos y riesgos de inversión.\n"
                ]
              }
            ]
          }
        },
        "6665092e88dd40398416cae33335c55f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3bc584ec20ad411295503531916ee646": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c3a9f09f03cd433d8553bd908526fa31": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "d0b8208304a443e8b2240b67e51d4295": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a710758960104f4a903bc003d048d881": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "c80c6aee4e7c4f52af80337aa9acdc31": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "588a82283d054b7f9ddcdd847ad23233": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "2af770a32e674740b16071f63710f1fe": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e40854aede5745bbb8e06598b24d5894": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "2852c60a8993488ebd8d9bbdfd86e709": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0c343d9c3f594b61866ba13dba97db41": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "e4a73338648d49d2bd19302b1938ed31": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dd4e560e841a47c392c0af0bbd44b324": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "b74b4ea0ee4d41c9825ee0b6d23ef3f1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "5653e15184c548fda5d8f52e2e0d26ed",
            "ea1001530e35410986821729e2e888d5",
            "76f600f1b26645e7994e01bb401b56af",
            "2d9fd23b2e384dea98ac6c20c33c9607",
            "2084bc5411c8461fb1f0951bd54d3b4c",
            "0f3a79e4512449a8ae192321dec07c9e",
            "b2797f9b7c954d02a8c1b7f075d59637",
            "210595db7a8647c59869919327b37979",
            "6665092e88dd40398416cae33335c55f",
            "3bc584ec20ad411295503531916ee646",
            "c3a9f09f03cd433d8553bd908526fa31",
            "d0b8208304a443e8b2240b67e51d4295",
            "a710758960104f4a903bc003d048d881",
            "c80c6aee4e7c4f52af80337aa9acdc31",
            "588a82283d054b7f9ddcdd847ad23233",
            "2af770a32e674740b16071f63710f1fe",
            "e40854aede5745bbb8e06598b24d5894",
            "2852c60a8993488ebd8d9bbdfd86e709",
            "0c343d9c3f594b61866ba13dba97db41",
            "e4a73338648d49d2bd19302b1938ed31",
            "dd4e560e841a47c392c0af0bbd44b324",
            "b74b4ea0ee4d41c9825ee0b6d23ef3f1"
          ]
        },
        "id": "iHfe1RvhZXCO",
        "outputId": "f87d16a9-89e2-4566-f30e-51d41b92f923"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=2, description='Niveles A', max=5, min=2), IntSlider(value=2, descriptio…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5653e15184c548fda5d8f52e2e0d26ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.simulador_ANOVA_factorial(n_niveles_A=2, n_niveles_B=2, n_muestra=5, media_min=50, media_max=100, desviacion=10)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_ANOVA_factorial</b><br/>def simulador_ANOVA_factorial(n_niveles_A=2, n_niveles_B=2, n_muestra=5, media_min=50, media_max=100, desviacion=10)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_1186/3710637268.py</a>Simula un ANOVA factorial de dos factores para análisis financiero.\n",
              "\n",
              "Parámetros:\n",
              "- n_niveles_A: número de niveles del factor A\n",
              "- n_niveles_B: número de niveles del factor B\n",
              "- n_muestra: tamaño de muestra por combinación de niveles\n",
              "- media_min, media_max: rango de medias\n",
              "- desviacion: desviación estándar de los datos</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from statsmodels.stats.anova import AnovaRM\n",
        "from ipywidgets import interact, IntSlider, FloatSlider\n",
        "\n",
        "# 🔹 Función simuladora de ANOVA factorial 2 factores\n",
        "def simulador_ANOVA_factorial(n_niveles_A=2, n_niveles_B=2, n_muestra=5,\n",
        "                              media_min=50, media_max=100, desviacion=10):\n",
        "    \"\"\"\n",
        "    Simula un ANOVA factorial de dos factores para análisis financiero.\n",
        "\n",
        "    Parámetros:\n",
        "    - n_niveles_A: número de niveles del factor A\n",
        "    - n_niveles_B: número de niveles del factor B\n",
        "    - n_muestra: tamaño de muestra por combinación de niveles\n",
        "    - media_min, media_max: rango de medias\n",
        "    - desviacion: desviación estándar de los datos\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar combinaciones de niveles\n",
        "    combinaciones = [(a, b) for a in range(n_niveles_A) for b in range(n_niveles_B)]\n",
        "    n_combinaciones = len(combinaciones)\n",
        "\n",
        "    # 🔹 Generar datos aleatorios\n",
        "    datos = []\n",
        "    medias_combinaciones = np.linspace(media_min, media_max, n_combinaciones)\n",
        "    for i, (a, b) in enumerate(combinaciones):\n",
        "        grupo = np.random.normal(loc=medias_combinaciones[i], scale=desviacion, size=n_muestra)\n",
        "        for valor in grupo:\n",
        "            datos.append({'Factor_A': f'A{a+1}', 'Factor_B': f'B{b+1}', 'Valor': valor})\n",
        "\n",
        "    df = pd.DataFrame(datos)\n",
        "\n",
        "    # 🔹 Resumen de medias\n",
        "    tabla_medias = df.groupby(['Factor_A','Factor_B'])['Valor'].mean().reset_index()\n",
        "    print(\"=== Medias por combinación de factores ===\")\n",
        "    display(tabla_medias)\n",
        "\n",
        "    # 🔹 ANOVA factorial simplificado con interacción\n",
        "    # Usaremos statsmodels AnovaRM para un diseño repetido ficticio\n",
        "    try:\n",
        "        from statsmodels.formula.api import ols\n",
        "        import statsmodels.api as sm\n",
        "        modelo = ols('Valor ~ C(Factor_A) * C(Factor_B)', data=df).fit()\n",
        "        tabla_anova = sm.stats.anova_lm(modelo, typ=2)\n",
        "        print(\"\\n=== Tabla ANOVA factorial ===\")\n",
        "        display(tabla_anova)\n",
        "    except:\n",
        "        print(\"No se pudo calcular la ANOVA factorial automáticamente, revise librerías.\")\n",
        "\n",
        "    # 🔹 Visualización de interacción\n",
        "    plt.figure(figsize=(8,6))\n",
        "    for b in range(n_niveles_B):\n",
        "        medias_B = [df[(df['Factor_A']==f'A{a+1}') & (df['Factor_B']==f'B{b+1}')]['Valor'].mean()\n",
        "                    for a in range(n_niveles_A)]\n",
        "        plt.plot(range(1, n_niveles_A+1), medias_B, marker='o', label=f'B{b+1}')\n",
        "    plt.xticks(range(1, n_niveles_A+1), [f'A{a+1}' for a in range(n_niveles_A)])\n",
        "    plt.xlabel('Factor A')\n",
        "    plt.ylabel('Valor promedio')\n",
        "    plt.title('Interacción Factor A x Factor B')\n",
        "    plt.legend(title='Factor B')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Factor A: tipo de inversión, Factor B: región o sucursal.\")\n",
        "    print(\"- Visualizar interacción ayuda a entender combinaciones óptimas.\")\n",
        "    print(\"- Medias y tabla ANOVA permiten detectar efectos significativos y riesgos de inversión.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_ANOVA_factorial,\n",
        "    n_niveles_A=IntSlider(value=2, min=2, max=5, step=1, description='Niveles A'),\n",
        "    n_niveles_B=IntSlider(value=2, min=2, max=5, step=1, description='Niveles B'),\n",
        "    n_muestra=IntSlider(value=5, min=3, max=20, step=1, description='N° Muestra'),\n",
        "    media_min=FloatSlider(value=50, min=0, max=100, step=1, description='Media mínima'),\n",
        "    media_max=FloatSlider(value=100, min=50, max=200, step=1, description='Media máxima'),\n",
        "    desviacion=FloatSlider(value=10, min=1, max=50, step=1, description='Desviación')\n",
        ")"
      ]
    }
  ]
}