{
  "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": {
        "689b82c4b34140edb6b762f692acf72a": {
          "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_df3ac7518a6a4ad38a8bd9c22dbb3958",
              "IPY_MODEL_bdfb8310a71d4102a05835471e13e5d4",
              "IPY_MODEL_a14486d6b6034e6e9fcfe771442f633c",
              "IPY_MODEL_e88dc619108445c5a043cf8c218f9c6c",
              "IPY_MODEL_b0533b8545b14f9b95477bbbe04ae513",
              "IPY_MODEL_21953f2c85ae47b9b88b7f36e38a6ebe",
              "IPY_MODEL_7ba668ad27f1489c9860c04b46f5d281"
            ],
            "layout": "IPY_MODEL_8dae06ad619c4e03901f21ef3b17e289"
          }
        },
        "df3ac7518a6a4ad38a8bd9c22dbb3958": {
          "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_1eeddf30a58441fa9859a3d33a0628de",
            "max": 5,
            "min": 2,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_cc16baecfed0463aa70ee8563b666727",
            "value": 2
          }
        },
        "bdfb8310a71d4102a05835471e13e5d4": {
          "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_8eb419c2b42949fcbc011501d03a7b52",
            "max": 5,
            "min": 2,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_90ad9fa1695946158364c02bc5d46cc7",
            "value": 2
          }
        },
        "a14486d6b6034e6e9fcfe771442f633c": {
          "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_e4dbdd52f3d348f286ccd73a8c751a70",
            "max": 20,
            "min": 3,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_a57c9930571a4f2abcaa7bd6e1dd0bbf",
            "value": 5
          }
        },
        "e88dc619108445c5a043cf8c218f9c6c": {
          "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_23bea78f4e3c4db59af8539aa3524abe",
            "max": 100,
            "min": 0,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_e2c17ef628414e869c333be13874cdcf",
            "value": 50
          }
        },
        "b0533b8545b14f9b95477bbbe04ae513": {
          "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_e74934142316444e992c028cb171314c",
            "max": 200,
            "min": 50,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_082eacdb280b4b499617e1d608d72afd",
            "value": 100
          }
        },
        "21953f2c85ae47b9b88b7f36e38a6ebe": {
          "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_2d89399057af4288912f2a048db173f9",
            "max": 50,
            "min": 1,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 1,
            "style": "IPY_MODEL_616eb69d446a4048b77b50c4b47fee37",
            "value": 10
          }
        },
        "7ba668ad27f1489c9860c04b46f5d281": {
          "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_2b34ff46edf24227ab434eab072b258f",
            "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  54.632959\n1       A1       B2  69.367277\n2       A2       B1  81.791873\n3       A2       B2  96.635419",
                  "text/html": "\n  <div id=\"df-03cbd3e3-4ef7-401e-861e-cdd89586882e\" 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>54.632959</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A1</td>\n      <td>B2</td>\n      <td>69.367277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A2</td>\n      <td>B1</td>\n      <td>81.791873</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A2</td>\n      <td>B2</td>\n      <td>96.635419</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-03cbd3e3-4ef7-401e-861e-cdd89586882e')\"\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-03cbd3e3-4ef7-401e-861e-cdd89586882e 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-03cbd3e3-4ef7-401e-861e-cdd89586882e');\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\": 17.881942548275816,\n        \"min\": 54.63295853827468,\n        \"max\": 96.63541888279462,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          69.3672771986958,\n          96.63541888279462\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)              3702.880522   1.0  34.801322  0.000022\nC(Factor_B)              1093.562605   1.0  10.277789  0.005509\nC(Factor_A):C(Factor_B)     0.014913   1.0   0.000140  0.990700\nResidual                 1702.409146  16.0        NaN       NaN",
                  "text/html": "\n  <div id=\"df-dee6a1f8-3b60-4e9f-a729-e7250777a823\" 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>3702.880522</td>\n      <td>1.0</td>\n      <td>34.801322</td>\n      <td>0.000022</td>\n    </tr>\n    <tr>\n      <th>C(Factor_B)</th>\n      <td>1093.562605</td>\n      <td>1.0</td>\n      <td>10.277789</td>\n      <td>0.005509</td>\n    </tr>\n    <tr>\n      <th>C(Factor_A):C(Factor_B)</th>\n      <td>0.014913</td>\n      <td>1.0</td>\n      <td>0.000140</td>\n      <td>0.990700</td>\n    </tr>\n    <tr>\n      <th>Residual</th>\n      <td>1702.409146</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-dee6a1f8-3b60-4e9f-a729-e7250777a823')\"\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-dee6a1f8-3b60-4e9f-a729-e7250777a823 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-dee6a1f8-3b60-4e9f-a729-e7250777a823');\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\": 1554.1966364871219,\n        \"min\": 0.014913291274389532,\n        \"max\": 3702.880521655994,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1093.5626045502886,\n          1702.4091456836807,\n          3702.880521655994\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\": 17.87995135941371,\n        \"min\": 0.00014016175899619397,\n        \"max\": 34.801321701489634,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34.801321701489634,\n          10.277788813086934\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PR(>F)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5703909816539803,\n        \"min\": 2.2421938962451486e-05,\n        \"max\": 0.990700435777531,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.2421938962451486e-05,\n          0.005508981146463129\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": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAIjCAYAAADC0ZkAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgcRJREFUeJzs3Xd4VGXexvHvzKRXCCQkgUBCht6llwSCKNiwu+iqgCwoTRHL6r5rYdddu6JSLIugiL33Sui9SychdEILpJI2c94/jkQjLYGEM0nuz3Xl0vwymbkzQHLnzDnPYzMMw0BERERExMPZrQ4gIiIiIlIWKq4iIiIiUiWouIqIiIhIlaDiKiIiIiJVgoqriIiIiFQJKq4iIiIiUiWouIqIiIhIlaDiKiIiIiJVgoqriNQoBQUF/Pe//+WHH36wOoqIiJSTiquIVCs2m43HH3/8tB8fP348s2bNomvXrhculIiIVAgVV5EqbsaMGdhsNlasWFHuz83Ly+Pxxx9nzpw5FR/MA3344Yd8/vnnfPfdd9SqVatC7/vxxx/HZrOd8u3VV1+t0MfauHEjjz/+ODt27KjQ+y2rBx98EJvNxl/+8pcL9ph9+vQ57fO7efPmCn2sb7/99oy//FSGP399Pj4+xMXFMWLECHbv3n1Bs4h4Mi+rA4iIdfLy8pgwYQJg/uCsDo4fP46X18nf2gzDYM+ePXz33Xc0bNiw0h5/6tSpBAUFlZpV9NHdjRs3MmHCBPr06UNsbGyF3vfZGIbBe++9R2xsLF999RXZ2dkEBwdfkMdu0KABTz755Enz6OjoCn2cb7/9lsmTJ1/w8vrHr6+wsJCNGzfy6quv8sMPP7Bp0yYCAgIuaB4RT6TiKiIVLjc3l8DAQEse28/P75Rzm83G+PHjK/3xb7jhBurWrVvpj1MZ8vLyzlqO5syZw549e5g9ezb9+/fn008/ZfDgwRckX2hoKLfeeusFeayK5na7KSwsPO3fTzj11xcXF8eYMWNYuHAhl1xySWXHFPF4OlVApBoaMmQIQUFB7N27l2uuuYagoCDCw8O5//77cblcAOzYsYPw8HAAJkyYUPIS5R+PMm3evJkbbriBsLAw/Pz86NSpE19++WWpxzpxqsLcuXMZNWoUERERNGjQAICdO3cyatQomjVrhr+/P3Xq1OHGG2885Uvcx44d49577yU2NhZfX18aNGjA7bffzuHDh0tuk5+fz+OPP07Tpk3x8/MjKiqK6667jtTU1JLbnOoc19WrV3PZZZcREhJCUFAQF198MUuWLDnl17Fw4ULGjx9PeHg4gYGBXHvttRw6dKjcfwZ/tm7dOoYMGULjxo3x8/MjMjKSO+64gyNHjpx027179zJs2DCio6Px9fUlLi6OkSNHUlhYyIwZM7jxxhsBSEpKKvlz++PpHlOmTKFVq1b4+voSHR3N6NGjOXbsWKnH6NOnD61bt2blypUkJiYSEBDAP/7xj7N+HbNmzaJly5YkJSXRr18/Zs2aVaavf/r06dhsNt58881S8//+97/YbDa+/fbbMt3P6XzxxRdcccUVJc9ZfHw8//73v0v+vv/R0qVLufzyy6lduzaBgYG0bduWl156CTD/7UyePBmg1Ev3J+Tm5nLfffcRExODr68vzZo147nnnsMwjFKPYbPZGDNmDLNmzSr5s/j+++/L/XVFRkYCnPJVBJGaSP8SRKopl8tF//796dq1K8899xw///wzzz//PPHx8YwcOZLw8HCmTp3KyJEjufbaa7nuuusAaNu2LQAbNmygZ8+e1K9fn4ceeojAwEA+/PBDrrnmGj755BOuvfbaUo83atQowsPDefTRR8nNzQVg+fLlLFq0iEGDBtGgQQN27NjB1KlT6dOnDxs3biw5upeTk0NCQgKbNm3ijjvu4KKLLuLw4cN8+eWX7Nmzh7p16+Jyubjyyiv55ZdfGDRoEPfccw/Z2dn89NNPrF+/nvj4+FM+Dxs2bCAhIYGQkBAefPBBvL29ee211+jTpw9z58496WX8sWPHUrt2bR577DF27NjBxIkTGTNmDB988EGZnveMjIxS7zscDmrXrs1PP/3E9u3bGTp0KJGRkWzYsIHXX3+dDRs2sGTJkpJytG/fPrp06cKxY8cYMWIEzZs3Z+/evXz88cfk5eWRmJjI3Xffzcsvv8w//vEPWrRoAVDy38cff5wJEybQr18/Ro4cyZYtW5g6dSrLly9n4cKFeHt7l2Q7cuQIl112GYMGDeLWW2+lXr16Z/zaCgoK+OSTT7jvvvsAuPnmmxk6dCjp6eklBet0hg4dyqeffsr48eO55JJLiImJ4ddff2XChAkMGzaMyy+//KzPrcvlKvWLDJhH2IOCgpgxYwZBQUGMHz+eoKAgZs+ezaOPPkpWVhbPPvtsye1/+uknrrzySqKiorjnnnuIjIxk06ZNfP3119xzzz3ceeed7Nu3j59++omZM2eWeizDMBg4cCDJyckMGzaM9u3b88MPP/DAAw+wd+9eXnzxxVK3nz17Nh9++CFjxoyhbt26Zz2t449fX1FREZs2beKxxx7D6XTSs2fPsz4/IjWCISJV2vTp0w3AWL58ecls8ODBBmD861//KnXbDh06GB07dix5/9ChQwZgPPbYYyfd78UXX2y0adPGyM/PL5m53W6jR48eRpMmTU56/F69ehnFxcWl7iMvL++k+128eLEBGG+//XbJ7NFHHzUA49NPPz3p9m632zAMw3jzzTcNwHjhhRdOexvDME76eq655hrDx8fHSE1NLZnt27fPCA4ONhITE0/6Ovr161fq/u69917D4XAYx44dO+lx/+ixxx4zgJPeGjVqdNrn4r333jMAY968eSWz22+/3bDb7aX+PP/8dX700UcGYCQnJ5f6+MGDBw0fHx/j0ksvNVwuV8l80qRJBmC8+eabJbPevXsbgPHqq6+e8ev6o48//tgAjG3bthmGYRhZWVmGn5+f8eKLL5bp8/fv32+EhYUZl1xyiVFQUGB06NDBaNiwoZGZmXnWzz2R989vgwcPNgzj1M/vnXfeaQQEBJT8HS4uLjbi4uKMRo0aGUePHi112z/+mY8ePdo41Y/Hzz//3ACMJ554otT8hhtuMGw2m5GSklIyAwy73W5s2LDhrF/bmb6+Fi1aGNu3by/TfYjUBDpVQKQau+uuu0q9n5CQwPbt28/6eRkZGcyePZubbrqJ7OxsDh8+zOHDhzly5Aj9+/dn27Zt7N27t9TnDB8+HIfDUWrm7+9f8v9FRUUcOXIEp9NJrVq1WLVqVcnHPvnkE9q1a3fSUVyg5EjkJ598Qt26dRk7duxpb/NnLpeLH3/8kWuuuYbGjRuXzKOiorjllltYsGABWVlZpT5nxIgRpe4vISEBl8vFzp07T/kYf/bJJ5/w008/lbydeCn9j89Ffn4+hw8fplu3bgAlz4Xb7ebzzz/nqquuolOnTmX+Ok/4+eefKSwsZNy4cdjtv397Hz58OCEhIXzzzTelbu/r68vQoUPL9HWBeZpAp06dcDqdAAQHB3PFFVeU+XSByMhIJk+ezE8//URCQgJr1qzhzTffJCQkpEyfHxsbW+q5/emnn3jwwQeB0s/vib+zCQkJ5OXllaw6sHr1atLS0hg3btxJq0qc7bkF86Ith8PB3XffXWp+3333YRgG3333Xal57969admyZZm+tj9/fd999x0TJ04kMzOTyy67rEJOVxGpDnSqgEg15efnV3IO6wm1a9fm6NGjZ/3clJQUDMPgkUce4ZFHHjnlbQ4ePEj9+vVL3o+LizvpNsePH+fJJ59k+vTp7N27t9R5gJmZmSX/n5qayvXXX3/GTKmpqTRr1qxc5/odOnSIvLw8mjVrdtLHWrRogdvtZvfu3bRq1apk/ucVB2rXrg1QpucNIDEx8ZQXZ2VkZDBhwgTef/99Dh48WOpjJ56LQ4cOkZWVRevWrcv0WH92olz/+ev18fGhcePGJ5Xv+vXr4+PjU6b7PnbsGN9++y1jxowhJSWlZN6zZ08++eQTtm7dStOmTc96P4MGDeKdd97hm2++YcSIEVx88cVlenyAwMBA+vXrd8qPbdiwgX/+85/Mnj37pF9GTjy/J86FPp/nNzo6+qRVFE6cpvHn5/dU/ybO5M9f34ABA+jVqxedOnXiqaee4vnnnz+n3CLViYqrSDX156Of5eF2uwG4//776d+//ylvc+Ko2wl/POJ1wtixY5k+fTrjxo2je/fuhIaGYrPZGDRoUMljeJrTPW/Gny6+Ka+bbrqJRYsW8cADD9C+fXuCgoJwu90MGDDAsufiVH9mp/PRRx9RUFDA888/f8oCNWvWrJKl1c7kyJEjJWsOb9y4EbfbXero8Lk4duwYvXv3JiQkhH/961/Ex8fj5+fHqlWr+Pvf/14lnt/T6dixI6GhocybN68CEolUfSquIjXY6V4ePfGyure392mPcJXFxx9/zODBg0sVnfz8/JOucI+Pj2f9+vVnvK/4+HiWLl1KUVFRqQuMziQ8PJyAgAC2bNly0sc2b96M3W4nJiamTPd1Po4ePcovv/zChAkTePTRR0vm27ZtOylvSEjIWZ+L0/25NWrUCIAtW7aUOjWisLCQtLS08/qznDVrFq1bt+axxx476WOvvfYa7777bpmK6+jRo8nOzubJJ5/k4YcfZuLEiee9TNmcOXM4cuQIn376KYmJiSXztLS0Urc7cQHf+vXrz/hcnOn5/fnnn09au/bEqQgnnv+K5nK5yMnJqZT7FqlqdI6rSA124qr+PxfJiIgI+vTpw2uvvcb+/ftP+ryynm/ncDhOOlL5yiuvnLRE0fXXX8/atWv57LPPTrqPE59//fXXc/jwYSZNmnTa25zq8S+99FK++OKLUktwHThwgHfffZdevXqV+fzK83HiKO6fc06cOLHU+3a7nWuuuYavvvrqlDuhnfj8E2vk/vnPrV+/fvj4+PDyyy+Xeqxp06aRmZnJFVdccU75d+/ezbx587jpppu44YYbTnobOnQoKSkpLF269Iz38/HHH/PBBx/w1FNP8dBDDzFo0CD++c9/snXr1nPKdcKpnt/CwkKmTJlS6nYXXXQRcXFxTJw48aTn7o+fe7rn9/LLL8flcp30d/DFF1/EZrNx2WWXndfXcSrJycnk5OTQrl27Cr9vkapIR1xFajB/f39atmzJBx98QNOmTQkLC6N169a0bt2ayZMn06tXL9q0acPw4cNp3LgxBw4cYPHixezZs4e1a9ee9f6vvPJKZs6cSWhoKC1btmTx4sX8/PPP1KlTp9TtHnjgAT7++GNuvPFG7rjjDjp27EhGRgZffvklr776Ku3ateP222/n7bffZvz48SxbtoyEhARyc3P5+eefGTVqFFdfffUpMzzxxBP89NNP9OrVi1GjRuHl5cVrr71GQUEBzzzzTIU8j2cTEhJCYmIizzzzDEVFRdSvX58ff/zxpCOCYK5r+uOPP9K7d29GjBhBixYt2L9/Px999BELFiygVq1atG/fHofDwdNPP01mZia+vr707duXiIgIHn74YSZMmMCAAQMYOHAgW7ZsYcqUKXTu3PmcF+9/9913S5aCOpXLL78cLy8vZs2addpdwg4ePMjIkSNJSkpizJgxAEyaNInk5GSGDBnCggULzvmUgR49elC7dm0GDx7M3Xffjc1mY+bMmSf9omC325k6dSpXXXUV7du3Z+jQoURFRbF582Y2bNjADz/8AJgvzwPcfffd9O/fH4fDwaBBg7jqqqtISkri//7v/9ixYwft2rXjxx9/5IsvvmDcuHGnXZKtrDIzM3nnnXcAKC4uLlnKzN/fn4ceeui87luk2rBiKQMRqTinWw4rMDDwpNueWLLpjxYtWmR07NjR8PHxOWkpqdTUVOP22283IiMjDW9vb6N+/frGlVdeaXz88cdnfPwTjh49agwdOtSoW7euERQUZPTv39/YvHmz0ahRo5JljE44cuSIMWbMGKN+/fqGj4+P0aBBA2Pw4MHG4cOHS26Tl5dn/N///Z8RFxdneHt7G5GRkcYNN9xQaqmrP38NhmEYq1atMvr3728EBQUZAQEBRlJSkrFo0aKzPo+GYRjJycmnXHrqz048t4cOHTrlx/fs2WNce+21Rq1atYzQ0FDjxhtvNPbt23fKvDt37jRuv/12Izw83PD19TUaN25sjB492igoKCi5zRtvvGE0btzYcDgcJ+WbNGmS0bx5c8Pb29uoV6+eMXLkyJOWf+rdu7fRqlWrM35NJ7Rp08Zo2LDhGW/Tp08fIyIiwigqKjrlx6+77jojODjY2LFjR6n5F198YQDG008/fcb7P1vehQsXGt26dTP8/f2N6Oho48EHHzR++OGHU/7ZLViwwLjkkkuM4OBgIzAw0Gjbtq3xyiuvlHy8uLjYGDt2rBEeHm7YbLZS/2ays7ONe++914iOjja8vb2NJk2aGM8++2yp5bQMw/x7OHr06DN+TX/++vjDMlg2m80ICwszBg4caKxcubLM9yNS3dkM4zyvOBARERERuQB0jquIiIiIVAkqriIiIiJSJai4ioiIiEiVoOIqIiIiIlWCiquIiIiIVAkqriIiIiJSJVT7DQjcbjf79u0jODj4tNv4iYiIiIh1DMMgOzub6OjoM25GUu2L6759+y7IXuQiIiIicn52795NgwYNTvvxal9cg4ODAfOJuBB7kouIiIhI+WRlZRETE1PS206n2hfXE6cHhISEqLiKiIiIeLCzndapi7NEREREpEpQcRURERGRKkHFVURERESqhGp/jmtZGIZBcXExLpfL6iiW8vb2xuFwWB1DRERE5JRqfHEtLCxk//795OXlWR3FcjabjQYNGhAUFGR1FBEREZGT1Oji6na7SUtLw+FwEB0djY+PT43dpMAwDA4dOsSePXto0qSJjryKiIiIx6nRxbWwsBC3201MTAwBAQFWx7FceHg4O3bsoKioSMVVREREPI4uzoIzbi1Wk9TUo80iIiJSNaixiYiIiEiVoOIqIiIiIlWCiquIiIiIVAkqrmcwZMgQbDbbSW8pKSnnfd99+vRh3Lhx5x/yNB5//PFSmUNDQ0lISGDu3LmV9pgiIiIilUnF9SwGDBjA/v37S73FxcVZHatEYWHhaT/WqlWrksyLFy+mSZMmXHnllWRmZl7AhCIiIiIVQ8X1LHx9fYmMjCz15nA4eOGFF2jTpg2BgYHExMQwatQocnJySn3uwoUL6dOnDwEBAdSuXZv+/ftz9OhRhgwZwty5c3nppZdKjoju2LEDgLlz59KlSxd8fX2JiorioYceori4uOQ++/Tpw5gxYxg3bhx169alf//+p83u5eVVkrlly5b861//Iicnh61bt1bKcyUiIiLVgNsFafPh14/N/7o9Z2dRFddzZLfbefnll9mwYQNvvfUWs2fP5sEHHyz5+Jo1a7j44otp2bIlixcvZsGCBVx11VW4XC5eeuklunfvzvDhw0uOiMbExLB3714uv/xyOnfuzNq1a5k6dSrTpk3jiSeeKPXYb731Fj4+PixcuJBXX321THkLCgqYPn06tWrVolmzZhX6XIiIiEg1sfFLmNga3roSPhlm/ndia3PuAWr0BgRl8fXXX5faAvWyyy7jo48+KnV+amxsLE888QR33XUXU6ZMAeCZZ56hU6dOJe+D+dL9CT4+PgQEBBAZGVkymzJlCjExMUyaNAmbzUbz5s3Zt28ff//733n00UdL1ptt0qQJzzzzzFmz//rrryXZ8/LyCA4O5oMPPiAkJOTcngwRERGpvjZ+CR/eDhil51n7zflNb0PLgZZEO0HF9SySkpKYOnVqyfuBgYEA/Pzzzzz55JNs3ryZrKwsiouLyc/PJy8vj4CAANasWcONN95YrsfatGkT3bt3L7URQM+ePcnJyWHPnj00bNgQgI4dO5bp/po1a8aXX5q/IWVnZ/PBBx9w4403kpycTKdOncqVTURERKoxtwu+/zsnlVb4bWaD7x+C5leA3brdNXWqwFkEBgbidDpL3qKiotixYwdXXnklbdu25ZNPPmHlypVMnjwZ+P1iKX9//0rNVBY+Pj4luTt06MBTTz1F/fr1mThxYqVlExERkSpo5yLI2neGGxiQtde8nYVUXM/BypUrcbvdPP/883Tr1o2mTZuyb1/pP+y2bdvyyy+/nPY+fHx8cLlKn+zcokULFi9ejGH8/tvOwoULCQ4OpkGDBhWS3eFwcPz48Qq5LxEREakGigvg1w/LdtucA5Wb5SxUXM+B0+mkqKiIV155he3btzNz5syTLpJ6+OGHWb58OaNGjWLdunVs3ryZqVOncvjwYcA8L3bp0qXs2LGDw4cP43a7GTVqFLt372bs2LFs3ryZL774gscee4zx48eXnN9aHsXFxaSnp5Oens62bdt44okn2LhxI1dffXWFPA8iIiJShRXmwZJX4eUOsOrtsn1OUL3KzXQWKq7noF27drzwwgs8/fTTtG7dmlmzZvHkk0+Wuk3Tpk358ccfWbt2LV26dKF79+588cUXeHmZpxXff//9OBwOWrZsSXh4OLt27aJ+/fp8++23LFu2jHbt2nHXXXcxbNgw/vnPf55Tzg0bNhAVFUVUVBTt27fnww8/ZOrUqdx+++3n/RyIiIhIFVWQDQsmwkttzfNas/ZCUCT4hgK203ySDULqQ6MeFzDoKVIYf3xduhrKysoiNDSUzMzMk66mz8/PJy0tjbi4OPz8/CxK6Dn0fIiIiFRjx4/C0tdhyRTIP2bOajWEXvdC+7/C1h9+W1UASl+k9VuZrcRVBc7U1/5IqwqIiIiIVGc5h2DJZFj2PyjMNmd1nJBwH7S5ERze5qzlQLOcfv/30hdqhUTDgKcsXwoLVFxFREREqqesfbDoFVgxHYp/uzA7ohUk3gctrzn1slYtB5pLXu1cZF6IFVTPPD3AwiWw/kjFVURERKQ6OboTFk6E1e+Ay1ymk+gOkPggNB0AZ7vg2+6AuIRKj3kuVFxFREREqoPD22D+C7DuAzB+W3KzYXdIfADi+4LtdBdeVR0qriIiIiJVWfp6mP88bPiMkouqGieZhTW2p6XRKpqKq4iIiEhVtHclzHsetnzz+6zZ5ZBwPzQo2/bwVY2Kq4iIiEhVsnMRzHsOUk/s0GmDVteYqwREtrEyWaVTcRURERHxdIYB25PNwrpzoTmzOaDtTdBrPIQ3tTbfBaLiKiIiIuKpDAO2fg/znjVPDQCwe0OHW6HnPRAWZ22+C0zFtQK43AbL0jI4mJ1PRLAfXeLCcNir/pV7IiIiYhG3CzZ+YV50dWC9OfPyh45DoMdYCK1vaTyrqLiep+/X72fCVxvZn5lfMosK9eOxq1oyoHVUpT3ukCFDeOutt0reDwsLo3PnzjzzzDO0bdsWgP/85z988803rFmzBh8fH44dO1ZpeURERKQCuIrg14/NwnpkmznzCYLOf4PuoyEowtp8FjvLCrRyJt+v38/Id1aVKq0A6Zn5jHxnFd+v31+pjz9gwAD279/P/v37+eWXX/Dy8uLKK68s+XhhYSE33ngjI0eOrNQcIiIicp6KC8wdrl7pCJ/fZZZWv1Do8zCM+xUumVDjSyvoiGsphmFwvMhVptu63AaPfbnhxGpppe8HsAGPf7mRns66ZTptwN/bga2cCwP7+voSGRkJQGRkJA899BAJCQkcOnSI8PBwJkyYAMCMGTPKdb8iIiJygRTmwaq3YeFLkL3PnAXUhR5joNMw8AuxNp+HUXH9g+NFLlo++kOF3JcBpGfl0+bxH8t0+43/6k+Az7n/ceTk5PDOO+/gdDqpU6fOOd+PiIiIXAD5WbBiGiyaBHmHzVlwlHnB1UWDwSfA2nweSsW1Cvv6668JCgoCIDc3l6ioKL7++mvsZ9uDWERERKyRlwHLXoclUyH/mDmr1RB63Qvt/wpevpbG83Qqrn/g7+1g47/6l+m2y9IyGDJ9+VlvN2NoZ7rEhZXpscsrKSmJqVOnAnD06FGmTJnCZZddxrJly2jUqFG5709EREQqSc4hWDwJlk+DwmxzVqeJuWlAmxvA4W1tvipCxfUPbDZbmV+uT2gSTlSoH+mZ+ac8z9UGRIb6kdAkvNKWxgoMDMTpdJa8/7///Y/Q0FDeeOMNnnjiiUp5TBERESmHrH2w8GVYOQOKj5uzeq3NwtryarCX/8BVTabieo4cdhuPXdWSke+swgalyuuJmvrYVS0v6HquNpsNu93O8ePHL9hjioiIyCkc3QELJsKaWeAqNGfRF0HvB6HpACjnBdliUnE9DwNaRzH11otOWsc18gKs4wpQUFBAeno6YJ4qMGnSJHJycrjqqqsA2LVrFxkZGezatQuXy8WaNWsAcDqdJefGioiISAU6tBUWvADrPgTjt5WKGvWExPuhcZIK63lScT1PA1pHcUnLSEt2zvr++++JijLLcXBwMM2bN+ejjz6iT58+ADz66KOlNino0KEDAMnJySW3ERERkQqQvh7mPwcbPqfkddj4vpBwP8T2tDJZtWIzDONUp2hWG1lZWYSGhpKZmUlISOm10PLz80lLSyMuLg4/Pz+LEnoOPR8iIiLltGelWVi3fPv7rNkVkHgf1O9oXa4q5kx97Y90xFVERESkvHYsNAtr6uzfBjZoda150VVka0ujVWcqriIiIiJlYRhmUZ33HOxaZM5sDmj7F0gYD3WbWJuvBlBxFRERETkTtxu2fg/znoV9q8yZw8fcMKDXOKgda2W6GkXFVURERORU3C7Y+DnMex4ObjBnXv7QaSj0GAsh0ZbGq4lUXEVERET+yFUEv34E85+HIynmzCcYuvwNuo2GoHBr89VgKq4iIiIiAMUF5oYBC16EY7vMmV8t6DYKuo4A/9qWxhMVVxEREanpCvNg1Vuw8CXI3m/OAsOh+xjoPAx8g63NJyVUXEVERKRmys+C5f+DxZMh77A5C46GnvfARbeDT4C1+eQkKq4iIiJSs+RlwNLXYOlUyM80Z7UaQa97of0t4OVrbT45LRXXiuB2wc5FkHMAgupBox5gd1idSkRERP4o5yAsngTLp0Fhjjmr29TcNKD1DeBQLfJ0dqsDVHkbv4SJreGtK+GTYeZ/J7Y255VoyJAh2Gy2krc6deowYMAA1q1bB8COHTsYNmwYcXFx+Pv7Ex8fz2OPPUZhYWGl5hIREfE4mXvhu7/DxDbmeayFOVCvNdw4A0YtgXaDVFqrCBXX87HxS/jwdsjaV3qetd+cV3J5HTBgAPv372f//v388ssveHl5ceWVVwKwefNm3G43r732Ghs2bODFF1/k1Vdf5R//+EelZhIREfEYGWnw1T3wUjtY+ioU50P9jnDz+3DXAnOLVr1CWqXo14s/Mgwoyivbbd0u+O5BwDjVHQE2+P7v0LhP2f5ReAeAzVb2rICvry+RkZEAREZG8tBDD5GQkMChQ4cYMGAAAwYMKLlt48aN2bJlC1OnTuW5554r1+OIiIhUKYe2woIXYN2HYLjMWaNekHi/+XO5nD9vxXOouP5RUR78t6J2wTDMI7FPxZTt5v/YBz6B5/xoOTk5vPPOOzidTurUqXPK22RmZhIWFnbOjyEiIuLR0n+Fec/Bxi8oObAUf7FZWBv1sDSaVAwV1yrs66+/JigoCIDc3FyioqL4+uuvsdtPPgMkJSWFV155RUdbRUSk+tmzwiysW7/7fdb8SkgYb54aINWGiusfeQeYRz7LYucimHXD2W/314/L9lued/nXiktKSmLq1KkAHD16lClTpnDZZZexbNkyGjVqVHK7vXv3MmDAAG688UaGDx9e7scRERHxSDsWwrxnYXvybwMbtL7OXCWgXitLo0nlUHH9I5ut7C/Xx/eFkGjzQqxTnudqMz8e37fSTvwODAzE6XSWvP+///2P0NBQ3njjDZ544gkA9u3bR1JSEj169OD111+vlBwiIiIXjGFA6i/mEdZdi82ZzWGuDNDrXqjbxNp8UqlUXM+V3QEDnjZXD8BG6fL620nfA566oFcr2mw27HY7x48fB8wjrUlJSXTs2JHp06ef8hQCERGRKsHtNk8FmPcs7Fttzhw+0OFW6DkOajc646dL9aDiej5aDoSb3jZXD/jjklgh0WZpbTmwUh++oKCA9PR0wDxVYNKkSeTk5HDVVVexd+9e+vTpQ6NGjXjuuec4dOhQyeedWIlARETE47ldsOEzmP8CHNxgzrz8odMd0GOM+TNXagwV1/PVciA0v8KSnbO+//57oqKiAAgODqZ58+Z89NFH9OnThxkzZpCSkkJKSgoNGjQo9XmGcapTG0RERDyIq8hczmrBC3AkxZz5BEOX4dBtFASFW5tPLGEzqnmLycrKIjQ0lMzMTEJCQkp9LD8/n7S0NOLi4vDz87MooefQ8yEiIpYryoc1s2DBRMjcZc78a5tltctw8/+l2jlTX/sjHXEVERER6xXmwsq3YNHLkL3fnAWGQ4+x5mkBvsHW5hOPoOIqIiIi1snPguVvwOIpkHfYnIXUh573wEW3g7e/tfnEo6i4ioiIyIWXlwFLXzXf8jPNWa1G5qYB7W4GL19r84lHUnEVERGRCyf7ACyeBCvehMIcc1a3KSTcD62vB4eqiZye/nagq+xP0PMgIiKVJnMPLHwZVr0FxfnmrF4bSLwfWgwErTUuZVCji6u3tzcAeXl5+PvrHJrCwkIAHI4Lt2mCiIhUcxlpsOBFWPMuuIvMWf1OkPgANO1v7lopUkY1urg6HA5q1arFwYMHAQgICMBWQ/8Bud1uDh06REBAAF5eNfqvhYiIVIRDW8xNA379CAyXOYtNMI+wxvVWYZVzUuMbyoldpE6U15rMbrfTsGHDGlveRUSkAuxfB/Ofh41fULIdurOfeQ5ro+6WRpOqr8YXV5vNRlRUFBERERQVFVkdx1I+Pj7YdY6RiIici93LYf5zsPX732fNr4SE+6D+RdblkmqlxhfXExwOh87tFBERKQ/DgJ0LYd6zsH2OObPZodV15rJW9VpZGk+qHxVXERERKR/DgJRfzMK6e4k5s3tB20HQ616o67Q2n1RbKq4iIiJSNm43bPnWLKz715gzhw90uM3c6ap2I0vjSfWn4ioiIiJn5nbBhs/Mi64ObjRn3gHQ6Q7oPgZCoqzNJzWGiquIiIicmqsI1n1gLmuVkWrOfIKh6wjoNgoC61qbT2ocFVcREREprSgf1rwDC16CzF3mzL+2WVa7DDf/X8QCKq4iIiJiKsyFlTPMrVlz0s1ZYAT0GGueFuAbZGk8EUsX7czOzmbcuHE0atQIf39/evTowfLly0s+bhgGjz76KFFRUfj7+9OvXz+2bdtmYWIREZFqKD8T5j0HE9vAD/8wS2tIfbjsWRi3DnrerdIqHsHS4vq3v/2Nn376iZkzZ/Lrr79y6aWX0q9fP/bu3QvAM888w8svv8yrr77K0qVLCQwMpH///uTn51sZW0REpHrIy4DZ/4EX28Dsf0PeEagdC1e9DHevMc9l9fa3OqVICZthGIYVD3z8+HGCg4P54osvuOKKK0rmHTt25LLLLuPf//430dHR3Hfffdx///0AZGZmUq9ePWbMmMGgQYPK9DhZWVmEhoaSmZlJSEhIpXwtIiIiVUr2AVg8CZZPg6Jcc1a3mbnLVevrwaEzCeXCKmtfs+xvZnFxMS6XCz8/v1Jzf39/FixYQFpaGunp6fTr16/kY6GhoXTt2pXFixeftrgWFBRQUFBQ8n5WVlblfAEiIiJVTeYeWPgSrHobin979TKyDSQ+AM2vAm37LR7OsuIaHBxM9+7d+fe//02LFi2oV68e7733HosXL8bpdJKebp4UXq9evVKfV69evZKPncqTTz7JhAkTKjW7iIhIlZKxHRa8CGveA3eROWvQ2SysTS4Fm83afCJlZOmvVjNnzsQwDOrXr4+vry8vv/wyN998M/bz+I3v4YcfJjMzs+Rt9+7dFZhYRESkCjm4GT4dAa90NI+yuosgNgFu/xKG/QRN+6u0SpVi6Uks8fHxzJ07l9zcXLKysoiKiuIvf/kLjRs3JjIyEoADBw4QFfX7jhwHDhygffv2p71PX19ffH19Kzu6iIiI59q/DuY/Bxu/BH67lMV5CSTeDw27WRpN5Hx4xMksgYGBREVFcfToUX744Qeuvvpq4uLiiIyM5Jdffim5XVZWFkuXLqV79+4WphUREfFQu5fBrJvgtQTY+AVgQPMrYcQcuPVjlVap8iw94vrDDz9gGAbNmjUjJSWFBx54gObNmzN06FBsNhvjxo3jiSeeoEmTJsTFxfHII48QHR3NNddcY2VsERERz2EYsGMBzHsW0uaaM5vdXB2g13io19LafCIVyNLimpmZycMPP8yePXsICwvj+uuv5z//+Q/e3t4APPjgg+Tm5jJixAiOHTtGr169+P77709aiUBERKTGMQxI+dksrLuXmjO7F7QbZBbWOvHW5hOpBJat43qhaB1XERGpVtxu2PKNWVj3rzVnDl+46DboeQ/UamhtPpFz4PHruIqIiEg5uF2w4TNza9ZDm8yZdwB0ugN6jIXgSGvziVwAKq4iIiKerLgQ1n1grsOakWrOfEOgywjoNgoC61ibT+QCUnEVERHxREX5sHqmudNV5m9rkvvXhm6joctw8K9laTwRK6i4ioiIeJLCXFgxHRa9DDkHzFlghHk6QKc7wDfI2nwiFlJxFRER8QT5mbDsdVg8BY5nmLOQBtBrHHS4Fbz9LY0n4glUXEVERKyUewSWToWlr0NBpjmrHQcJ46HtIPDysTafiAdRcRUREbFC9gFY/AosfxOKcs1ZeHNIuA9aXQcO/YgW+TP9qxAREbmQju02L7ha9Ta4CsxZZFtIfMDcntXuEbuxi3gkFVcREZEL4UiquaTV2vfAXWzOGnQxC2uTS8BmszafSBWg4ioiIlKZDm6G+c/D+o/BcJuzuESzsMYmqLCKlIOKq4iISGXYv9bc5WrTl7/PmlwKCfdDw67W5RKpwlRcRUREKtLuZTDvWdj24++zFleZhTW6vWWxRKoDFVcREZHzZRiwY75ZWNPmmTObHVpfb64SENHC2nwi1YSKq4iIyLkyDNj2k1lY9ywzZ3YvaHcz9LoX6sRbm0+kmlFxFRERKS+3GzZ/bRbW9HXmzOELF90OPe+BWjHW5hOpplRcRUREyspVDBs+g/nPwaHN5sw7EDrfAd3HQHCktflEqjkVVxERkbMpLoR1H8CCFyBjuznzDYGud0LXkRBYx9p8IjWEiquIiMjpFB2H1e/AgomQtcec+YdB91HQeTj417IynUiNo+IqIiLyZwU5sHI6LHoFcg6Ys6B60GMsdBwKvkHW5hOpoVRcRURETjh+DJa/AYunwPEMcxYaY15w1eE28PazNJ5ITafiKiIiknsElkyBZa9DQZY5C2sMvcZD27+Al4+1+UQEUHEVEZGaLDvdPB1gxZtQlGfOwluYmwa0uhYc+jEp4kn0L1JERGqeY7th4Uuw6m1wFZizqHaQ+AA0uwLsdmvzicgpqbiKiEjNcSTVXNJq7fvgLjZnDbpA7wfB2Q9sNmvzicgZqbiKiEj1d3ATzH8e1n8ChtucxSWaR1hjE1RYRaoIFVcREam+9q0xd7na9NXvsyb9IfF+iOliWSwROTcqriIiUv3sWgrznoWUn36ftRhoFtaodtblEpHzouIqIiLVg2FA2jyzsO6Yb85sdmh9AySMh4gW1uYTkfOm4ioiIlWbYcC2H2Hec7BnmTmze0P7m6HnOKgTb2k8Eak4Kq4iIlI1ud2w+SuzsKavM2cOX+g4GHrcDbVirM0nIhVOxVVERKoWVzFs+NRcJeDQZnPmHQidh0H3MRBcz9p8IlJpVFxFRKRqKC6Ede/D/BfgaJo58w2FrndCt5EQEGZtPhGpdCquIiLi2YqOw6qZ5k5XWXvMmX8YdB8NXYaDX6i1+UTkglFxFRERz1SQAyvehEWvQO5BcxZUzzx/tdNQ8Am0Np+IXHAqriIi4lmOH4Nlb8CSyXD8qDkLjYFe46D9reDtZ2U6EbGQiquIiHiG3MOwZIpZWguyzFlYY0i4D9r+BRze1uYTEcupuIqIiLWy083TAVa8CUV55iy8hbnLVatrwe6wNp+IeAwVVxERscaxXeYFV6tmgqvAnEW1h8QHoNnlYLdbGk9EPI+Kq4iIXFhHUs0lrda9D+5icxbTFRIfBOfFYLNZm09EPJaKq4iIXBgHNpqbBmz4FAy3OYvrbR5hje2lwioiZ6XiKiIilWvfanNb1s1f/z5rOgAS7oeYztblEpEqR8VVREQqx64lMO9ZSPn5t4ENWg40VwmIamdpNBGpmlRcRUSk4hgGpM01j7DumG/ObHZocyP0Gg8Rza3NJyJVmoqriIicP8OArT/A/Odgz3JzZveG9reYGweENbY0nohUDyquIiJy7txu2PSlWVjTfzVnXn5w0WDoeTeENrA2n4hUKyquIiJSfq5iWP+JuUrA4S3mzDsQuvwNuo+BoAhr84lItaTiKiIiZVdcCGvfgwUvwNEd5sw3FLrdBV3vgoAwS+OJSPWm4ioiImdXdNzc4WrhRMjaa84C6kD30dD5b+AXamk8EakZVFxFROT0CrJhxZuwaBLkHjRnQZHm+asdh4BPoKXxRKRmUXEVEZGTHT8Gy16HJVPg+FFzFtoQet0D7W8Fbz9L44lIzaTiKiIiv8s9DIsnw7I3oDDbnIXFm5sGtL0JHN7W5hORGk3FVUREIGs/LHoFVk6HojxzFtHSLKytrgW7w9p8IiKouIqI1GxHd8LCl2D1THAVmrPoDpD4ADS9DOx2a/OJiPyBiquISE10OAUWvAjr3gd3sTmL6Qa9H4D4i8FmszafiMgpqLiKiNQkBzaYmwZs+AwMtzlr3Mc8wtqopwqriHg0FVcRkZpg7yqzsG7++vdZ08sg8X5o0Mm6XCIi5aDiKiJSne1cDPOfg5SffxvYoOXV5kVXUW0tjSYiUl4qriIi1Y1hwPY5MO852LnAnNkc0OZGSBgP4c0sjScicq5UXEVEqgvDgK0/wLxnYe8Kc2b3hg5/hZ7jICzO0ngiIudLxVVEpKpzu2DTlzDveTjwqznz8jO3ZO0xFkIbWBpPRKSiqLiKiFRVrmJY/7F50dXhrebMJwg6D4PuYyAowtp8IiIVTMVVRKSqKS6Ate+Z67Ae3WHO/EKh60joeicEhFkaT0Sksqi4iohUFUXHYdXb5k5XWXvNWUAd8+hq57+BX4i1+UREKpmKq4iIpyvIhuXTYPEkyD1kzoKjoMfd0HEw+ARam09E5AJRcRUR8VTHj8LS12HJFMg/Zs5qNTRXCGj/V/D2szKdiMgFp+IqIuJpcg7Bksmw7H9QmG3O6jjNTQPa3AgOb2vziYhYRMVVRMRTZO2DRa/AiulQfNycRbSCxPug5TVgd1gaT0TEaiquIiJWO7oTFk6E1e+Aq9CcRXeAxAeh6QCw2y2NJyLiKVRcRUSscjgFFrwAa98Hw2XOGnaHxAcgvi/YbNbmExHxMCquIiIXWvp6c9OADZ8BhjlrnGQW1tielkYTEfFkKq4iIhfK3pXmtqxbvvl91vQySLwfGnSyLpeIyB+43AbL0jI4mJ1PRLAfXeLCcNg94xUgFVcRkcq2cxHMew5Sf/ltYINW15irBES2sTKZiEgp36/fz4SvNrI/M79kFhXqx2NXtWRA6ygLk5lUXEVEKoNhwPZks7DuXGjObA5oexP0Gg/hTa3NJyLyJ9+v38/Id1adOIGpRHpmPiPfWcXUWy+yvLyquIqIVCTDgK3fw7xnzVMDAOze0OFW6HkPhMVZm09E5BRcboMJX208qbSCeSa+DZjw1UYuaRlp6WkDKq4iIhXB7YJNX5rnsB741Zx5+UHHodBjLITWtzafiMgZLEvLKHV6wJ8ZwP7MfJalZdA9vs6FC/YnKq4iIufDVQS/fmyuEnBkmznzCYLOf4PuoyEowtp8IiJlsPtoXpludzD79OX2QlBxFRE5F8UFsOZdWPAiHNtpzvxCoetI6HonBIRZm09EpAyO5RUyY9EO3pi3vUy3jwj2q+REZ6biKiJSHoV5sOptWPgSZO8zZwF1occY6DQM/EKszSciUgaHsguYtiCNmYt3kFtoboDisNtwuU91lqt5jmtkqLk0lpVUXEVEyqIgG5b/DxZNgrzD5iw4yrzg6qLB4BNgbT4RkTLYd+w4r8/bznvLdlFQ7AageWQwo5OcOGw2Rr+7CqDURVonLsV67KqWlq/nquIqInImx4/C0tdgyVTIP2bOajWEXvdC+7+Cl6+l8UREymLnkVymzknlk1V7KHKZtbRdTC3GJDm5uHkE9t8K6VT7RSet4xqpdVxFRDxcziFYMhmW/Q8Ks81ZnSbmpgFtbgCHt7X5RETKYOuBbKYkp/Dl2n2cOAuga1wYY/s2oaezDjZb6SOoA1pHcUnLSO2cJSJSJWTtg4Uvw8oZUHzcnNVrbRbWlleD3WFpPBGRsvh1TyaTkrfxw4YDJbPeTcMZ09dJ59gzn6fqsNssXfLqTFRcRUQAju6ABRNhzSxwFZqz6Iug94PQdADYPONog4jImSzfkcGk2SnM3XqoZDagVSSjk5y0aRBqYbKKoeIqIjXb4W0w/wVY9wEY5pW1NOoJifdD4yQVVhHxeIZhsCDlMJNmp7A0LQMAuw0GtotmVJKTpvWCLU5YcVRcRaRmSl9vbhqw4TNKrp+N7wsJ90NsT0ujiYiUhdtt8Mvmg0xKTmHt7mMAeDtsXH9RA+7qHU9s3UBrA1YCFVcRqVn2rIT5z8GWb3+fNbvcLKwNOlqXS0SkjFxug29+3c+U5BQ2p5sXj/p62bm5S0NGJDYmupa/xQkrzzkV19TUVCZOnMimTZsAaNmyJffccw/x8fEVGk5EpMLsWGgW1tTZvw1s0Opa86KryNaWRhMRKYsil5vPVu/l1TmpbD+cC0Cgj4PbuscyrFcc4cHVf3m+chfXH374gYEDB9K+fXt69jRfTlu4cCGtWrXiq6++4pJLLqnwkCIi58QwzKI67znYtcic2RzQ9i+QMB7qNrE2n4hIGeQXufhoxW5enbudvcfM1U5C/b0Z2jOWIT1iqRXgY3HCC8dmGMap9/Y6jQ4dOtC/f3+eeuqpUvOHHnqIH3/8kVWrVlVowPOVlZVFaGgomZmZhIRoK0aRGsEwYMt3MO9Z2Pfb9ySHj7lhQK9xUDvWynQiImWSW1DMrKU7eWN+GoeyCwCoG+TL8IQ4/tqtEUG+1eeMz7L2NXt573jTpk0MGzbspPkdd9zBxo0by3VfLpeLRx55hLi4OPz9/YmPj+ff//43f+zShmHw6KOPEhUVhb+/P/369WPbtm3ljS0iNYHbBes/hVd7wfs3m6XVyx+6jYJ71sJVE1VaRcTjZR4v4uVfttHz6dn899vNHMouIDrUjwkDW7Hg70nc2Tu+WpXW8ij3Vx0eHs6aNWto0qT0S2xr1qwhIiKiXPf19NNPM3XqVN566y1atWrFihUrGDp0KKGhodx9990APPPMM7z88su89dZbxMXF8cgjj9C/f382btyIn59feeOLSHXkKoJfPzKXtTry2y+2PkHQZTh0Gw1B4dbmExEpgyM5BUxbkMbMxTvJLigGILZOACP7xHNthwb4eJX7eGO1U+7iOnz4cEaMGMH27dvp0aMHYJ7j+vTTTzN+/Phy3deiRYu4+uqrueKKKwCIjY3lvffeY9myZYB5tHXixIn885//5Oqrrwbg7bffpl69enz++ecMGjSovPFFpDopLjA3DFjwIhzbZc78akG3kdBlBASceXcYERFPkJ6Zz+vztvPusp3kF7kBaFoviNFJTq5oE4WXQ4X1hHIX10ceeYTg4GCef/55Hn74YQCio6N5/PHHS46SllWPHj14/fXX2bp1K02bNmXt2rUsWLCAF154AYC0tDTS09Pp169fyeeEhobStWtXFi9efMriWlBQQEFBQcn7WVlZ5f0SRcTTFebBqrdg4UuQvd+cBYZD9zHQeRj4Vp/FtkWk+tp1JI+pc1P5ZOUeCl1mYW3bIJTRSU4uaVEPu10boPxZuYurzWbj3nvv5d577yU721w7LDj43H5IPPTQQ2RlZdG8eXMcDgcul4v//Oc//PWvfwUgPT0dgHr16pX6vHr16pV87M+efPJJJkyYcE55RMTD5WfB8v/B4smQd9icBUdDz3vgotvBJ8DafCIiZZByMJspyal8sXYfLrd5XU+X2DBG93WS2KQuNu3Yd1rndWbvuRbWEz788ENmzZrFu+++S6tWrVizZg3jxo0jOjqawYMHn9N9Pvzww6VOWcjKyiImJua8coqIxfIyYOlrsHQq5Geas1qNoNe90P4W8Kr+axeKSNW3fm8mU+ak8N36dE5ch57QpC5jkpx0bVzH2nBVRJmK60UXXcQvv/xC7dq16dChwxl/EyjPclgPPPAADz30UMlL/m3atGHnzp08+eSTDB48mMjISAAOHDhAVFRUyecdOHCA9u3bn/I+fX198fXVDzGRaiHnECyeZB5lLcwxZ3WaQOL90PoGcNTMq2pFpGpZufMok2ZvI3nLoZLZpS3rMTrJSbuYWtYFq4LK9F3/6quvLimD11xzTYU9eF5eHnZ76ROOHQ4Hbrd5nkdcXByRkZH88ssvJUU1KyuLpUuXMnLkyArLISIeJnMvLHoZVr4FxeZi29RrbRbWFgPB7rA2n4jIWRiGweLUI7wyO4XF248AYLfBlW2jGZUUT/NIrS1/LspUXB977LFT/v/5uuqqq/jPf/5Dw4YNadWqFatXr+aFF17gjjvuAMzzaceNG8cTTzxBkyZNSpbDio6OrtACLSIeIiMNFk6E1bPAXWTO6neExAeg6QDQeV8i4uEMw2D25oNMSk5h9a5jAHjZbVx3UX1G9nESVzfQ2oBVnKWvs73yyis88sgjjBo1ioMHDxIdHc2dd97Jo48+WnKbBx98kNzcXEaMGMGxY8fo1asX33//vdZwFalODm2FBS/Aug/BcJmzRr3MI6yN+6iwiojHc7kNvl+fzqTkFDbtN1c08vGyc3PnGEb0jqd+LX+LE1YPZdrytXbt2mW+wi0jI+O8Q1Ukbfkq4sHSf4X5z8OGz4HfvhXFX2wW1kY9rEwmIlImRS43X67Zx+Q5KWw/lAtAgI+D27o1YlhCHBHBOtBWFmXta2U64jpx4sSS/z9y5AhPPPEE/fv3p3v37gAsXryYH374gUceeeT8UotIzbBnJcx7FrZ+9/us2RWQeJ95aoCIiIfLL3Lx8co9vDo3lT1HzXPxQ/y8GNIzjqE9Yqkd6GNxwuqpTEdc/+j6668nKSmJMWPGlJpPmjSJn3/+mc8//7wi8503HXEV8SA7FpqFdXvybwMbtL4OEu6Deq0sjSYiUhZ5hcW8u3QXr8/bzsFsc8OjOoE+DEuI47ZujQj287Y4YdVU1r5W7uIaFBTEmjVrcDqdpeYpKSm0b9+enJycc0tcSVRcRSxmGJD6C8x7DnYtNmc2B7QbZK7DWreJtflERMogK7+ImYt3Mm1BGhm5hQBEhvhxZ+/GDOrcEH8frXZyPir0VIE/qlOnDl988QX33XdfqfkXX3xBnTpaPFdEfuN2m6cCzHsW9q02Zw4f6HAr9BwHtRtZGk9EpCwycgt5c0Eaby3eQXZ+MQANwwIY2See6y6qj6+XCuuFVO7iOmHCBP72t78xZ84cunbtCsDSpUv5/vvveeONNyo8oIhUMW4XbPwc5j0PBzeYMy9/6HQH9BgDIdGWxhMRKYsDWfm8MW87s5bu4niRudqJMyKI0UnxXNU2Gi+H/Sz3IJWh3MV1yJAhtGjRgpdffplPP/0UgBYtWrBgwYKSIisiNZCrCH79yFwl4EiKOfMJhi7DodsoCAq3Np+ISBnszsjjtXmpfLh8D4Uuc0OkVtEhjO3r5NKWkdjtWp7PSuU+x7Wq0TmuIpWsKB/WzDI3Dji2y5z51TLLatcR4F/bynQiImWSeiiHKcmpfLFmL8Vusxp1bFSbMX2d9GkaXuZlQeXcVNo5rgCpqalMnz6d7du3M3HiRCIiIvjuu+9KdsASkRqgMNfcknXRy5C935wFhkP3MdB5GPgGW5tPRKQMNu7LYvKcFL79dT8nDuX1ctZldJKTbo3DVFg9TLmL69y5c7nsssvo2bMn8+bN44knniAiIoK1a9cybdo0Pv7448rIKSKeIj8Llr8Bi6dA3mFzFlIfet4DF90O3todRkQ83+pdR5mcnMLPmw6WzPq1iGB0kpMODfVKkacqd3F96KGHeOKJJxg/fjzBwb8fUenbty+TJk2q0HAi4kHyMmDpq+ZbfqY5q9UIEsZDu5vBy9fafCIiZ2EYBku2ZzApeRsLU44A5o7Sl7eJYnQfJy2jdUqhpyt3cf3111959913T5pHRERw+PDhCgklIh4k5yAsngTLp0Hhb+s0120KCfdD6+vBcU5nHImIXDCGYTBnyyEmJaewcudRALzsNq7pUJ+RfeKJDw+yOKGUVbl/4tSqVYv9+/cTFxdXar569Wrq169fYcFExGKZe2Dhy7DqLSjON2f12kDi/dDiKrBr7UIR8Wxut8EPG9KZlJzChn1ZAPh42bmpUwPuTIwnJizA4oRSXuUuroMGDeLvf/87H330ETabDbfbzcKFC7n//vu5/fbbKyOjiFxIGWmw4EVY8y64i8xZ/U6Q+AA07W++riYi4sGKXW6+WrePKcmpbDtovlLk7+3gr10bMjyxMfVC/CxOKOeq3MX1v//9L6NHjyYmJgaXy0XLli1xuVzccsst/POf/6yMjCJyIRzaAvNfMNdiNczFtolNMI+wxvVWYRURj1dQ7OLTVXuZOieVXRl5AAT7eTGkRyxDe8YRFuhjcUI5X+e8juuuXbtYv349OTk5dOjQgSZNPHO/ca3jKnIW+9eZmwZs/AL47duBs595Dmuj7pZGExEpi+OFLt5btovX520nPcs8tSks0IdhveK4rXsjQvy8LU4oZ1Op67gCNGzYkIYNG57rp4uI1fasgHnPwtbvf581vxIS7oP6F1mXS0SkjLLzi5i5ZCfT5qdxJLcQgHohvgxPaMwtXRsS4KOLR6ubcv+JGobBxx9/THJyMgcPHsTtdpf6+IltYEXEAxkG7FxoFtbtc8yZzQ6trjULaz1tICIinu9obiHTF+1gxsI0svKLAWhQ25+RfeK5oWMDfL108Wh1Ve7iOm7cOF577TWSkpKoV6+edpQQqQoMA1J+MQvr7iXmzO4FbQdBr3uhrtPafCIiZXAwO5//zU/jnSU7ySs0z8WPDw9kVB8nA9tH4+2wW5xQKlu5i+vMmTP59NNPufzyyysjj4hUJLcbtnxrFtb9a8yZwwc63GbudFW7kaXxRETKYu+x47w2N5X3l++msNh8pbdFVAhjkpwMaB2Jw66DaDVFuYtraGgojRs3rowsIlJR3C7Y8Jl50dXBjebMOwA63QHdx0BIlLX5RETKIO1wLlPnpPDpqr0Uu82LRzs0rMXYvk6SmkXoVd8aqNzF9fHHH2fChAm8+eab+PtrT3IRj+IqgnUfmoU1I9Wc+QRD1xHQbRQE1rU2n4hIGWxOz2JycirfrNvHb32VHvF1GJPkpHt8HRXWGqzcxfWmm27ivffeIyIigtjYWLy9Sy8xsWrVqgoLJyJlVJQPa96BBS9B5i5z5l/bLKtdhpv/LyLi4dbuPsak5BR+2nigZNa3eQSjk5x0bKTvY3IOxXXw4MGsXLmSW2+9VRdniVitMBdWzjC3Zs1JN2eB4dBjrHlagG+wpfFERMpi6fYjTEpOYf62w4C538llrSMZ1cdJ6/qhFqcTT1Lu4vrNN9/www8/0KtXr8rIIyJlkZ8Jy96AJVMg74g5C6kPPcfBRbeBt07jERHPZhgG87YdZtLsbSzfcRQAh93G1e2jGdUnHmeEfvGWk5W7uMbExGgHKhGr5GXAkqmw9DUoyDRntWOh13hodzN4aTtDEfFsbrfBT5sOMGl2Cr/uNb+P+Tjs3NCpAXclxtOwToDFCcWTlbu4Pv/88zz44IO8+uqrxMbGVkIkETlJ9gFYPAmWT4OiXHNWt5m5aUDr68Gh3WFExLMVu9x88+t+JiensPVADgB+3nZu6dKIEYmNiQz1szihVAXl/ml36623kpeXR3x8PAEBASddnJWRkVFh4URqvMw95vmrq96CYnP/bSLbQOID0PwqsGuxbRHxbIXFbj5bvYcpc1LZeSQPgGBfL27r3og7esVRN8jX4oRSlZS7uE6cOLESYohIKRnbYcGLsOY9cBeZs/qdoPeD0ORS88oFEREPll/k4oPlu3ltbir7Ms1fvGsFeDOsZxy394gl1N/7LPcgcrJzWlVARCrJwc2w4AX49SMwzN1hiE2AxPshrrcKq4h4vJyCYt5ZspP/zU/jcE4BAOHBvoxIaMwtXRsS6KtTm+TcndPfHpfLxeeff86mTZsAaNWqFQMHDsThcFRoOJEaY/86mP8cbPwS+G21beclZmFt2M3SaCIiZXEsr5AZi3YwfeEOMo+brxTVr+XPXb0bc2OnGPy81RHk/JW7uKakpHD55Zezd+9emjVrBsCTTz5JTEwM33zzDfHx8RUeUqTa2r0c5j0L2374fdb8SrOwRnewLpeISBkdyi5g2oI0Zi7eQW6hC4C4uoGM6hPPNR3q4+3QufhScWyGYRjl+YTLL78cwzCYNWsWYWFhABw5coRbb70Vu93ON998UylBz1VWVhahoaFkZmZqGS/xDIYBOxaYhTVtrjmz2aHVdeYqAfVaWptPRKQM9mce57W523lv2S4Kis1Tm5pHBjM6ycnlbaJw2HVqk5RdWftauY+4zp07lyVLlpSUVoA6derw1FNP0bNnz3NLK1ITGAak/AzznoPdS8yZ3QvaDTLXYa2jVytExPPtPJLL1DmpfLJqD0Uu89hXu5hajElycnHzCOwqrFKJyl1cfX19yc7OPmmek5ODj48WPxc5idsNW74xj7DuX2vOHL7mDlc974FaDa3NJyJSBlsPZDMlOYUv1+7D/dtrtV3jwhjbtwk9nXW0BbxcEOUurldeeSUjRoxg2rRpdOnSBYClS5dy1113MXDgwAoPKFJluV2w4TPzCOsh80JGvAOg0x3QYywER1qbT0SkDNbvzWTS7BS+35BeMuvdNJwxfZ10jg07w2eKVLxyF9eXX36ZwYMH071795LNB4qLixk4cCAvvfRShQcUqXJcRbDuA5j/AmSkmjPfEOgyArqNgsA61uYTESmD5TsymDQ7hblbD5XMBrSKZHSSkzYNQi1MJjVZuYqrYRhkZWXx/vvvs3fv3pLlsFq0aIHT6ayUgCJVRlE+rJ4JC1+CzN3mzL82dBsNXYaDfy1L44mInI1hGCxIOcyk2SksTTN3wrTbYGC7aEYlOWlaL9jihFLTlbu4Op1ONmzYQJMmTVRWRQAKc2HFdFj0CuT89lJaYIR5OkCnO8A3yNp8IiJnYRgGP286yKTkFNbuPgaAt8PG9Rc14K7e8cTWDbQ2oMhvylVc7XY7TZo04ciRIzRp0qSyMolUDfmZsOx1WDwFjptHJghpAL3GQYdbwdvf0ngiImfjcht88+t+piSnsDndvPDa18vOzV0aMiKxMdG19H1MPEu5z3F96qmneOCBB5g6dSqtW7eujEwini33CCydCktfh4JMc1Y7DhLGQ9tB4KXVNUTEsxW53Hy2ei+vzkll++FcAAJ9HNzWPZZhveIID/a1OKHIqZV7A4LatWuTl5dHcXExPj4++PuX/m0sIyOjQgOeL21AIBUm+wAsfgWWvwlF5jd6wpubmwa0ug4c2n9bRDxbfpGLj1bs5tW529l77DgAof7eDO0Zy5AesdQK0C/eYo1K24Bg4sSJ55NLpOo5thsWvQwr3wJXgTmLbAuJD5jbs9q1naGIeLbcgmLeXbqL1+dv51C2+X2sbpAvwxPi+Gu3RgT56hdvqRrK/Td18ODBlZFDxPNkbIcFL8Ka98BdZM4adIbEB6HJJaDFtkXEw2UeL+KtRTt4c2Eax/LM72PRoX7c2Tuev3SOwc/bYXFCkfI5p1+xXC4Xn332WclyWC1btuTqq6/Gy0u/sUk1cHAzzH8e1n8Mhrn/NrEJ5hHWuEQVVhHxeEdyCpi2II2Zi3eSXVAMQGydAEb2iefaDg3w8dIrRVI1lbtpbtiwgYEDB5Kenk6zZs0AePrppwkPD+err77SBVtSde1fa+5ytenL32dNLoWE+6FhV+tyiYiUUXpmPq/P2867y3aSX2T+4t20XhCjk5xc0SYKL4cKq1Rt5S6uf/vb32jVqhUrVqygdu3aABw9epQhQ4YwYsQIFi1aVOEhRSrV7mUw71nY9uPvsxZXmYU1ur1lsUREymrXkTymzk3lk5V7KHSZhbVtg1BGJzm5pEU97Ha9UiTVQ7mL65o1a0qVVjBXGvjPf/5D586dKzScSKUxDNgx3yysafPMmc0Ora83VwmIaGFtPhGRMkg5mM2U5FS+WLsPl9tcJKhLbBij+zpJbFIXm05tkmqm3MW1adOmHDhwgFatWpWaHzx4UDtpieczDEj52Sysu5eaM7sXtLsZet0LdeKtzSciUgbr92YyZU4K361P58SilglN6jImyUnXxnWsDSdSicpdXJ988knuvvtuHn/8cbp16wbAkiVL+Ne//sXTTz9NVlZWyW21bqp4DLcbNn8N858zz2UFcPjCRbdDz7uhVkNr84mIlMHKnUeZNHsbyVsOlcwuaVmPMUlO2sXUsi6YyAVS7g0I7H9Ys/LESxAn7uKP79tsNlwuV0XlPGfagKCGcxXDhs/MwnposznzDoROQ6HHWAiOtDafiMhZGIbB4tQjvDI7hcXbjwBgt8GVbaMZlRRP80j9bJOqr9I2IEhOTj6vYCIXRHEhrPsAFrxgrscK4BsCXe+EriMhUC+liYhnMwyD2ZsPMik5hdW7jgHgZbdx3UX1GdnHSVzdQGsDilig3MW1d+/elZFDpGIU5cPqmbDwJcjcbc78w6D7KOg8HPxrWRpPRORsXG6D79enMzk5hY37zdPvfLzsDOocw4jExjSoHWBxQhHraMcAqR4KcmDldFj0CuQcMGdB9czTAToOBd8ga/OJiJxFkcvNl2v2MXlOCtsP5QIQ4OPgtm6NGJYQR0Swn8UJRayn4ipV2/FjsPwNWDwFjmeYs5AG0GscdLgVvP2tTCciclYFxS4+XrmHqXNS2XP0OAAhfl4M6RnH0B6x1A70sTihiOdQcZWqKfcILJkCy16Hgt9WsghrDL3GQ9u/gJe+0YuIZ8srLObdpbt4Y/52DmQVAFAn0IdhCXHc1q0RwX7eFicU8TzlKq6GYbB7924iIiLw89NLFmKB7HTzdIAVb0JRnjkLb2FuGtDqWnDodzER8WxZ+UXMXLyTaQvSyMgtBCAyxI87ezdmUOeG+Ps4LE4o4rnKXVydTicbNmygSZMmlZVJ5GTHdpsXXK16G1zmkQmi2kHiA9DsCrBr/20R8WwZuYVMX5jGjEU7yM4vBqBhWAAj+8Rz3UX18fVSYRU5m3IVV7vdTpMmTThy5IiKq1wYR1JhwYuw9j1wm9/oadAFej8Izn6g7QxFxMMdyMrnjXnbmbV0F8eLzPXNnRFBjE6K56q20Xg59Iu3SFmV+3XVp556igceeICpU6fSunXrysgkAgc3wfznYf0nYLjNWVyieYQ1NkGFVUQ83u6MPF6bl8qHy/dQ6DK/j7WKDmFsXyeXtozEbtf3MZHyKvfOWbVr1yYvL4/i4mJ8fHzw9y991XZGRkaFBjxf2jmritm3xtzlatNXv8+aXAoJ90PDrpbFEhEpq9RDOUydk8rnq/dS7DZ/xHZsVJsxfZ30aRpessukiPyu0nbOmjhx4vnkEjm1XUth3rOQ8tPvsxYDzYuuottbFktEpKw27sti8pwUvv11PycOCfVy1mV0kpNujcNUWEUqQLmL6+DBgysjh9REhgFp88zCumO+ObPZofUNkDAeIlpYm09EpAxW7zrK5OQUft50sGTWr0UEo5OcdGhY28JkItXPOa0d5HK5+Pzzz9m0aRMArVq1YuDAgTgcuiJSysAwYNtPZmHds8yc2b2h/c3QcxzUibc0nojI2RiGwZLtGUxOTmFBymHAPPX+8jZRjO7jpGW0Tk0TqQzlLq4pKSlcfvnl7N27l2bNmgHw5JNPEhMTwzfffEN8vEqHnIbbDZu/Ngtr+jpz5vCFjoOhx91QK8bafCIiZ2EYBnO2HGJScgordx4FwMtu45oO9RnZJ574cG0vLVKZyn1x1uWXX45hGMyaNYuwsDAAjhw5wq233ordbuebb76plKDnShdneQBXMWz41Fwl4NBmc+YdCJ3vgO5jIDjS2nwiImfhdhv8sCGdSckpbNhn7tbn42Xnpk4NuDMxnpiwAIsTilRtlXZx1ty5c1myZElJaQWoU6cOTz31FD179jy3tFI9FRfCuvdh/gtwNM2c+YZC1xHQdSQE1rE2n4jIWRS73Hy1bh9TklPZdjAHAH9vB3/t2pDhiY2pF6JdJEUupHIXV19fX7Kzs0+a5+Tk4OOj/eEFKDoOq2aaO11l7TFn/mHQfTR0GQ5+odbmExE5i4JiF5+u2svUOansyjC3lw7282JIj1iG9owjLFA/70SsUO7ieuWVVzJixAimTZtGly5dAFi6dCl33XUXAwcOrPCAUoUU5MCKN2HRK5D729W1QfXM81c7DQWfQGvziYicxfFCF+8t28Xr87aTnpUPQFigD8N6xXFb90aE+HlbnFCkZit3cX355ZcZPHgw3bt3x9vb/AdcXFzMwIEDeemllyo8oFQBx4/BsjdgyWQ4bl6sQGgM9LwHOtwG3nopTUQ8W3Z+ETOX7GTa/DSO5BYCUC/El+EJjbmla0MCfM5pER4RqWDl/pdYq1YtvvjiC7Zt28bmzeaFNi1atMDpdFZ4OPFwuYdhyRSztBaYFysQ1tjcNKDNTeCll9JExLMdzS1k+qIdzFiYRlZ+MQANavszsk88N3RsgK+XlnkU8STn/CtkkyZNaNKkSUVmkaoiO908HWDFm1BknvtFeAtIvB9aXgMOHZkQEc92MDuf/81P450lO8krdAEQHx7IqD5OBraPxtthtzihiJxKmRrG+PHjy3yHL7zwwjmHEQ93bJd5wdWqmeAqMGdR7SHxAWh2Odj1jV5EPNveY8d5bW4q7y/fTWGxG4AWUSGMSXIyoHUkDru2ZRXxZGUqrqtXry7TnWkf5mrqSCoseAHWvg9u86U0YrpC4oPgvNjcLkZExIOlHc5l6pwUPl21l2K3uXx5h4a1GNvXSVKzCP38EqkiylRck5OTKzuHeKIDG81NAzZ8CoZ5ZIK43uYR1theKqwi4vG2pGczOTmFr9ft47e+SvfGdRjb10n3+DoqrCJVjE5GlJPtWw3znjO3Zz2hSX/zHNaYLtblEhEpo7W7jzEpOYWfNh4omfVtHsHoJCcdG9W2MJmInI9zKq4rVqzgww8/ZNeuXRQWFpb62KefflohwcQCu5bAvGch5effBjZoOdBcJSCqnaXRRETKYun2I0xKTmH+tsOA+cLQZa0jGdXHSev62vxEpKord3F9//33uf322+nfvz8//vgjl156KVu3buXAgQNce+21lZFRKpNhQNpc8wjrjvnmzGaHNjdCr/EQ0dzafCIiZ2EYBvO2HWby7BSW7cgAwGG3cXW7aEYlxeOMCLY4oYhUlHIX1//+97+8+OKLjB49muDgYF566SXi4uK48847iYqKqoyMUhkMA7b9aB5h3bPcnNm9of0t0GucuR6riIgHc7sNftp0gEmzU/h1byYAPg47N3RqwF2J8TSsE2BxQhGpaOUurqmpqVxxxRUA+Pj4kJubi81m495776Vv375MmDChwkNKBXK7YfNXZmFN/9WcefnBRYOh590Q2sDafCIiZ+FyG3y9bh+Tk1PYeiAHAD9vO7d0acSIxMZEhmq3PpHqqtzFtXbt2mRnZwNQv3591q9fT5s2bTh27Bh5eXkVHlAqiKsY1n9irhJweIs58w6EzsOg+xgIrmdtPhGRsygsdvPZ6j1MnZPKjiPmz5tgXy9u696IO3rFUTfI1+KEIlLZyl1cExMT+emnn2jTpg033ngj99xzD7Nnz+ann37i4osvroyMcj6KC2Hte+Y6rEd3mDPfUOh6J3QbCQFhlsYTETmb/CIXHyzfzWtzU9mXmQ9ArQBvhvWM4/YesYT6e1ucUEQulDIX1/Xr19O6dWsmTZpEfr75jeP//u//8Pb2ZtGiRVx//fX885//rLSgUk5Fx80drhZOhKy95iygDnQfDZ3/Bn66ulZEPFtOQTGzluzkjflpHM4xd+sLD/ZlREJjbunakEBfregoUtPYDMMwynJDu91O586d+dvf/sagQYMIDq4aV2lmZWURGhpKZmYmISEhVsepfAXZsOJNWDQJcg+as6BI8/zVjkPAJ9DSeCIiZ3Msr5AZi3YwfeEOMo8XAVC/lj939W7MjZ1i8PN2WJxQRCpaWftamYvr/PnzmT59Oh9//DFut5vrr7+ev/3tbyQkJFRY6MpQY4rr8WOw7HVYMgWOHzVnoQ2h1z3Q/lbw1sUKIuLZDmUXMG1BGjMX7yC30AVAXN1ARvWJ55oO9fF22C1OKCKVpcKL6wm5ubl8+OGHzJgxg/nz5+N0Ohk2bBiDBw8mMjLyvINXtGpfXHMPw+LJsPx/UJBlzsLizU0D2t4EDp37JSKebX/mcV6bu533lu2ioNjcXrp5ZDCjk5xc3iYKh13bsopUd5VWXP8oJSWF6dOnM3PmTNLT0xkwYABffvnlud5dpai2xTVrPyx6BVZOh6LfVnOIaGkW1lbXgl0vpYmIZ9t5JJepc1L5ZNUeilzmj6J2MbUYk+Tk4uYR2FVYRWqMC1JcwTwCO2vWLB5++GGOHTuGy+U6n7urcNWuuB7dCQtfgtUzwfXbdrtR7SHxAWh2Odj1UpqIeLatB7KZkpzCl2v34f7tJ1DXuDDG9m1CT2cdbDYVVpGapqx97ZwvyZw3bx5vvvkmn3zyCXa7nZtuuolhw4ad693J2RxOgQUvwrr3wV1szmK6Qe8HIP5ic0NuEREPtn5vJpNmp/D9hvSSWe+m4Yzp66RzrJbmE5GzK1dx3bdvHzNmzGDGjBmkpKTQo0cPXn75ZW666SYCA3W1eqU4sMHcNGDDZ2CY537RuI95hLVRTxVWEfF4K3Zk8MrsFOZuPVQyG9AqktFJTto00NJ8IlJ2ZS6ul112GT///DN169bl9ttv54477qBZs2bn9eCxsbHs3LnzpPmoUaOYPHky+fn53Hfffbz//vsUFBTQv39/pkyZQr16NWCXp72rzMK6+evfZ00HQML9ENPZulwiImVgGAYLUg4zaXYKS9MyALDbYGC7aEYlOWlar2osqSginqXMxdXb25uPP/6YK6+8EoejYi78Wb58ealzYtevX88ll1zCjTfeCMC9997LN998w0cffURoaChjxozhuuuuY+HChRXy+B5p52KY/xyk/PzbwAYtrzYvuopqa2k0EZGzMQyDnzcdZFJyCmt3HwPA22Hj+osacFfveGLr6tU5ETl3531xVkUaN24cX3/9Ndu2bSMrK4vw8HDeffddbrjhBgA2b95MixYtWLx4Md26dSvTfVaJi7MMA7bPgXnPwc4F5szmgDY3QsJ4CD+/I9siIpXN5Tb49tf9TE5OYXN6NgC+XnZu7tKQEYmNia7lb3FCEfFklX5xVkUrLCzknXfeYfz48dhsNlauXElRURH9+vUruU3z5s1p2LDhGYtrQUEBBQUFJe9nZWVVevZzZhiw9QeY9yzsXWHO7N7Q4a/QcxyExVkaT0TkbIpcbj5bvZdX56Sy/XAuAIE+Dm7rHsuwXnGEB/tanFBEqhOPKa6ff/45x44dY8iQIQCkp6fj4+NDrVq1St2uXr16pKenn3wHv3nyySeZMGFCJSatAG43bPrSPMJ64Fdz5uVnbsnaYyyENrA0nojI2eQXufhoxW5enbudvceOAxDq783QnrEM6RFLrQAfixOKSHXkMcV12rRpXHbZZURHR5/X/Tz88MOMHz++5P2srCxiYmLON17ZuF2wcxHkHICgetCoR+mNAFzFsP5jmP8CHN5iznyCoPMw6D4GgiIuTE4RkXOUW1DMu0t38fr87RzKNl/dqhvkw/CExvy1WyOCfD3mx4qIVEMe8R1m586d/Pzzz3z66acls8jISAoLCzl27Fipo64HDhw449ayvr6++Ppa8NLUxi/h+79D1r7fZyHRMOBpaNof1r5nrsN6dIf5Mb9Q6HqX+Rag9QtFxLNlHi/irUU7mL4wjaN5RQBEh/pxZ+94/tI5Bj9v7dYnIpXPI4rr9OnTiYiI4IorriiZdezYEW9vb3755Reuv/56ALZs2cKuXbvo3r27VVFPbeOX8OHtwJ+uc8vaDx/eBv5hcNxcDoaAOubR1c5/Az8PvVhMROQ3R3IKmLYgjZmLd5JdYG5+ElsngJF94rm2QwN8vLRbn4hcOJYXV7fbzfTp0xk8eDBeXr/HCQ0NZdiwYYwfP56wsDBCQkIYO3Ys3bt3L/OKAheE22Ueaf1zaYXfZ8czICgSet4DHQeDj5aDERHPlp6Zz+vztvPusp3kF5mbnzStF8ToJCdXtInCy6HCKiIXnuXF9eeff2bXrl3ccccdJ33sxRdfxG63c/3115fagMCj7FxU+vSA07lmCjgvrvw8IiLnYXdGHlPnpvLxij0UuszC2rZBKKOTnFzSoh52u3brExHrWF5cL730Uk63lKyfnx+TJ09m8uTJFzhVOeQcKNvtjh+t3BwiIuch5WA2U5JT+WLtPlxu83tyl9gwRvd1ktikLjZtLy0iHsDy4lrlBZVx+9my3k5E5ALasC+TyckpfLc+nRPHEBKa1GVMkpOujetYG05E5E9UXM9Xox7m6gFZ+zn1ea428+ONelzoZCIip7Vy51EmJ6cwe/PBktklLesxJslJu5ha1gUTETkDFdfzZXeYS159eDtgo3R5/e2ltQFPlV7PVUTEAoZhsDj1CK/MTmHx9iMA2G1wZdtoRiXF0zxSK52IiGdTca0ILQfCTW+fZh3Xp8yPi4hYxDAMZm8+yKTkFFbvOgaAl93GdRfVZ2QfJ3F1tdKJiFQNKq4VpeVAaH7FmXfOEhG5gFxug+/XpzM5OYWN+7MA8PGyM6hzDCMSG9OgdoDFCUVEykfFtSLZHRCXYHUKEanhilxuvlyzjylzUkg9lAtAgI+D27o1YlhCHBHBfhYnFBE5NyquIiLVREGxi49X7mHqnFT2HD0OQIifF0N6xjG0Ryy1A30sTigicn5UXEVEqri8wmLeXbqLN+Zv50BWAQB1An0YlhDHbd0aEeznbXFCEZGKoeIqIlJFZeUXMXPxTqYtSCMjtxCAyBA/7uzdmEGdG+Lvo3PsRaR6UXEVEaliMnILmb4wjRmLdpCdXwxAw7AARvaJ57qL6uPrpcIqItWTiquISBVxMCuf1+dtZ9bSXRwvcgHgjAhidFI8V7WNxsthtzihiEjlUnEVEfFwuzPyeG1eKh+u2ENhsRuAVtEhjO3r5NKWkdjtNosTiohcGCquIiIeKvVQDlPnpPL56r0Uu81d+To2qs2Yvk76NA3HZlNhFZGaRcVVRMTDbNqfxaTkFL79dT/Gb7tI93LWZXSSk26Nw1RYRaTGUnEVEfEQq3cdZXJyCj9vOlgy69cigtFJTjo0rG1hMhERz6DiKiJiIcMwWLI9g8nJKSxIOQyAzQaXt4lidB8nLaNDLE4oIuI5VFxFRCxgGAZzth5i0uwUVu48CoCX3cY1Heozsk888eFBFicUEfE8Kq4iIheQ223ww4Z0Js9JYf3eLAB8vOzc1KkBdybGExMWYHFCERHPpeIqInIBFLvcfLVuH1OSU9l2MAcAf28Hf+3akOGJjakX4mdxQhERz6fiKiJSiQqKXXy6ai9T56SyKyMPgGBfL4b0jGVozzjCAn0sTigiUnWouIqIVILjhS7eW7aL1+dtJz0rH4CwQB+G9Yrjtu6NCPHztjihiEjVo+IqIlKBsvOLmLlkJ9Pmp3EktxCAeiG+DE9ozC1dGxLgo2+7IiLnSt9BRUQqwNHcQqYv2sGMhWlk5RcD0KC2PyP7xHNDxwb4ejksTigiUvWpuIqInIeD2flMm5/GzCU7ySt0ARAfHsioPk4Gto/G22G3OKGISPWh4ioicg72HjvOa3NT+WD5bgqK3QC0iAphTJKTAa0jcdi1LauISEVTcRURKYe0w7lMnZPCp6v2Uuw2AOjQsBZj+zpJahaBzabCKiJSWVRcRUTKYEt6NpOTU/h63T5+66t0b1yHsX2ddI+vo8IqInIBqLiKiJzB2t3HmJScwk8bD5TM+jaPYHSSk46NaluYTESk5lFxFRE5haXbjzApOYX52w4DYLPBZa0jGdXHSev6oRanExGpmVRcRUR+YxgG87YdZvLsFJbtyADAYbdxdbtoRiXF44wItjihiEjNpuIqIjWe223w06YDTE5OYd2eTAB8HHZu6NSAuxLjaVgnwOKEIiICKq4iUoO53AZfr9vH5OQUth7IAcDP284tXRoxIrExkaF+FicUEZE/UnEVkRqnsNjNZ6v3MHVOKjuO5AEQ7OvFbd0bcUevOOoG+VqcUERETkXFVURqjPwiFx8s381rc1PZl5kPQK0Ab4b1jOP2HrGE+ntbnFBERM5ExVVEqr2cgmJmLdnJG/PTOJxTAEB4sC8jEhpzS9eGBPrqW6GISFWg79YiUm1l5hUxfVEa0xfuIPN4EQD1a/lzV+/G3NgpBj9vh8UJRUSkPFRcRaTaOZRdwLQFabyzZCc5BcUAxNUNZFSfeK7pUB9vh93ihCIici5UXEWk2tifeZzX5m7nvWW7KCh2A9A8MpjRSU4ubxOFw65tWUVEqjIVVxGp8nYeyWXqnFQ+WbWHIpcBQLuYWoxJcnJx8wjsKqwiItWCiquIVFlbD2QzJTmFL9fuw232VbrGhTG2bxN6Outgs6mwiohUJyquIlLlrN+byaTZKXy/Ib1k1rtpOGP6OukcG2ZhMhERqUwqriJSZazYkcErs1OYu/VQyWxAq0hGJzlp0yDUwmQiInIhqLiKiEczDIOFKUd4ZfY2lqZlAGC3wcB20YxKctK0XrDFCUVE5EJRcRURj2QYBj9vOsik5BTW7j4GgLfDxvUXNeCu3vHE1g20NqCIiFxwKq4i4lFcboNvf93P5OQUNqdnA+DrZefmLg0ZkdiY6Fr+FicUERGrqLiKiEcocrn5fPVeps5JZfvhXAACfRzc1j2WYb3iCA/2tTihiIhYTcVVRCyVX+TioxW7eXXudvYeOw5AqL83Q3vGMqRHLLUCfCxOKCIinkLFVUQskVtQzLtLd/H6/O0cyi4AoG6QD8MTGvPXbo0I8tW3JxERKU0/GUTkgso8XsTbi3bw5sI0juYVARAd6sedveP5S+cY/LwdFicUERFPpeIqIhfEkZwCpi1IY+binWQXFAMQWyeAkX3iubZDA3y87BYnFBERT6fiKiKVKj0zn9fnbefdZTvJL3ID0LReEKOTnFzRJgovhwqriIiUjYqriFSK3Rl5TJ2byscr9lDoMgtr2wahjE5yckmLetjtNosTiohIVaPiKiIVKuVgNlOSU/li7T5cbgOALrFhjO7rJLFJXWw2FVYRETk3Kq4iUiE27MtkcnIK361PxzD7KglN6jImyUnXxnWsDSciItWCiquInJeVO48yOTmF2ZsPlswuaVmPMUlO2sXUsi6YiIhUOyquIlJuhmGwOPUIr8xOYfH2IwDYbXBl22hGJcXTPDLE4oQiIlIdqbiKSJkZhsHszQeZlJzC6l3HAPCy27juovqM7OMkrm6gtQFFRKRaU3EVkbNyuQ2+X5/O5OQUNu7PAsDHy86gzjGMSGxMg9oBFicUEZGaQMVVRE6ryOXmyzX7mDInhdRDuQAE+Di4rVsjhiXEERHsZ3FCERGpSVRcReQkBcUuPl65h6lzUtlz9DgAIX5eDOkZx9AesdQO9LE4oYiI1EQqriJSIq+wmHeX7uKN+ds5kFUAQJ1AH4YlxHFbt0YE+3lbnFBERGoyFVcRISu/iJmLdzJtQRoZuYUARIb4cWfvxgzq3BB/H4fFCUVERFRcRWq0jNxCpi9MY8aiHWTnFwPQMCyAkX3iue6i+vh6qbCKiIjnUHEVqYEOZuXz+rztzFq6i+NFLgCcEUGMTornqrbReDnsFicUERE5mYqrSA2yOyOP1+al8uGKPRQWuwFoFR3C2L5OLm0Zid1uszihiIjI6am4itQAqYdymDonlc9X76XYbQDQsVFtxvR10qdpODabCquIiHg+FVeRamzT/iwmJafw7a/7Mcy+Si9nXUYnOenWOEyFVUREqhQVV5FqaPWuo0xOTuHnTQdLZv1aRDA6yUmHhrUtTCYiInLuVFxFqgnDMFiyPYPJySksSDkMgM0Gl7eJYnQfJy2jQyxOKCIicn5UXEWqOMMwmLP1EJNmp7By51EAHHYb13aoz8g+8cSHB1mcUEREpGKouIpUUW63wY8b05mUnML6vVkA+HjZualTA+5MjCcmLMDihCIiIhVLxVWkiil2uflq3T6mJKey7WAOAP7eDv7atSHDExtTL8TP4oQiIiKVQ8VVpIooKHbx6aq9TJ2Tyq6MPACCfb0Y0jOWoT3jCAv0sTihiIhI5VJxFfFwxwtdvL98F6/N3U56Vj4AYYE+DOsVx23dGxHi521xQhERkQtDxVXEQ2XnFzFzyU6mzU/jSG4hAPVCfBme0JhbujYkwEf/fEVEpGbRTz4RD3M0t5Dpi3YwY2EaWfnFADSo7c/IPvHc0LEBvl4OixOKiIhYQ8VVxEMczM5n2vw0Zi7ZSV6hC4D48EBG9XEysH003g67xQlFRESspeIqYrG9x47z2txUPli+m4JiNwAtokIYk+RkQOtIHHZtyyoiIgIqriKWSTucy9Q5KXy6ai/FbgOADg1rMbavk6RmEdhsKqwiIiJ/pOIqcoFtSc9mcnIKX6/bx299le6N6zC2r5Pu8XVUWEVERE5DxVXkAlm35xiTZqfw48YDJbO+zSMYneSkY6PaFiYTERGpGlRcRSrZ0u1HmJScwvxthwGw2eCy1pGM6uOkdf1Qi9OJiIhUHSquIpXAMAzmbTvM5NkpLNuRAYDDbuPqdtGMSorHGRFscUIREZGqR8VVpAK53QY/bTrA5OQU1u3JBMDHYeeGTg24KzGehnUCLE4oIiJSdam4ilQAl9vg63X7mJKcypYD2QD4edu5pUsjRiQ2JjLUz+KEIiIiVZ+Kq8h5KCx289nqPUydk8qOI3kABPt6cVv3RtzRK466Qb4WJxQREak+VFxFzkF+kYsPlu/mtbmp7MvMB6BWgDfDesZxe49YQv29LU4oIiJS/ai4ipRDTkExs5bs5I35aRzOKQAgPNiXEQmNuaVrQwJ99U9KRESksuinrEgZZOYVMX1RGtMX7iDzeBEA9Wv5c1fvxtzYKQY/b4fFCUVERKo/u9UB9u7dy6233kqdOnXw9/enTZs2rFixouTjhmHw6KOPEhUVhb+/P/369WPbtm0WJpaa5HBOAU99t5meT89m4s/byDxeRFzdQJ65oS1zHujDbd1jVVpFREQuEEuPuB49epSePXuSlJTEd999R3h4ONu2baN27d93EXrmmWd4+eWXeeutt4iLi+ORRx6hf//+bNy4ET8/XaktlWN/5nFem7ud95fvIr/IDUDzyGBGJzm5vE0UDru2ZRUREbnQbIZhGFY9+EMPPcTChQuZP3/+KT9uGAbR0dHcd9993H///QBkZmZSr149ZsyYwaBBg876GFlZWYSGhpKZmUlISEiF5pfqZ+eRXKbOSeWTVXsocpn/NNrF1GJMkpOLm0dgV2EVERGpcGXta5Yecf3yyy/p378/N954I3PnzqV+/fqMGjWK4cOHA5CWlkZ6ejr9+vUr+ZzQ0FC6du3K4sWLT1lcCwoKKCgoKHk/Kyur8r8QqfK2HchmcnIKX67dh/u3X+W6xoUxpq+TXs662GwqrCIiIlaztLhu376dqVOnMn78eP7xj3+wfPly7r77bnx8fBg8eDDp6ekA1KtXr9Tn1atXr+Rjf/bkk08yYcKESs8u1cP6vZlMmp3C9xt+//vUu2k4Y/o66RwbZmEyERER+TNLi6vb7aZTp07897//BaBDhw6sX7+eV199lcGDB5/TfT788MOMHz++5P2srCxiYmIqJK9UHyt2ZPDK7BTmbj1UMhvQKpLRSU7aNAi1MJmIiIicjqXFNSoqipYtW5aatWjRgk8++QSAyMhIAA4cOEBUVFTJbQ4cOED79u1PeZ++vr74+mq3IjmZYRgsTDnCK7O3sTQtAwC7DQa2i2ZUkpOm9YItTigiIiJnYmlx7dmzJ1u2bCk127p1K40aNQIgLi6OyMhIfvnll5KimpWVxdKlSxk5cuSFjitVlGEY/LzpIJOSU1i7+xgA3g4b11/UgLt6xxNbN9DagCIiIlImlhbXe++9lx49evDf//6Xm266iWXLlvH666/z+uuvA2Cz2Rg3bhxPPPEETZo0KVkOKzo6mmuuucbK6FIFuNwG3/66n8nJKWxOzwbA18vOzV0aMiKxMdG1/C1OKCIiIuVhaXHt3Lkzn332GQ8//DD/+te/iIuLY+LEifz1r38tuc2DDz5Ibm4uI0aM4NixY/Tq1Yvvv/9ea7jKaRW53Hy+ei9T56Sy/XAuAIE+Dm7rHsuwXnGEB+tUEhERkarI0nVcLwSt41pz5Be5+GjlHl6dk8reY8cBCPX3ZmjPWIb0iKVWgI/FCUVERORUqsQ6riIVIbegmHeX7uKN+ds5mG2u4Vs3yIfhCY35a7dGBPnqr7mIiEh1oJ/oUmVlHi/i7UU7eHNhGkfzigCIDvXjzt7x/KVzDH7eDosTioiISEVScZUq50hOAW8uTOPtRTvJLigGILZOACP7xHNthwb4eNktTigiIiKVQcVVqoz0zHxen7ed95bt4niRC4Cm9YIYneTkijZReDlUWEVERKozFVfxeLsz8pg6N5WPV+yh0OUGoG2DUEYnObmkRT3sdpvFCUVERORCUHEVj5VyMIcpc1L4Ys0+XG5z8YsusWGM7usksUldbDYVVhERkZpExVU8zoZ9mUxOTuG79emcWKwtoUldxiQ56dq4jrXhRERExDIqruIxVu48yuTkFGZvPlgyu6RlPcYkOWkXU8u6YCIiIuIRVFzFUoZhsDj1CJOSU1iUegQAuw2ubBvNqKR4mkdq0wgRERExqbiKJQzDIHnLQV6ZncLqXccA8LLbuO6i+ozs4ySubqC1AUVERMTjqLjKBeVyG3y/Pp3JySls3J8FgI+XnUGdYxiR2JgGtQMsTigiIiKeSsVVLogil5sv1+xjypwUUg/lAhDg4+C2bo0YlhBHRLCfxQlFRETE06m4SqUqKHbx8co9TJ2Typ6jxwEI8fNiSM84hvaIpXagj8UJRUREpKpQcZVKkVdYzLtLd/HG/O0cyCoAoE6gD8MS4ritWyOC/bwtTigiIiJVjYqrVKis/CJmLt7JtAVpZOQWAhAZ4seIxMbc3KUh/j4OixOKiIhIVaXiKhUiI7eQ6QvTmLFoB9n5xQA0DAtgZJ94rruoPr5eKqwiIiJyflRc5bwczMrn9XnbmbV0F8eLXAA4I4IYnRTPVW2j8XLYLU4oIiIi1YWKq5yT3Rl5vDYvlQ9X7KGw2A1Aq+gQxiQ56d8qErvdZnFCERERqW5UXKVcth/KYcqcVD5fvZditwFAx0a1GdPXSZ+m4dhsKqwiIiJSOVRcpUw27c9icnIK3/y6H8Psq/Ry1mV0kpNujcNUWEVERKTSqbjKGa3edZTJySn8vOlgyaxfiwhGJznp0LC2hclERESkplFxlZMYhsHStAwmzU5hQcphAGw2uLxNFKP7OGkZHWJxQhEREamJVFylhGEYzNl6iMmzU1ix8ygADruNazvUZ2SfeOLDgyxOKCIiIjWZiqvgdhv8uDGdSckprN+bBYCPl52bOjXgzsR4YsICLE4oIiIiouJaoxW73Hy9bj+Tk1PYdjAHAH9vB3/t2pDhiY2pF+JncUIRERGR36m41kAFxS4+XbWXqXNS2ZWRB0CwrxdDesYytGccYYE+FicUEREROZmKaw1yvNDF+8t38drc7aRn5QMQFujDsF5x3Na9ESF+3hYnFBERETk9FdcaIDu/iHeW7OJ/87dzJLcQgHohvgxPaMwtXRsS4KO/BiIiIuL51FiqsaO5hUxftIMZC9PIyi8GoEFtf0b2ieeGjg3w9XJYnFBERESk7FRcq6GD2flMm5/GzCU7ySt0ARAfHsioPk4Gto/G22G3OKGIiIhI+am4ViN7jx3n9bmpvL98NwXFbgBaRIUwJsnJgNaROOzallVERESqLhXXaiDtcC5T56Tw6aq9FLsNADo0rMXYvk6SmkVgs6mwioiISNWn4lqFbUnPZnJyCl+v28dvfZXujeswtq+T7vF1VFhFRESkWlFxrYLW7TnGpNkp/LjxQMmsb/MIRic56diotoXJRERERCqPimsVsnT7ESYlpzB/22EAbDa4rHUko/o4aV0/1OJ0IiIiIpVLxdXDGYbBvG2HmTw7hWU7MgBw2G1c3S6aUUnxOCOCLU4oIiIicmGouHoot9vgp00HmJycwro9mQD4OOzc0KkBdyXG07BOgMUJRURERC4sFVcP43IbfL1uH1OSU9lyIBsAP287t3RpxIjExkSG+lmcUERERMQaKq4eorDYzWer9zB1Tio7juQBEOTrxe3dG3FHrzjqBvlanFBERETEWiquFssvcvHB8t28NjeVfZn5ANQK8OaOnnEM7h5LaIC3xQlFREREPIOKq0VyCoqZtWQnb8xP43BOAQDhwb6MSGjMLV0bEuirPxoRERGRP1I7usAy84qYsWgHby5MI/N4EQD1a/lzV+/G3NgpBj9vh8UJRURERDyTimsFcrkNlqVlcDA7n4hgP7rEheGwm7tXHc4p4H/z03hnyU5yCooBiKsbyMg+8VzboT7eDruV0UVEREQ8noprBfl+/X4mfLWR/b+dpwoQFerH3Rc72ZKew/vLd5Ff5AageWQwo5OcXN4mqqTYioiIiMiZqbhWgO/X72fkO6sw/jTfn5nPw5+uL3m/XUwtxiQ5ubh5BHYVVhEREZFyUXE9Ty63wYSvNp5UWv/Ix2Hjjds7kdg0HJtNhVVERETkXOjEyvO0LC2j1OkBp1LoMvDxcqi0ioiIiJwHFdfzdDD7zKW1vLcTERERkVNTcT1PEcFl24K1rLcTERERkVNTcT1PXeLCiAr143QnAdgwVxfoEhd2IWOJiIiIVDsqrufJYbfx2FUtAU4qryfef+yqllr2SkREROQ8qbhWgAGto5h660VEhpY+HSAy1I+pt17EgNZRFiUTERERqT60HFYFGdA6iktaRp525ywREREROT8qrhXIYbfRPb6O1TFEREREqiWdKiAiIiIiVYKKq4iIiIhUCSquIiIiIlIlqLiKiIiISJWg4ioiIiIiVYKKq4iIiIhUCSquIiIiIlIlqLiKiIiISJWg4ioiIiIiVYKKq4iIiIhUCSquIiIiIlIlqLiKiIiISJWg4ioiIiIiVYKX1QEqm2EYAGRlZVmcRERERERO5URPO9HbTqfaF9fs7GwAYmJiLE4iIiIiImeSnZ1NaGjoaT9uM85Wbas4t9vNvn37CA4OxmazVfrjZWVlERMTw+7duwkJCan0xxMRERGpaBe6zxiGQXZ2NtHR0djtpz+TtdofcbXb7TRo0OCCP25ISIiKq4iIiFRpF7LPnOlI6wm6OEtEREREqgQVVxERERGpElRcK5ivry+PPfYYvr6+VkcREREROSee2meq/cVZIiIiIlI96IiriIiIiFQJKq4iIiIiUiWouIqIiIhIlaDiKiIiIiJVgorreVi8eDEOh4MrrrjipI/dfffddOzYEV9fX9q3b3/hw4mIiIiUwen6zNq1a7n55puJiYnB39+fFi1a8NJLL1mU0qTieh6mTZvG2LFjmTdvHvv27Tvp43fccQd/+ctfLEgmIiIiUjan6zMrV64kIiKCd955hw0bNvB///d/PPzww0yaNMmyrFoO6xzl5OQQFRXFihUreOyxx2jbti3/+Mc/Trrd448/zueff86aNWsufEgRERGRMyhrnzlh9OjRbNq0idmzZ1/AlL/TEddz9OGHH9K8eXOaNWvGrbfeyptvvol+BxAREZGqpLx9JjMzk7CwsAuYsDQV13M0bdo0br31VgAGDBhAZmYmc+fOtTiViIiISNmVp88sWrSIDz74gBEjRlzIiKWouJ6DLVu2sGzZMm6++WYAvLy8+Mtf/sK0adMsTiYiIiJSNuXpM+vXr+fqq6/mscce49JLL73QUUt4WfbIVdi0adMoLi4mOjq6ZGYYBr6+vkyaNInQ0FAL04mIiIicXVn7zMaNG7n44osZMWIE//znP62KC+iIa7kVFxfz9ttv8/zzz7NmzZqSt7Vr1xIdHc17771ndUQRERGRMyprn9mwYQNJSUkMHjyY//znPxan1hHXcvv66685evQo/9/O3YU03f5xHP/oYtZGB0VGUZaBjBorhUiTCtYDrCIPJAoyg4EFa1QMix4o6KSQDrIsaJ38qtEDQU94kAfRA8KGaCRbEEpIDbOSECpZJqbb/+CG8R/WP71vl//fzfsF18Gu67fr9+U6GB8url01NTWjdla3bt0qwzDk8/nU1dWlRCKh3t5eff/+PX2rgNPplNVqnYTKAQAA/jKWPLN69WqtW7dOHo9HtbW16u3tlSRZLBbl5+dPRtlchzVeFRUVSiaTevjw4aixtrY2lZWVKRaL6cCBAz893Pz27VsVFhb+gUoBAAB+bix5prKyUg8ePBg1vnDhQsXj8T9Q5WgEVwAAAJgCZ1wBAABgCgRXAAAAmALBFQAAAKZAcAUAAIApEFwBAABgCgRXAAAAmALBFQAAAKZAcAUAAIApEFwBAABgCgRXAJggXq9XOTk5o1pXV9c/ntvtdisQCPzzIn+jp6dHVqtVLpcr6+8CgPEiuALABNq4caM+fvyY0RYtWjTZZaUNDQ39z/Fr165p+/bt6u/vV2tr6x+qCgDGhuAKABMoLy9Pc+bMyWgWi0X19fVaunSp7Ha7CgoK5Pf7lUgkMr4biUTkdrtls9k0Y8YMeTweff78WV6vV83NzWpoaEjv4sbjcUlSc3OzSktLlZeXp7lz5+ro0aMaHh5Oz+l2u7Vv3z4FAgHNmjVLHo/nl7WnUildvXpVu3btUlVVlQzDyMoaAcDfRXAFgD8gNzdXFy5c0KtXrxQKhfT06VMdPnw4PR6NRrV+/Xo5nU61tLQoHA6roqJCIyMjamhoUHl5ufbs2ZPexS0oKND79++1efNmrVixQrFYTMFgUIZh6NSpUxnvDoVCslqtikQiunz58i9rfPbsmQYGBrRhwwZVV1fr9u3b+vbtW9bWBADGKyeVSqUmuwgA+Dfwer26ceOGpk6dmu7btGmT7ty5M+rZu3fvyufzqa+vT5JUVVWl7u5uhcPhn87tdrtVUlKi8+fPp/uOHz+ue/fuqaOjQzk5OZKkS5cu6ciRI/r69atyc3PldrvV39+v9vb239a/c+dOzZ49W+fOnZMklZSUKBAIyOv1jnUJACCrpkx2AQDwb7J27VoFg8H0Z7vdLkl6/Pix6urq1NnZqf7+fg0PD2twcFADAwOy2WyKRqPatm3buN7V0dGh8vLydGiVpFWrVimRSKinp0cLFiyQJC1fvvy3c3358kX379/PCM7V1dUyDIPgCuD/BsEVACaQ3W5XUVFRRl88HteWLVu0d+9enT59WjNnzlQ4HFZNTY2GhoZks9k0bdq0rNb0O7du3dLg4KDKysrSfalUSslkUq9fv5bD4chafQAwVpxxBYAse/HihZLJpM6ePauVK1fK4XDow4cPGc8sW7ZMT548+eUcVqtVIyMjGX1LlixRS0uL/vvEVyQS0fTp0zV//vxx1WgYhg4ePKhoNJpusVhMa9as0ZUrV8Y1FwBkC8EVALKsqKhIP3780MWLF/XmzRtdv3591J+kjh07pufPn8vv9+vly5fq7OxUMBhMn4EtLCxUa2ur4vG4+vr6lEwm5ff79e7dO+3fv1+dnZ1qbGzUyZMnVVtbq9zcsf+8R6NRtbe3a/fu3XK5XBltx44dCoVCGTcVAMBkIbgCQJYVFxervr5eZ86ckcvl0s2bN1VXV5fxjMPh0KNHjxSLxVRaWqry8nI1NjZqypS/TnQdOnRIFotFTqdT+fn56u7u1rx589TU1KS2tjYVFxfL5/OppqZGJ06cGFd9hmHI6XRq8eLFo8YqKyv16dMnNTU1/f0FAIAJwq0CAAAAMAV2XAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApkBwBQAAgCkQXAEAAGAKBFcAAACYAsEVAAAApvAfg8zUttb7nsIAAAAASUVORK5CYII=\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Factor A: tipo de inversión, Factor B: región o sucursal.\n",
                  "- Analizar interacción permite identificar combinaciones óptimas de inversión.\n",
                  "- Medias y tabla ANOVA ayudan a detectar efectos significativos y riesgo asociado.\n"
                ]
              }
            ]
          }
        },
        "8dae06ad619c4e03901f21ef3b17e289": {
          "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
          }
        },
        "1eeddf30a58441fa9859a3d33a0628de": {
          "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
          }
        },
        "cc16baecfed0463aa70ee8563b666727": {
          "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
          }
        },
        "8eb419c2b42949fcbc011501d03a7b52": {
          "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
          }
        },
        "90ad9fa1695946158364c02bc5d46cc7": {
          "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
          }
        },
        "e4dbdd52f3d348f286ccd73a8c751a70": {
          "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
          }
        },
        "a57c9930571a4f2abcaa7bd6e1dd0bbf": {
          "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
          }
        },
        "23bea78f4e3c4db59af8539aa3524abe": {
          "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
          }
        },
        "e2c17ef628414e869c333be13874cdcf": {
          "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
          }
        },
        "e74934142316444e992c028cb171314c": {
          "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
          }
        },
        "082eacdb280b4b499617e1d608d72afd": {
          "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
          }
        },
        "2d89399057af4288912f2a048db173f9": {
          "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
          }
        },
        "616eb69d446a4048b77b50c4b47fee37": {
          "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
          }
        },
        "2b34ff46edf24227ab434eab072b258f": {
          "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": [
            "689b82c4b34140edb6b762f692acf72a",
            "df3ac7518a6a4ad38a8bd9c22dbb3958",
            "bdfb8310a71d4102a05835471e13e5d4",
            "a14486d6b6034e6e9fcfe771442f633c",
            "e88dc619108445c5a043cf8c218f9c6c",
            "b0533b8545b14f9b95477bbbe04ae513",
            "21953f2c85ae47b9b88b7f36e38a6ebe",
            "7ba668ad27f1489c9860c04b46f5d281",
            "8dae06ad619c4e03901f21ef3b17e289",
            "1eeddf30a58441fa9859a3d33a0628de",
            "cc16baecfed0463aa70ee8563b666727",
            "8eb419c2b42949fcbc011501d03a7b52",
            "90ad9fa1695946158364c02bc5d46cc7",
            "e4dbdd52f3d348f286ccd73a8c751a70",
            "a57c9930571a4f2abcaa7bd6e1dd0bbf",
            "23bea78f4e3c4db59af8539aa3524abe",
            "e2c17ef628414e869c333be13874cdcf",
            "e74934142316444e992c028cb171314c",
            "082eacdb280b4b499617e1d608d72afd",
            "2d89399057af4288912f2a048db173f9",
            "616eb69d446a4048b77b50c4b47fee37",
            "2b34ff46edf24227ab434eab072b258f"
          ]
        },
        "id": "NQb1DQEwZb8-",
        "outputId": "6a58b106-ca50-4d83-9549-1274e2ecf939"
      },
      "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": "689b82c4b34140edb6b762f692acf72a"
            }
          },
          "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_10007/3874459547.py</a>Simula un ANOVA factorial de dos factores para análisis financiero.\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\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.formula.api import ols\n",
        "import statsmodels.api as sm\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",
        "    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\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 por combinación de niveles\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",
        "    # 🔹 Mostrar medias por combinación\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\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",
        "\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(\"- Analizar interacción permite identificar combinaciones óptimas de inversión.\")\n",
        "    print(\"- Medias y tabla ANOVA ayudan a detectar efectos significativos y riesgo asociado.\")\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",
        ")"
      ]
    }
  ]
}