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                  "=== Resultados ANOVA de un Factor ===\n",
                  "Estadístico F: 58.27\n",
                  "Valor p: 0.0000\n",
                  "Decisión: Rechazar H0 → Hay diferencias significativas entre los grupos\n",
                  "\n",
                  "Tabla resumen de grupos:\n"
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                  "text/plain": "     Grupo      Media  Desviación\n0  Grupo 1  49.180156   10.365973\n1  Grupo 2  69.754303    9.259396\n2  Grupo 3  98.046911   10.802671",
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                    "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Grupo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Grupo 1\",\n          \"Grupo 2\",\n          \"Grupo 3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Media\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.53476095650358,\n        \"min\": 49.18015564948875,\n        \"max\": 98.04691087529173,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          49.18015564948875,\n          69.75430265844723,\n          98.04691087529173\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desviaci\\u00f3n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7954994497970614,\n        \"min\": 9.259396151604875,\n        \"max\": 10.802671257887809,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.365973376370698,\n          9.259396151604875,\n          10.802671257887809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
                  "image/png": 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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Comparar rendimiento de distintas sucursales o portafolios.\n",
                  "- Ver si existen diferencias significativas entre grupos.\n",
                  "- Visualizar medias y dispersión ayuda en la toma de decisiones.\n"
                ]
              }
            ]
          }
        },
        "08dcaff14f754fb1976075c6adf17b10": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_ANOVA</b><br/>def simulador_ANOVA(n_grupos=3, n_muestra=10, 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_2783/1314601381.py</a>Simula un ANOVA de un factor para comparar medias de múltiples grupos financieros.\n",
              "\n",
              "Parámetros:\n",
              "- n_grupos: número de grupos a comparar\n",
              "- n_muestra: tamaño de muestra por grupo\n",
              "- media_min: media mínima posible de los grupos\n",
              "- media_max: media máxima posible de los grupos\n",
              "- desviacion: desviación estándar de cada grupo</pre></div>"
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        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.stats import f_oneway\n",
        "from ipywidgets import interact, IntSlider, FloatSlider\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 🔹 Función simuladora de ANOVA de un factor\n",
        "def simulador_ANOVA(n_grupos=3, n_muestra=10, media_min=50, media_max=100, desviacion=10):\n",
        "    \"\"\"\n",
        "    Simula un ANOVA de un factor para comparar medias de múltiples grupos financieros.\n",
        "\n",
        "    Parámetros:\n",
        "    - n_grupos: número de grupos a comparar\n",
        "    - n_muestra: tamaño de muestra por grupo\n",
        "    - media_min: media mínima posible de los grupos\n",
        "    - media_max: media máxima posible de los grupos\n",
        "    - desviacion: desviación estándar de cada grupo\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar datos aleatorios para cada grupo\n",
        "    grupos = []\n",
        "    medias_grupos = np.linspace(media_min, media_max, n_grupos)\n",
        "    for i in range(n_grupos):\n",
        "        datos = np.random.normal(loc=medias_grupos[i], scale=desviacion, size=n_muestra)\n",
        "        grupos.append(datos)\n",
        "\n",
        "    # 🔹 Realizar ANOVA de un factor\n",
        "    F_stat, p_value = f_oneway(*grupos)\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(\"=== Resultados ANOVA de un Factor ===\")\n",
        "    print(f\"Estadístico F: {F_stat:.2f}\")\n",
        "    print(f\"Valor p: {p_value:.4f}\")\n",
        "    if p_value < 0.05:\n",
        "        print(\"Decisión: Rechazar H0 → Hay diferencias significativas entre los grupos\")\n",
        "    else:\n",
        "        print(\"Decisión: No rechazar H0 → No hay evidencia de diferencias significativas entre los grupos\")\n",
        "\n",
        "    # 🔹 Crear tabla resumen de medias y desviaciones por grupo\n",
        "    tabla = pd.DataFrame({\n",
        "        \"Grupo\": [f\"Grupo {i+1}\" for i in range(n_grupos)],\n",
        "        \"Media\": [np.mean(g) for g in grupos],\n",
        "        \"Desviación\": [np.std(g, ddof=1) for g in grupos]\n",
        "    })\n",
        "    print(\"\\nTabla resumen de grupos:\")\n",
        "    display(tabla)\n",
        "\n",
        "    # 🔹 Gráfica de los grupos\n",
        "    plt.figure(figsize=(10,6))\n",
        "    for i, g in enumerate(grupos):\n",
        "        plt.scatter([i+1]*n_muestra, g, label=f'Grupo {i+1}')\n",
        "        plt.plot([i+1], [np.mean(g)], marker='o', color='black', markersize=10)  # media\n",
        "    plt.xticks(range(1, n_grupos+1), [f\"Grupo {i+1}\" for i in range(n_grupos)])\n",
        "    plt.xlabel(\"Grupos\")\n",
        "    plt.ylabel(\"Valores\")\n",
        "    plt.title(\"Simulación ANOVA de un Factor\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Comparar rendimiento de distintas sucursales o portafolios.\")\n",
        "    print(\"- Ver si existen diferencias significativas entre grupos.\")\n",
        "    print(\"- Visualizar medias y dispersión ayuda en la toma de decisiones.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_ANOVA,\n",
        "    n_grupos=IntSlider(value=3, min=2, max=6, step=1, description='N° Grupos'),\n",
        "    n_muestra=IntSlider(value=10, min=5, max=50, step=1, description='N° Datos por Grupo'),\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",
        ")"
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