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                "text": [
                  "=== Variabilidad de los grupos ===\n",
                  "Grupo 1 - Varianza intra-grupo: 80.55\n",
                  "Grupo 2 - Varianza intra-grupo: 136.86\n",
                  "Grupo 3 - Varianza intra-grupo: 89.13\n",
                  "Varianza inter-grupos (entre medias): 619.08\n"
                ]
              },
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                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
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                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Variabilidad intra-grupo: dispersión de rendimientos dentro de un portafolio o sucursal.\n",
                  "- Variabilidad inter-grupo: diferencias promedio entre carteras, sucursales o productos.\n",
                  "- Útil para identificar riesgos y oportunidades de optimización de portafolios.\n"
                ]
              }
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>visualizador_variabilidad</b><br/>def visualizador_variabilidad(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_4028/1990587117.py</a>Simula y visualiza variabilidad intra e intergrupos.\n",
              "\n",
              "Parámetros:\n",
              "- n_grupos: número de grupos\n",
              "- n_muestra: número de observaciones por grupo\n",
              "- media_min, media_max: rango de medias para los grupos\n",
              "- desviacion: desviación estándar dentro de cada grupo</pre></div>"
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      ],
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        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, IntSlider, FloatSlider\n",
        "\n",
        "# 🔹 Función para simular y visualizar variabilidad\n",
        "def visualizador_variabilidad(n_grupos=3, n_muestra=10, media_min=50, media_max=100, desviacion=10):\n",
        "    \"\"\"\n",
        "    Simula y visualiza variabilidad intra e intergrupos.\n",
        "\n",
        "    Parámetros:\n",
        "    - n_grupos: número de grupos\n",
        "    - n_muestra: número de observaciones por grupo\n",
        "    - media_min, media_max: rango de medias para los grupos\n",
        "    - desviacion: desviación estándar dentro de cada grupo\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar datos\n",
        "    medias_grupos = np.linspace(media_min, media_max, n_grupos)\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",
        "    # 🔹 Calcular variabilidad\n",
        "    var_intra = [np.var(g, ddof=1) for g in grupos]  # variabilidad dentro de cada grupo\n",
        "    var_inter = np.var([np.mean(g) for g in grupos], ddof=1)  # variabilidad entre medias de grupos\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(\"=== Variabilidad de los grupos ===\")\n",
        "    for i, v in enumerate(var_intra):\n",
        "        print(f\"Grupo {i+1} - Varianza intra-grupo: {v:.2f}\")\n",
        "    print(f\"Varianza inter-grupos (entre medias): {var_inter:.2f}\")\n",
        "\n",
        "    # 🔹 Visualización\n",
        "    plt.figure(figsize=(10,6))\n",
        "\n",
        "    colors = plt.cm.tab10.colors\n",
        "    for i, g in enumerate(grupos):\n",
        "        plt.scatter([i+1]*n_muestra, g, color=colors[i % 10], alpha=0.6, label=f'Grupo {i+1}')\n",
        "        plt.plot([i+1], [np.mean(g)], 'kx', markersize=12, label=f'Media Grupo {i+1}' if i==0 else \"\")\n",
        "\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('Visualización de Variabilidad Intra e Intergrupos')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Variabilidad intra-grupo: dispersión de rendimientos dentro de un portafolio o sucursal.\")\n",
        "    print(\"- Variabilidad inter-grupo: diferencias promedio entre carteras, sucursales o productos.\")\n",
        "    print(\"- Útil para identificar riesgos y oportunidades de optimización de portafolios.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    visualizador_variabilidad,\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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