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                  "=== Prueba de Levene ===\n",
                  "Estadístico: 7.50\n",
                  "Valor p: 0.0026\n",
                  "Decisión: Rechazar H0 → Varianzas no son homogéneas\n",
                  "\n",
                  "Tabla resumen de grupos:\n"
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                  "text/plain": "     Grupo     Media  Desviación\n0  Grupo 1  1.816498    1.028670\n1  Grupo 2  3.361535    0.305545\n2  Grupo 3  3.898205    0.140288",
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                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
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                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Usar Levene para validar supuestos antes de ANOVA o comparaciones de varianzas.\n",
                  "- Transformaciones ayudan a estabilizar varianzas cuando los datos financieros son muy dispersos.\n",
                  "- Ejemplo: rendimientos de distintas sucursales, volatilidad de portafolios o ventas.\n"
                ]
              }
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              "<function __main__.simulador_levene(n_grupos=3, n_muestra=10, media_min=50, media_max=100, desviacion=10, transformacion='Ninguna')>"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_levene</b><br/>def simulador_levene(n_grupos=3, n_muestra=10, media_min=50, media_max=100, desviacion=10, transformacion=&#x27;Ninguna&#x27;)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_9522/601207820.py</a>Aplica la prueba de Levene para evaluar homogeneidad de varianzas y permite transformaciones de datos.\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",
              "- transformacion: &#x27;Ninguna&#x27;, &#x27;Logarítmica&#x27;, &#x27;Raíz cuadrada&#x27;</pre></div>"
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      "source": [
        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import levene\n",
        "from ipywidgets import interact, IntSlider, FloatSlider, Dropdown\n",
        "\n",
        "# 🔹 Función simuladora de prueba de Levene y transformaciones\n",
        "def simulador_levene(n_grupos=3, n_muestra=10, media_min=50, media_max=100, desviacion=10, transformacion='Ninguna'):\n",
        "    \"\"\"\n",
        "    Aplica la prueba de Levene para evaluar homogeneidad de varianzas y permite transformaciones de datos.\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",
        "    - transformacion: 'Ninguna', 'Logarítmica', 'Raíz cuadrada'\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",
        "    # 🔹 Aplicar transformación si corresponde\n",
        "    if transformacion == 'Logarítmica':\n",
        "        grupos = [np.log(g - np.min(np.concatenate(grupos)) + 1) for g in grupos]\n",
        "    elif transformacion == 'Raíz cuadrada':\n",
        "        grupos = [np.sqrt(g - np.min(np.concatenate(grupos)) + 1) for g in grupos]\n",
        "\n",
        "    # 🔹 Prueba de Levene\n",
        "    stat, p_value = levene(*grupos)\n",
        "\n",
        "    print(\"=== Prueba de Levene ===\")\n",
        "    print(f\"Estadístico: {stat:.2f}\")\n",
        "    print(f\"Valor p: {p_value:.4f}\")\n",
        "    if p_value < 0.05:\n",
        "        print(\"Decisión: Rechazar H0 → Varianzas no son homogéneas\")\n",
        "    else:\n",
        "        print(\"Decisión: No rechazar H0 → Varianzas homogéneas\")\n",
        "\n",
        "    # 🔹 Crear tabla resumen\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",
        "    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",
        "    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(f'Prueba de Levene - Transformación: {transformacion}')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Usar Levene para validar supuestos antes de ANOVA o comparaciones de varianzas.\")\n",
        "    print(\"- Transformaciones ayudan a estabilizar varianzas cuando los datos financieros son muy dispersos.\")\n",
        "    print(\"- Ejemplo: rendimientos de distintas sucursales, volatilidad de portafolios o ventas.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_levene,\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",
        "    transformacion=Dropdown(options=['Ninguna', 'Logarítmica', 'Raíz cuadrada'], description='Transformación')\n",
        ")"
      ]
    }
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}