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                "text": [
                  "=== Tabla de Contingencia ===\n"
                ]
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                  "text/plain": "             Grupo 1  Grupo 2\nCategoría 1       30       20\nCategoría 2       10       40",
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                  "Chi-Cuadrado: 15.04\n",
                  "Grados de libertad: 1\n",
                  "p-valor: 0.000\n",
                  "\n",
                  "=== Frecuencias Esperadas (si independencia) ===\n"
                ]
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                  "text/plain": "             Grupo 1  Grupo 2\nCategoría 1     20.0     30.0\nCategoría 2     20.0     30.0",
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                "data": {
                  "text/plain": "<Figure size 600x400 with 2 Axes>",
                  "image/png": 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                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Evaluar independencia entre variables categóricas como estrategia de inversión y resultado.\n",
                  "- p-valor < 0.05 indica relación significativa entre variables, útil para decisiones de negocio.\n",
                  "- Modificar frecuencias permite explorar distintos escenarios y riesgos.\n"
                ]
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>chi2_interactiva</b><br/>def chi2_interactiva(A=30, B=20, C=10, D=40)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_7524/1285503843.py</a>Simula una tabla de contingencia 2x2 y realiza prueba Chi-Cuadrado.\n",
              "\n",
              "Parámetros:\n",
              "- A, B, C, D: frecuencias observadas en la tabla 2x2</pre></div>"
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        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import chi2_contingency\n",
        "from ipywidgets import interact, IntSlider\n",
        "\n",
        "# 🔹 Función simuladora de prueba Chi-Cuadrado\n",
        "def chi2_interactiva(A=30, B=20, C=10, D=40):\n",
        "    \"\"\"\n",
        "    Simula una tabla de contingencia 2x2 y realiza prueba Chi-Cuadrado.\n",
        "\n",
        "    Parámetros:\n",
        "    - A, B, C, D: frecuencias observadas en la tabla 2x2\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Crear tabla de contingencia\n",
        "    tabla = pd.DataFrame([[A, B],\n",
        "                          [C, D]],\n",
        "                         index=['Categoría 1', 'Categoría 2'],\n",
        "                         columns=['Grupo 1', 'Grupo 2'])\n",
        "\n",
        "    print(\"=== Tabla de Contingencia ===\")\n",
        "    display(tabla)\n",
        "\n",
        "    # 🔹 Realizar prueba Chi-Cuadrado\n",
        "    chi2, p, dof, expected = chi2_contingency(tabla)\n",
        "\n",
        "    print(f\"Chi-Cuadrado: {chi2:.2f}\")\n",
        "    print(f\"Grados de libertad: {dof}\")\n",
        "    print(f\"p-valor: {p:.3f}\")\n",
        "\n",
        "    # 🔹 Mostrar tabla esperada\n",
        "    print(\"\\n=== Frecuencias Esperadas (si independencia) ===\")\n",
        "    display(pd.DataFrame(expected, index=tabla.index, columns=tabla.columns))\n",
        "\n",
        "    # 🔹 Visualización de calor\n",
        "    plt.figure(figsize=(6,4))\n",
        "    sns.heatmap(tabla, annot=True, fmt=\"d\", cmap='Blues')\n",
        "    plt.title('Tabla de Contingencia')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Evaluar independencia entre variables categóricas como estrategia de inversión y resultado.\")\n",
        "    print(\"- p-valor < 0.05 indica relación significativa entre variables, útil para decisiones de negocio.\")\n",
        "    print(\"- Modificar frecuencias permite explorar distintos escenarios y riesgos.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    chi2_interactiva,\n",
        "    A=IntSlider(value=30, min=0, max=100, step=1, description='A'),\n",
        "    B=IntSlider(value=20, min=0, max=100, step=1, description='B'),\n",
        "    C=IntSlider(value=10, min=0, max=100, step=1, description='C'),\n",
        "    D=IntSlider(value=40, min=0, max=100, step=1, description='D')\n",
        ")"
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