exercise_chisquared-independence.ipynb

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        "## Ejercicio Test Chi-Cuadrado\n",
        "\n",
        "### Tabla de Frecuencias Observadas\n",
        "\n",
        "| **Origen del tráfico** | **Electrónica** | **Ropa** | **Hogar** | **Total** |\n",
        "|------------------------|----------------|----------|-----------|-----------|\n",
        "| Orgánico              | 30             | 50       | 40        | 120       |\n",
        "| Pago por clic         | 40             | 30       | 60        | 130       |\n",
        "| Redes Sociales        | 20             | 40       | 90        | 150       |\n",
        "| **Total**             | 90             | 120      | 190       | 400       |\n",
        "\n",
        "---\n",
        "\n",
        "### **Cálculo de las Frecuencias Esperadas**\n",
        "\n",
        "Las frecuencias esperadas se calculan utilizando la fórmula:\n",
        "\n",
        "$$\n",
        "E_{ij} = \\frac{\\text{(Total de la fila i)} \\times \\text{(Total de la columna j)}}{\\text{Total general}}\n",
        "$$\n",
        "\n",
        "1. **Frecuencia esperada para la celda (Orgánico, Electrónica):**\n",
        "   $$\n",
        "   E_{11} = \\frac{(120 \\times 90)}{400} = 27\n",
        "   $$\n",
        "\n",
        "2. **Frecuencia esperada para la celda (Orgánico, Ropa):**\n",
        "   $$\n",
        "   E_{12} = \\frac{(120 \\times 120)}{400} = 36\n",
        "   $$\n",
        "\n",
        "3. **Frecuencia esperada para la celda (Orgánico, Hogar):**\n",
        "   $$\n",
        "   E_{13} = \\frac{(120 \\times 190)}{400} = 57\n",
        "   $$\n",
        "\n",
        "4. Se repite este cálculo para todas las celdas.\n",
        "\n",
        "### Tabla de Valores Esperados\n",
        "\n",
        "| **Origen del tráfico** | **Electrónica (E)** | **Ropa (E)** | **Hogar (E)** | **Total** |\n",
        "|------------------------|--------------------|--------------|---------------|-----------|\n",
        "| Orgánico              | 27.00             | 36.00        | 57.00         | 120       |\n",
        "| Pago por clic         | 29.25             | 39.00        | 61.75         | 130       |\n",
        "| Redes Sociales        | 33.75             | 45.00        | 71.25         | 150       |\n",
        "| **Total**             | 90                | 120          | 190           | 400       |\n",
        "\n",
        "---\n",
        "\n",
        "### **Cálculo del Estadístico \\( \\chi^2 \\)**\n",
        "\n",
        "La fórmula para calcular \\( \\chi^2 \\) es:\n",
        "\n",
        "$$\n",
        "\\chi^2 = \\sum \\frac{(O_{ij} - E_{ij})^2}{E_{ij}}\n",
        "$$\n",
        "\n",
        "Donde:\n",
        "- $O_{ij}$: Frecuencias observadas.\n",
        "- $E_{ij}$: Frecuencias esperadas.\n",
        "\n",
        "1. **Para la celda (Orgánico, Electrónica):**\n",
        "   $$\n",
        "   \\chi^2_{11} = \\frac{(30 - 27)^2}{27} = \\frac{3^2}{27} = 0.33\n",
        "   $$\n",
        "\n",
        "2. **Para la celda (Orgánico, Ropa):**\n",
        "   $$\n",
        "   \\chi^2_{12} = \\frac{(50 - 36)^2}{36} = \\frac{14^2}{36} = 5.44\n",
        "   $$\n",
        "\n",
        "3. **Para la celda (Orgánico, Hogar):**\n",
        "   $$\n",
        "   \\chi^2_{13} = \\frac{(40 - 57)^2}{57} = \\frac{(-17)^2}{57} = 5.07\n",
        "   $$\n",
        "\n",
        "4. Se repite este cálculo para todas las celdas, y luego se suma.\n",
        "\n",
        "### Suma Total del Estadístico\n",
        "\n",
        "$$\n",
        "\\chi^2 = 0.33 + 5.44 + 5.07 + \\dots = 28.02\n",
        "$$\n",
        "\n",
        "---\n",
        "\n",
        "### **Interpretación del Resultado**\n",
        "\n",
        "1. **Grados de Libertad (\\( \\text{dof} \\)):**\n",
        "   $$\n",
        "   \\text{dof} = (\\text{número de filas} - 1) \\times (\\text{número de columnas} - 1) = (3 - 1) \\times (3 - 1) = 4\n",
        "   $$\n",
        "\n",
        "2. **Umbral Crítico (para \\( \\alpha = 0.05 \\)):**\n",
        "   De la tabla $\\chi^2$, el valor crítico para 4 grados de libertad es aproximadamente **9.49**.\n",
        "\n",
        "3. **Conclusión:**\n",
        "   - Como $\\chi^2 = 28.02$ es mayor que el umbral crítico \\( 9.49 \\), rechazamos la hipótesis nula.\n",
        "   - **Interpretación:** Existe una relación significativa entre el origen del tráfico y la categoría de producto comprada.\n"
      ],
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      "cell_type": "code",
      "execution_count": 1,
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uCEtPRG6iSIl",
        "outputId": "135f7aa1-af24-4dec-f2e6-5497546146e7"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cálculo manual:\n",
            "Valor de chi-cuadrado (manual): 28.02\n",
            "Usando scipy:\n",
            "Valor de chi-cuadrado (scipy): 28.02\n",
            "P-valor: 0.00001\n",
            "Grados de libertad: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import chi2, chi2_contingency\n",
        "\n",
        "# Datos observados\n",
        "observed = np.array([\n",
        "    [30, 50, 40],  # Orgánico\n",
        "    [40, 30, 60],  # Pago por clic\n",
        "    [20, 40, 90]   # Redes Sociales\n",
        "])\n",
        "\n",
        "# Totales\n",
        "row_totals = observed.sum(axis=1)\n",
        "col_totals = observed.sum(axis=0)\n",
        "total = observed.sum()\n",
        "\n",
        "# Cálculo manual de frecuencias esperadas\n",
        "expected = np.outer(row_totals, col_totals) / total\n",
        "\n",
        "# Cálculo manual de chi-cuadrado\n",
        "chi_squared_manual = np.sum((observed - expected) ** 2 / expected)\n",
        "\n",
        "# Usando scipy para calcular chi-cuadrado, p-valor y grados de libertad\n",
        "chi_squared, p_value, dof, expected_scipy = chi2_contingency(observed)\n",
        "\n",
        "# Validación\n",
        "print(\"Cálculo manual:\")\n",
        "print(f\"Valor de chi-cuadrado (manual): {chi_squared_manual:.2f}\")\n",
        "print(\"Usando scipy:\")\n",
        "print(f\"Valor de chi-cuadrado (scipy): {chi_squared:.2f}\")\n",
        "print(f\"P-valor: {p_value:.5f}\")\n",
        "print(f\"Grados de libertad: {dof}\")\n",
        "\n",
        "# Gráficos de barras (frecuencias observadas y esperadas)\n",
        "categories = [\"Electrónica\", \"Ropa\", \"Hogar\"]\n",
        "traffic_sources = [\"Orgánico\", \"Pago por clic\", \"Redes Sociales\"]\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True)\n",
        "\n",
        "# Frecuencias observadas\n",
        "axes[0].bar(categories, observed[0], label=\"Orgánico\", alpha=0.7)\n",
        "axes[0].bar(categories, observed[1], label=\"Pago por clic\", alpha=0.7, bottom=observed[0])\n",
        "axes[0].bar(categories, observed[2], label=\"Redes Sociales\", alpha=0.7,\n",
        "            bottom=observed[0] + observed[1])\n",
        "axes[0].set_title(\"Frecuencias Observadas\")\n",
        "axes[0].set_ylabel(\"Frecuencia\")\n",
        "axes[0].legend(title=\"Origen del tráfico\")\n",
        "\n",
        "# Frecuencias esperadas\n",
        "axes[1].bar(categories, expected[0], label=\"Orgánico\", alpha=0.7)\n",
        "axes[1].bar(categories, expected[1], label=\"Pago por clic\", alpha=0.7, bottom=expected[0])\n",
        "axes[1].bar(categories, expected[2], label=\"Redes Sociales\", alpha=0.7,\n",
        "            bottom=expected[0] + expected[1])\n",
        "axes[1].set_title(\"Frecuencias Esperadas\")\n",
        "axes[1].legend(title=\"Origen del tráfico\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Gráfico de distribución chi-cuadrado\n",
        "alpha = 0.05\n",
        "critical_value = chi2.ppf(1 - alpha, dof)\n",
        "x = np.linspace(0, chi_squared + 10, 500)\n",
        "y = chi2.pdf(x, dof)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(x, y, label=\"Distribución chi-cuadrado\", lw=2)\n",
        "plt.axvline(chi_squared, color=\"red\", linestyle=\"--\", label=f\"Valor calculado: {chi_squared:.2f}\")\n",
        "plt.axvline(critical_value, color=\"blue\", linestyle=\"--\", label=f\"Umbral crítico: {critical_value:.2f}\")\n",
        "plt.fill_between(x, y, where=(x >= critical_value), color=\"blue\", alpha=0.3, label=\"Rechazo de H0\")\n",
        "plt.title(\"Distribución chi-cuadrado y valores calculados\")\n",
        "plt.xlabel(\"Valor de chi-cuadrado\")\n",
        "plt.ylabel(\"Densidad de probabilidad\")\n",
        "plt.legend()\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()"
      ]
    }
  ]
}