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              {
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                "text": [
                  "=== Prueba Z ===\n",
                  "Z calculado: -1.75\n",
                  "Z crítico: ±1.96\n",
                  "Decisión: No rechazar H0 (medias iguales)\n",
                  "\n",
                  "=== Prueba F ===\n",
                  "F calculado: 0.69\n",
                  "F crítico: [0.48, 2.10]\n",
                  "Decisión: No rechazar H0 (varianzas iguales)\n",
                  "\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1400x500 with 2 Axes>",
                  "image/png": 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                  "💡 Aplicación financiera:\n",
                  "- Z: comparar medias de rendimientos de dos carteras.\n",
                  "- F: comparar volatilidad (riesgo) de dos inversiones.\n",
                  "- Visualizar regiones críticas ayuda a entender riesgo de error tipo I.\n"
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              "<function __main__.simulador_ZF(media1=100, media2=105, sigma1=10, sigma2=12, n1=30, n2=30, alpha=0.05)>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_ZF</b><br/>def simulador_ZF(media1=100, media2=105, sigma1=10, sigma2=12, n1=30, n2=30, alpha=0.05)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_2312/1092784025.py</a>Simula pruebas Z y F para comparar medias y varianzas de dos poblaciones financieras.\n",
              "\n",
              "Parámetros:\n",
              "- media1, media2: medias de las poblaciones\n",
              "- sigma1, sigma2: desviaciones estándar\n",
              "- n1, n2: tamaños de muestra\n",
              "- alpha: nivel de significancia</pre></div>"
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      ],
      "source": [
        "# 📦 Librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, FloatSlider, IntSlider\n",
        "from scipy.stats import norm, f\n",
        "\n",
        "# 🔹 Simulador de pruebas Z y F\n",
        "def simulador_ZF(media1=100, media2=105, sigma1=10, sigma2=12, n1=30, n2=30, alpha=0.05):\n",
        "    \"\"\"\n",
        "    Simula pruebas Z y F para comparar medias y varianzas de dos poblaciones financieras.\n",
        "\n",
        "    Parámetros:\n",
        "    - media1, media2: medias de las poblaciones\n",
        "    - sigma1, sigma2: desviaciones estándar\n",
        "    - n1, n2: tamaños de muestra\n",
        "    - alpha: nivel de significancia\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Prueba Z para comparación de medias\n",
        "    z = (media1 - media2) / np.sqrt((sigma1**2 / n1) + (sigma2**2 / n2))\n",
        "    z_crit = norm.ppf(1 - alpha/2)\n",
        "\n",
        "    # 🔹 Decisión Z\n",
        "    if abs(z) > z_crit:\n",
        "        decision_z = \"Rechazar H0 (medias distintas)\"\n",
        "    else:\n",
        "        decision_z = \"No rechazar H0 (medias iguales)\"\n",
        "\n",
        "    # 🔹 Prueba F para comparación de varianzas\n",
        "    F_value = (sigma1**2) / (sigma2**2)\n",
        "    F_lower = f.ppf(alpha/2, n1-1, n2-1)\n",
        "    F_upper = f.ppf(1 - alpha/2, n1-1, n2-1)\n",
        "\n",
        "    # 🔹 Decisión F\n",
        "    if F_value < F_lower or F_value > F_upper:\n",
        "        decision_f = \"Rechazar H0 (varianzas distintas)\"\n",
        "    else:\n",
        "        decision_f = \"No rechazar H0 (varianzas iguales)\"\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(\"=== Prueba Z ===\")\n",
        "    print(f\"Z calculado: {z:.2f}\")\n",
        "    print(f\"Z crítico: ±{z_crit:.2f}\")\n",
        "    print(f\"Decisión: {decision_z}\\n\")\n",
        "\n",
        "    print(\"=== Prueba F ===\")\n",
        "    print(f\"F calculado: {F_value:.2f}\")\n",
        "    print(f\"F crítico: [{F_lower:.2f}, {F_upper:.2f}]\")\n",
        "    print(f\"Decisión: {decision_f}\\n\")\n",
        "\n",
        "    # 🔹 Gráfica de Z\n",
        "    x = np.linspace(-4, 4, 400)\n",
        "    y = norm.pdf(x, 0, 1)\n",
        "    plt.figure(figsize=(14,5))\n",
        "\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.plot(x, y, label='Distribución Z')\n",
        "    plt.fill_between(x, y, where=(x <= -z_crit) | (x >= z_crit), color='red', alpha=0.3, label='Región crítica')\n",
        "    plt.axvline(z, color='blue', linestyle='--', label=f'Z calculado = {z:.2f}')\n",
        "    plt.title('Prueba Z')\n",
        "    plt.xlabel('Z')\n",
        "    plt.ylabel('Densidad')\n",
        "    plt.legend()\n",
        "\n",
        "    # 🔹 Gráfica de F\n",
        "    df1, df2 = n1-1, n2-1\n",
        "    x = np.linspace(f.ppf(0.001, df1, df2), f.ppf(0.999, df1, df2), 400)\n",
        "    y = f.pdf(x, df1, df2)\n",
        "\n",
        "    plt.subplot(1,2,2)\n",
        "    plt.plot(x, y, label='Distribución F')\n",
        "    plt.fill_between(x, y, where=(x <= F_lower) | (x >= F_upper), color='red', alpha=0.3, label='Región crítica')\n",
        "    plt.axvline(F_value, color='blue', linestyle='--', label=f'F calculado = {F_value:.2f}')\n",
        "    plt.title('Prueba F')\n",
        "    plt.xlabel('F')\n",
        "    plt.ylabel('Densidad')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "    # 🔹 Consejo financiero\n",
        "    print(\"💡 Aplicación financiera:\")\n",
        "    print(\"- Z: comparar medias de rendimientos de dos carteras.\")\n",
        "    print(\"- F: comparar volatilidad (riesgo) de dos inversiones.\")\n",
        "    print(\"- Visualizar regiones críticas ayuda a entender riesgo de error tipo I.\")\n",
        "\n",
        "# 🔹 Interactividad con sliders\n",
        "interact(\n",
        "    simulador_ZF,\n",
        "    media1=FloatSlider(value=100, min=50, max=150, step=1, description='Media 1'),\n",
        "    media2=FloatSlider(value=105, min=50, max=150, step=1, description='Media 2'),\n",
        "    sigma1=FloatSlider(value=10, min=1, max=50, step=1, description='σ1'),\n",
        "    sigma2=FloatSlider(value=12, min=1, max=50, step=1, description='σ2'),\n",
        "    n1=IntSlider(value=30, min=5, max=100, step=1, description='N1'),\n",
        "    n2=IntSlider(value=30, min=5, max=100, step=1, description='N2'),\n",
        "    alpha=FloatSlider(value=0.05, min=0.01, max=0.10, step=0.005, description='α')\n",
        ")"
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