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            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "R² del modelo: 0.87\n",
                  "Error estándar de los residuos: 10.69\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
                  "image/png": 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                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Evaluar ajuste de modelos predictivos, como predicción de ventas o rendimientos.\n",
                  "- R² indica qué proporción de la variabilidad se explica por el modelo.\n",
                  "- Error estándar mide la precisión de las predicciones y volatilidad de datos.\n"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>evaluacion_modelo</b><br/>def evaluacion_modelo(n=50, pendiente=1, intercepto=0, ruido=10)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_4027/1973948034.py</a>Simula un modelo de regresión lineal y evalúa R² y error estándar.\n",
              "\n",
              "Parámetros:\n",
              "- n: número de observaciones\n",
              "- pendiente: pendiente de la recta\n",
              "- intercepto: intercepto de la recta\n",
              "- ruido: desviación estándar del ruido</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",
        "\n",
        "# 🔹 Función simuladora de evaluación de modelo\n",
        "def evaluacion_modelo(n=50, pendiente=1, intercepto=0, ruido=10):\n",
        "    \"\"\"\n",
        "    Simula un modelo de regresión lineal y evalúa R² y error estándar.\n",
        "\n",
        "    Parámetros:\n",
        "    - n: número de observaciones\n",
        "    - pendiente: pendiente de la recta\n",
        "    - intercepto: intercepto de la recta\n",
        "    - ruido: desviación estándar del ruido\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar variable independiente X\n",
        "    X = np.linspace(0, 100, n)\n",
        "\n",
        "    # 🔹 Generar variable dependiente Y\n",
        "    Y = intercepto + pendiente*X + np.random.normal(0, ruido, n)\n",
        "\n",
        "    # 🔹 Ajustar recta de regresión por mínimos cuadrados\n",
        "    pendiente_ajustada, intercepto_ajustada = np.polyfit(X, Y, 1)\n",
        "    Y_pred = pendiente_ajustada*X + intercepto_ajustada\n",
        "\n",
        "    # 🔹 Calcular R²\n",
        "    SS_res = np.sum((Y - Y_pred)**2)\n",
        "    SS_tot = np.sum((Y - np.mean(Y))**2)\n",
        "    R2 = 1 - SS_res/SS_tot\n",
        "\n",
        "    # 🔹 Calcular error estándar de los residuos\n",
        "    error_std = np.std(Y - Y_pred, ddof=1)\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(f\"R² del modelo: {R2:.2f}\")\n",
        "    print(f\"Error estándar de los residuos: {error_std:.2f}\")\n",
        "\n",
        "    # 🔹 Gráfica\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(X, Y, color='blue', alpha=0.6, label='Datos simulados')\n",
        "    plt.plot(X, Y_pred, color='red', label='Recta de regresión ajustada')\n",
        "    plt.xlabel('Variable X')\n",
        "    plt.ylabel('Variable Y')\n",
        "    plt.title('Evaluación del Modelo: R² y Error Estándar')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Evaluar ajuste de modelos predictivos, como predicción de ventas o rendimientos.\")\n",
        "    print(\"- R² indica qué proporción de la variabilidad se explica por el modelo.\")\n",
        "    print(\"- Error estándar mide la precisión de las predicciones y volatilidad de datos.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    evaluacion_modelo,\n",
        "    n=IntSlider(value=50, min=10, max=200, step=5, description='N° Observaciones'),\n",
        "    pendiente=FloatSlider(value=1, min=-5, max=5, step=0.1, description='Pendiente'),\n",
        "    intercepto=FloatSlider(value=0, min=-50, max=50, step=1, description='Intercepto'),\n",
        "    ruido=FloatSlider(value=10, min=0, max=50, step=1, description='Ruido')\n",
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