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            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Coeficiente de correlación (r): 0.94\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
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                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Analizar relación entre variables como inversión y rendimiento.\n",
                  "- Ajustar pendiente e intercepto ayuda a entender sensibilidad y efecto base.\n",
                  "- Ruido permite simular volatilidad o incertidumbre en los datos financieros.\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>simulador_regresion_lineal</b><br/>def simulador_regresion_lineal(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_6330/3880248255.py</a>Simula datos de regresión lineal y muestra relación entre variables.\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 aleatorio</pre></div>"
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          },
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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 regresión lineal\n",
        "def simulador_regresion_lineal(n=50, pendiente=1, intercepto=0, ruido=10):\n",
        "    \"\"\"\n",
        "    Simula datos de regresión lineal y muestra relación entre variables.\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 aleatorio\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar variable independiente X\n",
        "    X = np.linspace(0, 100, n)\n",
        "\n",
        "    # 🔹 Generar variable dependiente Y con ruido\n",
        "    Y = intercepto + pendiente*X + np.random.normal(0, ruido, n)\n",
        "\n",
        "    # 🔹 Calcular coeficiente de correlación\n",
        "    r = np.corrcoef(X, Y)[0,1]\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(f\"Coeficiente de correlación (r): {r:.2f}\")\n",
        "\n",
        "    # 🔹 Gráfica de datos y recta de regresión\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(X, Y, color='blue', alpha=0.6, label='Datos simulados')\n",
        "    # Recta de regresión ajustada por mínimos cuadrados\n",
        "    pendiente_ajustada, intercepto_ajustada = np.polyfit(X, Y, 1)\n",
        "    plt.plot(X, pendiente_ajustada*X + intercepto_ajustada, color='red', label='Recta de regresión')\n",
        "    plt.xlabel('Variable X')\n",
        "    plt.ylabel('Variable Y')\n",
        "    plt.title('Simulación de Regresión Lineal')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Analizar relación entre variables como inversión y rendimiento.\")\n",
        "    print(\"- Ajustar pendiente e intercepto ayuda a entender sensibilidad y efecto base.\")\n",
        "    print(\"- Ruido permite simular volatilidad o incertidumbre en los datos financieros.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_regresion_lineal,\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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