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              {
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                "name": "stdout",
                "text": [
                  "N° de outliers detectados: 1\n",
                  "Indices de outliers: [49]\n"
                ]
              },
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                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
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                "text": [
                  "\n",
                  "💡 Aplicación financiera:\n",
                  "- Identificar valores extremos en rendimientos, ventas o costos.\n",
                  "- Outliers pueden distorsionar regresiones y análisis de riesgo.\n",
                  "- Permite decidir si se corrigen, eliminan o ajustan los datos.\n"
                ]
              }
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              "<function __main__.simulador_outliers(n=50, pendiente=1, intercepto=0, ruido=10, metodo='Desviación estándar', factor=2)>"
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simulador_outliers</b><br/>def simulador_outliers(n=50, pendiente=1, intercepto=0, ruido=10, metodo=&#x27;Desviación estándar&#x27;, factor=2)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_2858/420773060.py</a>Simula datos con posibilidad de outliers y permite detectarlos.\n",
              "\n",
              "Parámetros:\n",
              "- n: número de observaciones\n",
              "- pendiente: pendiente de la recta\n",
              "- intercepto: intercepto\n",
              "- ruido: desviación estándar\n",
              "- metodo: &#x27;Desviación estándar&#x27; o &#x27;IQR&#x27;\n",
              "- factor: número de desviaciones o rango intercuartílico para detección</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, Dropdown\n",
        "\n",
        "# 🔹 Función simuladora de detección de outliers\n",
        "def simulador_outliers(n=50, pendiente=1, intercepto=0, ruido=10, metodo='Desviación estándar', factor=2):\n",
        "    \"\"\"\n",
        "    Simula datos con posibilidad de outliers y permite detectarlos.\n",
        "\n",
        "    Parámetros:\n",
        "    - n: número de observaciones\n",
        "    - pendiente: pendiente de la recta\n",
        "    - intercepto: intercepto\n",
        "    - ruido: desviación estándar\n",
        "    - metodo: 'Desviación estándar' o 'IQR'\n",
        "    - factor: número de desviaciones o rango intercuartílico para detección\n",
        "    \"\"\"\n",
        "\n",
        "    # 🔹 Generar datos\n",
        "    X = np.linspace(0, 100, n)\n",
        "    Y = intercepto + pendiente*X + np.random.normal(0, ruido, n)\n",
        "\n",
        "    # 🔹 Introducir un outlier extremo para ejemplo\n",
        "    Y[-1] += 3*ruido\n",
        "\n",
        "    # 🔹 Detectar outliers\n",
        "    if metodo == 'Desviación estándar':\n",
        "        mean_y = np.mean(Y)\n",
        "        std_y = np.std(Y, ddof=1)\n",
        "        outliers = np.where(np.abs(Y - mean_y) > factor*std_y)[0]\n",
        "    elif metodo == 'IQR':\n",
        "        Q1 = np.percentile(Y, 25)\n",
        "        Q3 = np.percentile(Y, 75)\n",
        "        IQR = Q3 - Q1\n",
        "        outliers = np.where((Y < Q1 - factor*IQR) | (Y > Q3 + factor*IQR))[0]\n",
        "\n",
        "    # 🔹 Mostrar resultados\n",
        "    print(f\"N° de outliers detectados: {len(outliers)}\")\n",
        "    if len(outliers) > 0:\n",
        "        print(f\"Indices de outliers: {outliers}\")\n",
        "\n",
        "    # 🔹 Gráfica\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(X, Y, color='blue', alpha=0.6, label='Datos')\n",
        "    if len(outliers) > 0:\n",
        "        plt.scatter(X[outliers], Y[outliers], color='red', s=100, label='Outliers', edgecolor='k')\n",
        "    plt.xlabel('Variable X')\n",
        "    plt.ylabel('Variable Y')\n",
        "    plt.title('Detección de Outliers')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n💡 Aplicación financiera:\")\n",
        "    print(\"- Identificar valores extremos en rendimientos, ventas o costos.\")\n",
        "    print(\"- Outliers pueden distorsionar regresiones y análisis de riesgo.\")\n",
        "    print(\"- Permite decidir si se corrigen, eliminan o ajustan los datos.\")\n",
        "\n",
        "# 🔹 Interactividad\n",
        "interact(\n",
        "    simulador_outliers,\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",
        "    metodo=Dropdown(options=['Desviación estándar', 'IQR'], description='Método'),\n",
        "    factor=FloatSlider(value=2, min=1, max=5, step=0.1, description='Factor')\n",
        ")"
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