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    "colab": {
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    "kernelspec": {
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            "layout": "IPY_MODEL_456d718006cc4fc4b1eeff87c4c10c79",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1400x500 with 2 Axes>",
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                  "Media de los datos: -0.02\n",
                  "Desviación estándar de los datos: 5.93\n",
                  "Media de Z: -0.00\n",
                  "Desviación estándar de Z: 1.02\n"
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              "<function __main__.simular_normal(media=0, desviacion=1, n=1000)>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>simular_normal</b><br/>def simular_normal(media=0, desviacion=1, n=1000)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/tmp/ipykernel_3745/3420200444.py</a>Esta función genera una simulación de datos con distribución normal\n",
              "y muestra la curva de la distribución normal y su transformación Z.\n",
              "\n",
              "Parámetros:\n",
              "- media: valor de la media de la distribución\n",
              "- desviacion: valor de la desviación estándar\n",
              "- n: número de datos a simular (convertido a entero)</pre></div>"
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        "# 📦 Importamos las librerías necesarias\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from ipywidgets import interact, FloatSlider, IntSlider\n",
        "\n",
        "# 🔹 Configuración para que los gráficos se vean dentro de Colab\n",
        "%matplotlib inline\n",
        "\n",
        "# Función para graficar la distribución normal y su versión estandarizada (Z)\n",
        "def simular_normal(media=0, desviacion=1, n=1000):\n",
        "    \"\"\"\n",
        "    Esta función genera una simulación de datos con distribución normal\n",
        "    y muestra la curva de la distribución normal y su transformación Z.\n",
        "\n",
        "    Parámetros:\n",
        "    - media: valor de la media de la distribución\n",
        "    - desviacion: valor de la desviación estándar\n",
        "    - n: número de datos a simular (convertido a entero)\n",
        "    \"\"\"\n",
        "\n",
        "    n = int(n)  # 🔹 Convertimos n a entero por seguridad\n",
        "\n",
        "    # 🔹 Generamos datos aleatorios con distribución normal\n",
        "    datos = np.random.normal(loc=media, scale=desviacion, size=n)\n",
        "\n",
        "    # 🔹 Calculamos la distribución Z\n",
        "    z_scores = (datos - media) / desviacion\n",
        "\n",
        "    # 🔹 Creamos la figura con dos subplots\n",
        "    fig, axs = plt.subplots(1, 2, figsize=(14,5))\n",
        "\n",
        "    # 🔹 Gráfico 1: Distribución normal\n",
        "    axs[0].hist(datos, bins=30, density=True, color='skyblue', edgecolor='black')\n",
        "    axs[0].set_title(f'Distribución Normal (μ={media}, σ={desviacion})')\n",
        "    axs[0].set_xlabel('Valores')\n",
        "    axs[0].set_ylabel('Densidad')\n",
        "\n",
        "    # 🔹 Gráfico 2: Distribución Z (estandarizada)\n",
        "    axs[1].hist(z_scores, bins=30, density=True, color='lightcoral', edgecolor='black')\n",
        "    axs[1].set_title('Distribución Z (Estandarizada)')\n",
        "    axs[1].set_xlabel('Valores Z')\n",
        "    axs[1].set_ylabel('Densidad')\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "    # 🔹 Mostrar información básica\n",
        "    print(f\"Media de los datos: {np.mean(datos):.2f}\")\n",
        "    print(f\"Desviación estándar de los datos: {np.std(datos):.2f}\")\n",
        "    print(f\"Media de Z: {np.mean(z_scores):.2f}\")\n",
        "    print(f\"Desviación estándar de Z: {np.std(z_scores):.2f}\")\n",
        "\n",
        "# 🔹 Creamos los sliders interactivos para controlar media, desviación estándar y cantidad de datos\n",
        "interact(\n",
        "    simular_normal,\n",
        "    media=FloatSlider(value=0, min=-10, max=10, step=0.5, description='Media (μ)'),\n",
        "    desviacion=FloatSlider(value=1, min=0.1, max=10, step=0.1, description='Desviación (σ)'),\n",
        "    n=IntSlider(value=1000, min=100, max=5000, step=100, description='N° Datos')\n",
        ")"
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