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"msg_id": "", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "=== Pruebas No Paramétricas ===\n", "Mann-Whitney U (Grupo 1 vs 2): estadístico=297.00, p-valor=0.024\n", "Kruskal-Wallis (Grupos 1,2,3): estadístico=18.15, p-valor=0.000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_12500/1107011594.py:36: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", " plt.boxplot([grupo1, grupo2, grupo3], labels=['Grupo 1', 'Grupo 2', 'Grupo 3'], patch_artist=True)\n" ] }, { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "💡 Aplicación financiera:\n", "- Comparar rendimiento de portafolios o ventas por región sin asumir normalidad.\n", "- Mann-Whitney U sirve para comparar dos grupos.\n", "- Kruskal-Wallis permite comparar más de dos grupos y detectar diferencias significativas.\n" ] } ] } }, "c9d438f3faa1465680928fc2b3500707": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, 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comparar_grupos
def comparar_grupos(n1=30, n2=30, n3=30, med1=50, med2=55, med3=60, ruido=10)
/tmp/ipykernel_12500/1107011594.pyCompara grupos usando Mann-Whitney (dos grupos) y Kruskal-Wallis (tres grupos).\n",
              "\n",
              "Parámetros:\n",
              "- n1, n2, n3: tamaño de los grupos\n",
              "- med1, med2, med3: medianas de los grupos\n",
              "- ruido: desviación estándar del ruido
" ] }, "metadata": {}, "execution_count": 1 } ], "source": [ "# 📦 Librerías necesarias\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import mannwhitneyu, kruskal\n", "from ipywidgets import interact, IntSlider, FloatSlider\n", "\n", "# 🔹 Función simuladora de pruebas no paramétricas\n", "def comparar_grupos(n1=30, n2=30, n3=30, med1=50, med2=55, med3=60, ruido=10):\n", " \"\"\"\n", " Compara grupos usando Mann-Whitney (dos grupos) y Kruskal-Wallis (tres grupos).\n", "\n", " Parámetros:\n", " - n1, n2, n3: tamaño de los grupos\n", " - med1, med2, med3: medianas de los grupos\n", " - ruido: desviación estándar del ruido\n", " \"\"\"\n", "\n", " # 🔹 Generar datos\n", " grupo1 = np.random.normal(loc=med1, scale=ruido, size=n1)\n", " grupo2 = np.random.normal(loc=med2, scale=ruido, size=n2)\n", " grupo3 = np.random.normal(loc=med3, scale=ruido, size=n3)\n", "\n", " # 🔹 Mann-Whitney U (grupo1 vs grupo2)\n", " stat_mw, p_mw = mannwhitneyu(grupo1, grupo2, alternative='two-sided')\n", "\n", " # 🔹 Kruskal-Wallis (grupo1 vs grupo2 vs grupo3)\n", " stat_kw, p_kw = kruskal(grupo1, grupo2, grupo3)\n", "\n", " # 🔹 Mostrar resultados\n", " print(\"=== Pruebas No Paramétricas ===\")\n", " print(f\"Mann-Whitney U (Grupo 1 vs 2): estadístico={stat_mw:.2f}, p-valor={p_mw:.3f}\")\n", " print(f\"Kruskal-Wallis (Grupos 1,2,3): estadístico={stat_kw:.2f}, p-valor={p_kw:.3f}\")\n", "\n", " # 🔹 Visualización\n", " plt.figure(figsize=(10,6))\n", " plt.boxplot([grupo1, grupo2, grupo3], labels=['Grupo 1', 'Grupo 2', 'Grupo 3'], patch_artist=True)\n", " plt.ylabel('Valores')\n", " plt.title('Comparación de Grupos (No Paramétrica)')\n", " plt.show()\n", "\n", " print(\"\\n💡 Aplicación financiera:\")\n", " print(\"- Comparar rendimiento de portafolios o ventas por región sin asumir normalidad.\")\n", " print(\"- Mann-Whitney U sirve para comparar dos grupos.\")\n", " print(\"- Kruskal-Wallis permite comparar más de dos grupos y detectar diferencias significativas.\")\n", "\n", "# 🔹 Interactividad\n", "interact(\n", " comparar_grupos,\n", " n1=IntSlider(value=30, min=5, max=100, step=1, description='Tamaño G1'),\n", " n2=IntSlider(value=30, min=5, max=100, step=1, description='Tamaño G2'),\n", " n3=IntSlider(value=30, min=5, max=100, step=1, description='Tamaño G3'),\n", " med1=FloatSlider(value=50, min=0, max=100, step=1, description='Mediana G1'),\n", " med2=FloatSlider(value=55, min=0, max=100, step=1, description='Mediana G2'),\n", " med3=FloatSlider(value=60, min=0, max=100, step=1, description='Mediana G3'),\n", " ruido=FloatSlider(value=10, min=0, max=50, step=1, description='Ruido')\n", ")" ] } ] }