diff --git a/.gitignore b/.gitignore
index 03fc1690..8d193625 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,2 @@
-
-   
-# Checkpoints will not be pushed.
-.ipynb_checkpoints
+.venv
+.env
diff --git a/notebook/problems.ipynb b/notebook/problems.ipynb
index a253f320..4ab23a3b 100644
--- a/notebook/problems.ipynb
+++ b/notebook/problems.ipynb
@@ -24,12 +24,55 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": 2,
             "id": "34720ab6",
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Distribucion Normal\n",
+                        "Media: -0.0633\n",
+                        "Mediana: -0.0275\n",
+                        "Desviación estándar: 0.9305\n",
+                        "Varianza: 0.8659\n",
+                        "Mínimo: -2.9169\n",
+                        "Máximo: 2.0706\n",
+                        "None\n",
+                        "\n",
+                        "Distribucion Chi-Cuadrado\n",
+                        "Media: 2.8621\n",
+                        "Mediana: 2.0939\n",
+                        "Desviación estándar: 2.4748\n",
+                        "Varianza: 6.1247\n",
+                        "Mínimo: 0.0704\n",
+                        "Máximo: 13.2238\n",
+                        "None\n"
+                    ]
+                }
+            ],
             "source": [
-                "# TODO"
+                "import numpy as np\n",
+                "\n",
+                "# Generación de datos con distribuciones\n",
+                "distribucion_normal = np.random.normal(size=100)\n",
+                "distribucion_chi_square = np.random.chisquare(df=3, size=100)\n",
+                "\n",
+                "# Cálculo de Medidas de tendencia \n",
+                "def medidas_estadisticas(datos):\n",
+                "    #print(f\"\\nEstadísticas para {name}:\")\n",
+                "    print(f\"Media: {np.mean(datos):.4f}\")\n",
+                "    print(f\"Mediana: {np.median(datos):.4f}\")\n",
+                "    print(f\"Desviación estándar: {np.std(datos):.4f}\")\n",
+                "    print(f\"Varianza: {np.var(datos):.4f}\")\n",
+                "    print(f\"Mínimo: {np.min(datos):.4f}\")\n",
+                "    print(f\"Máximo: {np.max(datos):.4f}\")\n",
+                "\n",
+                "print(\"Distribucion Normal\")\n",
+                "print(medidas_estadisticas(distribucion_normal))\n",
+                "print(\"\\nDistribucion Chi-Cuadrado\")\n",
+                "print(medidas_estadisticas(distribucion_chi_square))"
             ]
         },
         {
@@ -48,21 +91,52 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": 5,
             "id": "d590308e",
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Desviación estándar con funcion: 2.00\n",
+                        "Desviación estándar con metodo np.std: 2.00\n"
+                    ]
+                }
+            ],
             "source": [
-                "# TODO"
+                "datos = [4, 2, 5, 8, 6]\n",
+                "\n",
+                "#Calculo de la media\n",
+                "def media(data):\n",
+                "    n = len(data)\n",
+                "    return sum(data)/n\n",
+                "\n",
+                "#Funcion para calcular Desviacion Estandar\n",
+                "def desviacion_std(data):\n",
+                "    promedio = media(data)\n",
+                "\n",
+                "    #Calculo de varianza\n",
+                "    resta_al_cuadrado = [(x - promedio) ** 2 for x in datos]\n",
+                "    varianza = sum(resta_al_cuadrado)/len(data)\n",
+                "    \n",
+                "    #Cálculo de la desviación estándar\n",
+                "    desviacion = np.sqrt(varianza)\n",
+                "\n",
+                "    return desviacion\n",
+                "\n",
+                "print(f\"Desviación estándar con funcion: {desviacion_std(datos):.2f}\") \n",
+                "\n",
+                "def desviacion_estándar(datos):\n",
+                "    return np.std(datos, ddof=0) \n",
+                "\n",
+                "print(f\"Desviación estándar con metodo np.std: {desviacion_estándar(datos):.2f}\") "
             ]
         }
     ],
     "metadata": {
-        "interpreter": {
-            "hash": "9248718ffe6ce6938b217e69dbcc175ea21f4c6b28a317e96c05334edae734bb"
-        },
         "kernelspec": {
-            "display_name": "Python 3.9.12 ('ML-BOOTCAMP')",
+            "display_name": ".venv",
             "language": "python",
             "name": "python3"
         },