diff --git a/Practica1_Probabilidad.ipynb b/Practica1_Probabilidad.ipynb
index 00ce62d4d5166d5ce7680ed0e67dfaf3b80fd1cb..46aa9302634b5d80891801bd8b9f93bdd1a81f24 100644
--- a/Practica1_Probabilidad.ipynb
+++ b/Practica1_Probabilidad.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 1,
    "id": "administrative-black",
    "metadata": {},
    "outputs": [],
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 2,
    "id": "inappropriate-cleanup",
    "metadata": {},
    "outputs": [
@@ -74,13 +74,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Al lanzar la moneda 10 veces, la proporcion de vece que obtuvimos cara:  0.4\n",
+      "Al lanzar la moneda 10 veces, la proporcion de vece que obtuvimos cara:  0.5\n",
       "\n",
-      "Al lanzar la moneda 100 veces, la proporcion de vece que obtuvimos cara:  0.57\n",
+      "Al lanzar la moneda 100 veces, la proporcion de vece que obtuvimos cara:  0.5\n",
       "\n",
-      "Al lanzar la moneda 1000 veces, la proporcion de vece que obtuvimos cara:  0.497\n",
+      "Al lanzar la moneda 1000 veces, la proporcion de vece que obtuvimos cara:  0.517\n",
       "\n",
-      "Al lanzar la moneda 1000000 veces, la proporcion de vece que obtuvimos cara:  0.500076\n"
+      "Al lanzar la moneda 1000000 veces, la proporcion de vece que obtuvimos cara:  0.499648\n"
      ]
     }
    ],
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 3,
    "id": "metric-lemon",
    "metadata": {},
    "outputs": [],
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 4,
    "id": "resident-small",
    "metadata": {},
    "outputs": [
@@ -136,18 +136,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Con 10 experimentos, valor medio: 0.49 desviación estandar: 0.15238839267549947\n",
+      "Con 10 experimentos, valor medio: 0.5 desviación estandar: 0.1247219128924647\n",
       "\n",
-      "Con 100 experimentos, valor medio: 0.498 desviación estandar: 0.16328694455658804\n",
+      "Con 100 experimentos, valor medio: 0.506 desviación estandar: 0.14129273287623947\n",
       "\n",
-      "Con 1000 experimentos, valor medio: 0.4959 desviación estandar: 0.16012129411427084\n",
+      "Con 1000 experimentos, valor medio: 0.5046 desviación estandar: 0.16007643319509735\n",
       "\n",
-      "Con 100000 experimentos, valor medio: 0.499687 desviación estandar: 0.15796471618534183\n"
+      "Con 100000 experimentos, valor medio: 0.500633 desviación estandar: 0.15795932648149721\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 4 Axes>"
       ]
@@ -210,6 +210,42 @@
     "- ¿Como simulamos el resultado de lanzar dos monedas? E.g probabilidad de tener dos caras."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "norwegian-channels",
+   "metadata": {},
+   "source": [
+    "## Ensayos de Bernulli\n",
+    "\n",
+    "Lanzar una moneda tiene dos resultados posibles. Este sencillo experimento, denominado ensayo de Bernoulli, se modela mediante una variable aleatoria denominada Bernoulli. Entender este bloque de construcción nos puede llevar lejos. Podemos utilizarlo para construir modelos más complejos.\n",
+    "\n",
+    "Python tiene su propia librerias para simular ensayos de Bernulli dato un numero de lanzamientos y una probabilidad. \n",
+    "Para obtener el resultado de 15 ensayos Bernoulli con una probabilidad de éxito igual a 0,5 (una moneda justa), escribimos\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "interior-alarm",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import bernoulli\n",
+    "bernoulli.rvs(size=15,p=0.5)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "square-entry",
@@ -265,21 +301,13 @@
     "    - Ahora la lanzamos y nos sale sello. Con esta información, ¿cual es la probabilidad que sea una moneda truncada? \n",
     "    "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "tender-spell",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "LA-CoNGA Stats Env",
+   "display_name": "clase13datos",
    "language": "python",
-   "name": "laconga_stats"
+   "name": "clase13datos"
   },
   "language_info": {
    "codemirror_mode": {
@@ -291,7 +319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index e3f05361aa34806837338bd6003eb9fca3fd53c7..6fea568f643c34dc5bc7752c75182b9f2e265dae 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ jupyterlab
 notebook
 ipykernel
 matplotlib
+scipy