diff --git a/Fisica Estadistica/Trabajo_CPSs.ipynb b/Fisica Estadistica/Trabajo_CPSs.ipynb
index 141d307c91c4b56ec2804619277e458b9e21c2c0..6f259254f1889a183a2a7028311762c914663aa9 100644
--- a/Fisica Estadistica/Trabajo_CPSs.ipynb	
+++ b/Fisica Estadistica/Trabajo_CPSs.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,14 +11,53 @@
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Potencia media en una batería"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "69.25862894736841"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Función de valor medio de potencia\n",
+    "def potencia_media(v0, R, p, n):\n",
+    "    return (v0**2 / R * p * n) * (1 - p + p*n)\n",
+    "\n",
+    "# Calculamos la potencia media\n",
+    "potencia_media(2.3, 38, 0.89, 25) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trabajo realizado a partir de datos tomados"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -35,8 +74,9 @@
     "sistema4 = \"https://raw.githubusercontent.com/davidalejandromiranda/StatisticalPhysics/main/data/S5_Sistema_4.csv\"\n",
     "sistema5 = \"https://raw.githubusercontent.com/davidalejandromiranda/StatisticalPhysics/main/data/S5_Sistema_5.csv\"\n",
     "\n",
+    "sistema = sistema4\n",
     "# Descargar datos V en cm^3 y P en Pa\n",
-    "data = pd.read_csv(sistema3, names=(\"V\",\"P\"), skiprows=1)\n",
+    "data = pd.read_csv(sistema, names=(\"V\",\"P\"), skiprows=1)\n",
     "\n",
     "# Aproximar la integral\n",
     "integral = round(np.trapz(data.P, data.V))\n",
@@ -46,10 +86,246 @@
     "plt.fill_between(data.V, data.P, alpha=0.2)\n",
     "plt.xlabel(\"Volumen ($cm^3$)\")\n",
     "plt.ylabel(\"Presión ($Pa$)\")\n",
-    "plt.title(\"Integral de $P(V)$ = \" + str(integral)+ \" $\\mu J$\")\n",
+    "plt.title(\"Integral de $P(V)$ = \" + str(integral)+ \" $\\mu J$, Sistema \" + sistema[-5])\n",
     "plt.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Número de dados D y número de caras C para una estadística dada"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>d1</th>\n",
+       "      <th>d2</th>\n",
+       "      <th>d3</th>\n",
+       "      <th>d4</th>\n",
+       "      <th>d5</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.0000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>5000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.987400</td>\n",
+       "      <td>4.947800</td>\n",
+       "      <td>5.029200</td>\n",
+       "      <td>5.0196</td>\n",
+       "      <td>4.956600</td>\n",
+       "      <td>24.940600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.558232</td>\n",
+       "      <td>2.563667</td>\n",
+       "      <td>2.573727</td>\n",
+       "      <td>2.5594</td>\n",
+       "      <td>2.601859</td>\n",
+       "      <td>5.644523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.0000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.0000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>43.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                d1           d2           d3         d4           d5  \\\n",
+       "count  5000.000000  5000.000000  5000.000000  5000.0000  5000.000000   \n",
+       "mean      4.987400     4.947800     5.029200     5.0196     4.956600   \n",
+       "std       2.558232     2.563667     2.573727     2.5594     2.601859   \n",
+       "min       1.000000     1.000000     1.000000     1.0000     1.000000   \n",
+       "25%       3.000000     3.000000     3.000000     3.0000     3.000000   \n",
+       "50%       5.000000     5.000000     5.000000     5.0000     5.000000   \n",
+       "75%       7.000000     7.000000     7.000000     7.0000     7.000000   \n",
+       "max       9.000000     9.000000     9.000000     9.0000     9.000000   \n",
+       "\n",
+       "                 y  \n",
+       "count  5000.000000  \n",
+       "mean     24.940600  \n",
+       "std       5.644523  \n",
+       "min       7.000000  \n",
+       "25%      21.000000  \n",
+       "50%      25.000000  \n",
+       "75%      29.000000  \n",
+       "max      43.000000  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtenemos la tabla para D datos y C caras\n",
+    "D = 5 # Número de dados\n",
+    "C = 9 # Número de caras de un dado\n",
+    "N = 5000 # Número de experimentos: sistemas en el ensamble\n",
+    "\n",
+    "p = np.random.randint(low=1, high=C+1, size=N)\n",
+    "\n",
+    "ensamble_dict = {}\n",
+    "for d in range(D):\n",
+    "    key = 'd%d' % (d+1)\n",
+    "    values = np.random.randint(low=1, high=C+1, size=N)\n",
+    "    ensamble_dict[key] = list(values)\n",
+    "ensamble = pd.DataFrame(ensamble_dict)\n",
+    "ensamble['y'] = ensamble.sum(axis=1)\n",
+    "ensamble.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cambio en entropía de un sistema de agua y un reservorio térmico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "179.01730659629322"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Función que calcula el cambio de entropía en función de la masa y las temperaturas iniciales y finales\n",
+    "def cambio_entropia(m, T, T_prima):\n",
+    "    # Masa en kg, temperaturas en Celsius\n",
+    "    T = T + 273.15\n",
+    "    T_prima = T_prima + 273.15\n",
+    "    return m*1e3 * 4.1813 * (np.log(T_prima/T) - (T_prima-T)/T_prima) \n",
+    "\n",
+    "# Calculamos el cambio de entropía\n",
+    "cambio_entropia(1.18, 0, 89)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Temperatura del agua en un calorímetro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "287.0045141538891"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Función que calcula la temperatura final\n",
+    "def temperatura_final(mc,ma,mh,T0):\n",
+    "    # Masas en g, temperaturas en Celsius\n",
+    "    T0 = T0 + 273.15\n",
+    "    cc = 0.418\n",
+    "    ca = 4.1813\n",
+    "    cf = 333\n",
+    "    E0 = mc * cc * T0 + ma * ca * T0 - mh * cf\n",
+    "    return E0 / (mc * cc + (ma + mh) * ca) # Temperatura final en Kelvin\n",
+    "\n",
+    "# Calculamos la temperatura final\n",
+    "temperatura_final(750, 226, 19, 37)"
+   ]
   }
  ],
  "metadata": {