diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 31ff6c5..e151f7b 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,5 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import binom\n",
+    "from scipy.stats import poisson\n",
+    "import math\n",
+    "import seaborn as sns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,17 +47,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of picking an apple: 0.60\n",
+      "Probability of picking an orange: 0.40\n"
+     ]
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Calculate:\n",
     "p = probability that the fruit is an apple \n",
     "q = probability that the fruit is an orange\n",
+    "\n",
     "\"\"\"\n",
+    "total_fruits = 100\n",
+    "\n",
+    "# Number of apples\n",
+    "apples = 60\n",
     "\n",
-    "# your code here"
+    "# Number of oranges\n",
+    "oranges = 40\n",
+    "\n",
+    "p = apples / total_fruits\n",
+    "\n",
+    "q = oranges / total_fruits\n",
+    "\n",
+    "print(f\"Probability of picking an apple: {p:.2f}\")\n",
+    "print(f\"Probability of picking an orange: {q:.2f}\")\n"
    ]
   },
   {
@@ -61,11 +97,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of the first 5 fruits being apples: 0.077760\n",
+      "Probability of 5 apples followed by 15 oranges: 0.0000000835\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# Probability of picking an apple (success)\n",
+    "p = 0.60\n",
+    "\n",
+    "# Probability of picking an orange (failure)\n",
+    "q = 0.40\n",
+    "\n",
+    "# Probability that the first 5 fruits are all apples\n",
+    "probability_5_apples = p ** 5\n",
+    "\n",
+    "# Probability that the first 5 fruits are all apples and the next 15 fruits are all oranges\n",
+    "probability_5_apples_15_oranges = probability_5_apples * (q ** 15)\n",
+    "\n",
+    "print(f\"Probability of the first 5 fruits being apples: {probability_5_apples:.6f}\")\n",
+    "print(f\"Probability of 5 apples followed by 15 oranges: {probability_5_apples_15_oranges:.10f}\")\n"
    ]
   },
   {
@@ -83,11 +141,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of getting 5 apples and 15 oranges: 0.001294\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# Number of trials\n",
+    "n = 20\n",
+    "\n",
+    "# Probability of success\n",
+    "p = 0.6\n",
+    "\n",
+    "# Number of successes\n",
+    "k = 5\n",
+    "\n",
+    "# Calculate the probability using the binom.pmf function\n",
+    "probability = binom.pmf(k, n, p)\n",
+    "\n",
+    "print(f\"Probability of getting 5 apples and 15 oranges: {probability:.6f}\")\n",
+    "\n"
    ]
   },
   {
@@ -101,11 +180,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of picking fewer than 5 apples: 0.000317\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "probability = binom.cdf(4, n, p)\n",
+    "\n",
+    "print(f\"Probability of picking fewer than 5 apples: {probability:.6f}\")\n"
    ]
   },
   {
@@ -119,12 +208,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot "
+    "# Generate the x values from 0 to n (inclusive)\n",
+    "x = np.arange(0, n + 1)\n",
+    "\n",
+    "# Calculate the PDF values using the binom.pmf function\n",
+    "pdf_values = binom.pmf(x, n, p)\n",
+    "\n",
+    "# Plot the PDF\n",
+    "plt.bar(x, pdf_values, align='center', alpha=0.5)ggggg\n",
+    "plt.xticks(x)\n",
+    "plt.xlabel('Number of Apples')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Binomial Distribution PDF')\n",
+    "plt.grid(axis='y', linestyle='-', alpha=0.7)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -144,11 +257,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of scoring 5 goals in a match: 0.0538\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here "
+    "# Average number of goals in a match\n",
+    "avg_number_goals = 2.3\n",
+    "\n",
+    "# Specific number of goals to calculate the probability for\n",
+    "k = 5\n",
+    "\n",
+    "# Calculate the probability using the Poisson probability formula\n",
+    "probability = poisson.pmf(k, avg_number_goals)\n",
+    "\n",
+    "print(f\"Probability of scoring 5 goals in a match: {probability:.4f}\")"
    ]
   },
   {
@@ -160,20 +290,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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//IDOnTsrDie5uLjg2WefxR9//IGjR48qxo8fP15xzlTPnj0BoNLbQ1kXLlxASkoKxo0bpzhc0q5dOzzwwAMWa791W7SmoKBAXtZlPfnkk/L26OPjI58kXJXtTa/XyyeSG41G5ObmwsXFBa1atTI71F6Wk5MTdDoddu3aZXZouiK5ubkWz+UpKiqyeg6bo6MjioqKqvQ+pW7nc7oqSvOzdG6atfw9PDxQVFSEa9eu2SSHuoIN0B1i5cqVSEhIwM6dO3H06FGkpaUhKioKAPDnn3/CwcEBLVq0ULzG398f7u7u+PPPP61O95VXXoGLiws6d+6Mli1bIjo62mxX6KJFi5CamoqmTZuic+fOmD17tuLDuXT6ls4lCQ0NNXt/R0dHs8MHHh4elf4ACwoKQkJCArZv346ff/4ZmZmZ+P777+XdzaVu/WLPzs7GtWvXLOYZFhYGk8mEc+fOKeItW7ZUPHZxcUHjxo0V5xccOXIE//jHP+Dm5gZXV1f4+PjIu8jz8/MVrw8ICDA7ybT0sJGtL18va8yYMSgqKpJ3s584cQIHDx7Ek08+edvTLr20tlGjRlbHVGY9q0hVG7Vbl13z5s3h4OBQo/MZuLk9WFvHSp8v66677lI8Lv1SruoXetlpW3v/nJwcFBYWKuKVna+NGjUyu4wauHmFakJCAhISEhTxqmxvJpMJS5YsQcuWLaHX6+Ht7Q0fHx8cPnzYbBsqS6/XY+HChfjxxx/h5+eHXr16YdGiRcjMzKxUTcLCIT8nJycYDAaL469fv17tk7xv53O6Kkrzs3RekbX8S+fDnXYVGBugO0Tnzp0RGRmJPn36ICwszOJlt9VZecPCwnDixAl89dVX6NGjB/7973+jR48eivNChg8fjrS0NKxYsQIBAQF466230KZNG8XllFWh0Wiq9bpSzs7OiIyMRL9+/dC9e3f4+vpaHFcbV6NcvnwZvXv3xm+//Ya5c+fiu+++Q0JCAhYuXAgAdebeRK1bt0bHjh3x2WefAQA+++wz6HQ6DB8+/LannZqaCgBmH+xlVWY9q8jtLs9btw9r20t1T8avLmvbg6Uv55pQ2fkaGhqKEydOmJ1M365dO0RGRpqdTFwV8+fPR2xsLHr16oXPPvsMW7duRUJCAtq0aVPhNjRlyhScPHkS8fHxcHR0xOuvv46wsDCL57qU5eXlZbHJbNy4MYxGo3yVXSmDwYDc3Nxyz0eqjJpuMho3bgzg5t7AW124cMFi/nl5eWjYsGG9uYKvstgAqUCzZs1gMplw6tQpRTwrKwuXL1+u8OZozs7OGDFiBNauXYuzZ89i0KBBePPNNxVXzDRu3BgTJ07Epk2bkJ6eDi8vL7z55pvy+wM39yrc6sSJE7Vyc7bK8PHxQcOGDS3mefz4cTg4OKBp06aK+K3z9OrVq7hw4YJ8cuWuXbuQm5uLjz/+GJMnT8ZDDz2EyMhIq5fJZmRkmP0FXnr11O3eu6eiD9YxY8Zgx44duHDhAr744gsMGjTIJpfzfvrpp5AkqcK76Va0ntn6i+HWZXf69GmYTCZ5PpfWfusN6yz9JV6V3Jo1a2Z1HSt9vqaUty0eP34c3t7eVi9zr8hDDz2k2ItYkapsbxs2bMD999+Pf/3rXxg5ciT69++PyMjISt9MsHnz5njppZewbds2pKamwmAw4J133in3NaGhocjLyzPbw9S+fXsAMLsL/IEDB2AymeTnrbG2rtzu53Rl3XPPPdBqtWb5GwwGpKSkWMw/PT1d3kN5J2EDpAIPPvggAGDp0qWK+OLFiwHcPBfHmtzcXMVjnU6H1q1bQwiBGzduwGg0mn1A+Pr6IiAgQN7FGhERAV9fX6xevVqx2/XHH3/EsWPHyn3/2qTRaNC/f39s3rxZcRgkKysLX3zxBXr06KG4bBQA1qxZo/iLd9WqVSgpKcHAgQPlaQLKv9YNBgPee+89izmUlJQo7rpaehdWHx8fdOzY8bbqK/1is/alMWrUKEiShMmTJyMtLc3qlSxVsWDBAmzbtg0jRowwO+RUVkXrWWXyr6qVK1cqHpfeybd02bm6usLb2xs//fSTYpylZVeV3B588EHs27dPcaO6wsJCrFmzBkFBQVU6j6mqGjdujPbt22PdunWKXFNTU7Ft2zb5s6I6XnjhBfj5+WHq1KmKWx6UunWPVVW2N41GY/b6b775xuw2Gre6du2a4g814GYz1KhRowovLe/atSuEEGbn3vXt2xeenp5YtWqVIr5q1So0bNiwws8za+vK7XxOV4WbmxsiIyPx2WefKc7L+/TTT3H16lWL9w86dOgQunXrZpP3r0t4GbwKhIeHY+zYsVizZo18SGbfvn1Yt24dhg4danaX07L69+8Pf39/dO/eHX5+fjh27BjeffddDBo0CI0aNcLly5cRGBiIxx57DOHh4XBxccH27duxf/9++S+sBg0aYOHChRg/fjx69+6NUaNGyZfBBwUFYerUqbU1Kyr0xhtvICEhAT169MDEiROh1Wrx/vvvo7i4GIsWLTIbbzAY0K9fPwwfPhwnTpzAe++9hx49euDhhx8GAHTr1g0eHh4YO3YsJk2aBEmS8Omnn1o9fBEQEICFCxfijz/+wN13343169cjJSUFa9asqfDOzxVp3749NBoNFi5ciPz8fOj1evTt21c+ROjj44MBAwbgm2++gbu7e5U+cEtKSuTDZ9evX8eff/6Jb7/9FocPH8b999+PNWvWlPv6itYzAHID+M9//hMjR45EgwYNMHjw4GrvsUhPT8fDDz+MAQMGICkpCZ999hkef/xxhIeHy2OeeeYZLFiwAM888wwiIiLw008/Wfxyr0pur776Kr788ksMHDgQkyZNgqenJ9atW4f09HT8+9//rvG7Rr/11lsYOHAgunbtiqefflq+DN7Nzc3i/ZIqy9PTExs3bsTgwYMRHh6OkSNHolOnTmjQoAHOnTuHb775BoDynKbKbm8PPfQQ5s6di/Hjx6Nbt274/fff8fnnnytOnrbk5MmT8vbZunVraLVabNy4EVlZWRg5cmS5r+3Rowe8vLywfft2xQ0AnZycMG/ePERHR2PYsGGIiorC//73P3z22Wd48803K7wXj7V15XY+p0u98cYbAG6edwjcbGpKf37ktddek8e9+eab6NatG3r37o1nn30W58+fxzvvvIP+/ftjwIABimkePHgQly5dwpAhQyp8/3rHXpefkW2UXo69f//+csfduHFDzJkzRwQHB4sGDRqIpk2bihkzZiguwxbC/PLk999/X/Tq1Ut4eXkJvV4vmjdvLqZPny7y8/OFEDcv054+fboIDw8XjRo1Es7OziI8PFy89957ZjmsX79e3HvvvUKv1wtPT08xevRocf78ecUYa5dUW7rM25LevXtXeDm1EDcvY7Z26f6hQ4dEVFSUcHFxEQ0bNhT333+/2LNnj2JM6XzfvXu3ePbZZ4WHh4dwcXERo0ePVlxeLIQQv/zyi7jvvvuEk5OTCAgIEC+//LLYunWr2WWqpbkfOHBAdO3aVTg6OopmzZqJd999VzG96l4GL4QQH3zwgQgJCREajcbiZbJff/21ACCeffbZcuaeUumtC0r/NWzYUAQFBYlHH31UbNiwwezy5dJaq7KelZo3b55o0qSJcHBwUFxKXN7yhJXL4I8ePSoee+wx0ahRI+Hh4SFiYmIUt2kQ4uYtDJ5++mnh5uYmGjVqJIYPHy4uXrxoNs3ycrO0HM6cOSMee+wx4e7uLhwdHUXnzp3F999/rxhj7TL28i7Pr8zrhRBi+/btonv37sLJyUm4urqKwYMHi6NHjyrGlM6n7Ozsct/nVhcuXBDTp08XrVu3Fk5OTkKv14uQkBAxZswYi5deV2Z7u379unjppZdE48aNhZOTk+jevbtISkoyW49unTc5OTkiOjpahIaGCmdnZ+Hm5ia6dOkivv7660rVMmnSJNGiRQuLz61Zs0a0atVK6HQ60bx5c7FkyRJhMpkqNV1r60plP6etKbsd3vrvVv/73/9Et27dhKOjo/Dx8RHR0dGioKDAbNwrr7wi7rrrrkrXVp9IQtTSmXREVOdt3rwZQ4cOxU8//SRfbk2kVmlpaQgNDcWPP/6Ifv362TudWldcXIygoCC8+uqrmDx5sr3TsTk2QEQke+ihh3Ds2DGcPn36jrvklag6XnjhBZw+fdrsMn41WL16NebPn49Tp07Z7TftahIbICLCV199hcOHDyM+Ph7Lli3DpEmT7J0SEVGNYgNERJAkCS4uLhgxYgRWr14NrZbXRxDRnY2fckRUazfVIyKqK3gfICIiIlIdNkBERESkOjwEZoHJZEJGRgYaNWrEK2GIiIjqCSEErly5goCAgApvKsoGyIKMjAyz33wiIiKi+uHcuXMIDAwsdwwbIAtKb71/7tw5s99+IiIiorqpoKAATZs2lb/Hy8MGyILSw16urq5sgIiIiOqZypy+wpOgiYiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdrb0TILodSxJO2jsFTH3gbnunQEREVcQ9QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpTp1ogFauXImgoCA4OjqiS5cu2Ldvn9WxH3zwAXr27AkPDw94eHggMjLSbPy4ceMgSZLi34ABA2q6DCIiIqon7N4ArV+/HrGxsYiLi8OhQ4cQHh6OqKgoXLx40eL4Xbt2YdSoUdi5cyeSkpLQtGlT9O/fH3/99Zdi3IABA3DhwgX535dfflkb5RAREVE9YPcGaPHixZgwYQLGjx+P1q1bY/Xq1WjYsCE++ugji+M///xzTJw4Ee3bt0doaCg+/PBDmEwmJCYmKsbp9Xr4+/vL/zw8PGqjHCIiIqoHtPZ8c4PBgIMHD2LGjBlyzMHBAZGRkUhKSqrUNK5du4YbN27A09NTEd+1axd8fX3h4eGBvn374o033oCXl5fFaRQXF6O4uFh+XFBQAAAoKSlBSUmJnJeDgwNMJhNMJpMiXwcHBxiNRgghKoxrNBpIkiRPt2wcAIxGY6XiWq0WQghFXJIkaDQasxytxe+EmiAEJPydi4AESJLVuCT+nka144Bi2iUlJVxOrIk1sSbWVEdqqiy7NkA5OTkwGo3w8/NTxP38/HD8+PFKTeOVV15BQEAAIiMj5diAAQPwyCOPIDg4GGfOnMHMmTMxcOBAJCUlyQusrPj4eMyZM8csnpycDGdnZwCAj48PmjdvjvT0dGRnZ8tjAgMDERgYiJMnTyI/P1+Oh4SEwNfXF6mpqSgqKpLjoaGhcHd3R3JysmJBtWvXDjqdDgcOHFDkEBERAYPBgMOHD8sxjUaDTp06IT8/XzGfnJycEB4ejpycHKSlpclxNzc3hIWFISMjA+fPn5fjd0JNzsYCuJQUyPEijTMKGnjCtSQPTsZCOX5V64pCrRvcb+RCZ7ouxwsaeKBI4wLPG1nQmv7e6PN03jBITvAxZEAqs5Hl6PxhkjTwLf77kOuBA5e5nFgTa2JNrKkO1HT06FFUliTKtlC1LCMjA02aNMGePXvQtWtXOf7yyy9j9+7d2Lt3b7mvX7BgARYtWoRdu3ahXbt2VselpaWhefPm2L59O/r162f2vKU9QE2bNkVubi5cXV0BsMOuqzUt2XbC7nuAYvq24HJiTayJNbGmOlBTXl4ePD09kZ+fL39/W2PXPUDe3t7QaDTIyspSxLOysuDv71/ua99++20sWLAA27dvL7f5AW52ht7e3jh9+rTFBkiv10Ov15vFtVottFrlLCqdybeytGepvPit061OXJIki3FrOVY1Xh9qgiTJTUll4kKyfNpbleNlpl02Xy4n1sSaWFNV46yp5muyxK4nQet0OnTs2FFxAnPpCc1l9wjdatGiRZg3bx62bNmCiIiICt/n/PnzyM3NRePGjW2SNxEREdVvdr8KLDY2Fh988AHWrVuHY8eO4YUXXkBhYSHGjx8PABgzZoziJOmFCxfi9ddfx0cffYSgoCBkZmYiMzMTV69eBQBcvXoV06dPx6+//oo//vgDiYmJGDJkCFq0aIGoqCi71EhERER1i10PgQHAiBEjkJ2djVmzZiEzMxPt27fHli1b5BOjz549q9j9tWrVKhgMBjz22GOK6cTFxWH27NnQaDQ4fPgw1q1bh8uXLyMgIAD9+/fHvHnzLB7mIiIiIvWx60nQdVVBQQHc3NwqdRIV2deShJP2TgFTH7jb3ikQERGq9v1t90NgRERERLWNDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh2tvROgumlJwkl7pwAAmPrA3fZOgYiI7kDcA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREamO1t4JAMDKlSvx1ltvITMzE+Hh4VixYgU6d+5scewHH3yATz75BKmpqQCAjh07Yv78+YrxQgjExcXhgw8+wOXLl9G9e3esWrUKLVu2rJV6iG61JOGkvVPA1AfutncKRER1ht33AK1fvx6xsbGIi4vDoUOHEB4ejqioKFy8eNHi+F27dmHUqFHYuXMnkpKS0LRpU/Tv3x9//fWXPGbRokVYvnw5Vq9ejb1798LZ2RlRUVG4fv16bZVFREREdZjdG6DFixdjwoQJGD9+PFq3bo3Vq1ejYcOG+OijjyyO//zzzzFx4kS0b98eoaGh+PDDD2EymZCYmAjg5t6fpUuX4rXXXsOQIUPQrl07fPLJJ8jIyMCmTZtqsTIiIiKqq+x6CMxgMODgwYOYMWOGHHNwcEBkZCSSkpIqNY1r167hxo0b8PT0BACkp6cjMzMTkZGR8hg3Nzd06dIFSUlJGDlypNk0iouLUVxcLD8uKCgAAJSUlKCkpETOy8HBASaTCSaTSZGvg4MDjEYjhBAVxjUaDSRJkqdbNg4ARqOxUnGtVgshhCIuSRI0Go1Zjtbi5dUEAJL4OwYAAhIgSVWLA5AgKheXHAAhFHGj0VhuTbeOL83FWrwmaiopKalwOZlNx0Kt1uI2q0mIerHu3YnbE2tiTayp9mqqLLs2QDk5OTAajfDz81PE/fz8cPz48UpN45VXXkFAQIDc8GRmZsrTuHWapc/dKj4+HnPmzDGLJycnw9nZGQDg4+OD5s2bIz09HdnZ2fKYwMBABAYG4uTJk8jPz5fjISEh8PX1RWpqKoqKiuR4aGgo3N3dkZycrFhQ7dq1g06nw4EDBxQ5REREwGAw4PDhw3JMo9GgU6dOyM/PV8wnJycnhIeHIycnB2lpaXLczc0NYWFhyMjIwPnz5+V4eTUBgPuNXOhMfx82LGjggSKNCzxvZEFr+nsDydN5wyA5wceQAanMCpmj84dJ0sC3+O/DkwBwUd8EDsIIb8Pfy0NIEi7qA6ET1+FhyJHjqanF5dbkbCyAS0mBHC/SOKOggSdcS/LgZCyU41e1rijUutVITQcOXK5wOZUdX+KgRa6uMZxMhXC9kSfHDQ6OyNP51FhN+fn+9WLduxO3J9bEmlhT7dR09OhRVJYkyrZQtSwjIwNNmjTBnj170LVrVzn+8ssvY/fu3di7d2+5r1+wYAEWLVqEXbt2oV27dgCAPXv2oHv37sjIyEDjxo3lscOHD4ckSVi/fr3ZdCztAWratClyc3Ph6uoKQH0d9rLE03ViD9CL/VqWW9OSbSfsvgcopm+LCpfTsoQTFdZqLW6rmiY/0KperHt34vbEmlgTa6qdmvLy8uDp6Yn8/Hz5+9sau+4B8vb2hkajQVZWliKelZUFf3//cl/79ttvY8GCBdi+fbvc/ACQX5eVlaVogLKystC+fXuL09Lr9dDr9WZxrVYLrVY5i0pn8q1KV4TKxm+dbnXikiRZjFvLsapxIVk+RazK8f9vGioVlyRFvHT+Wcvx1vEVxWuiprLLwNpysjidKuZ+uzVJkmQ1x6rGa3rduxO3J9bEmqzFWVPN12SJXU+C1ul06Nixo3wCMwD5hOaye4RutWjRIsybNw9btmxBRESE4rng4GD4+/srpllQUIC9e/eWO00iIiJSD7vfByg2NhZjx45FREQEOnfujKVLl6KwsBDjx48HAIwZMwZNmjRBfHw8AGDhwoWYNWsWvvjiCwQFBcnn9bi4uMDFxQWSJGHKlCl444030LJlSwQHB+P1119HQEAAhg4daq8yiYiIqA6xewM0YsQIZGdnY9asWcjMzET79u2xZcsW+STms2fPKnZ/rVq1CgaDAY899phiOnFxcZg9ezaAm+cQFRYW4tlnn8Xly5fRo0cPbNmyBY6OjrVWFxEREdVddj0Juq4qKCiAm5tbpU6iulPVhTsXAxXfvbgu5FmZOyzXlzyJiOqzqnx/2/1GiERERES1jQ0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiIiIi1WEDRERERKpTrQZo586dts6DiIiIqNZUqwEaMGAAmjdvjjfeeAPnzp2zdU5ERERENapaDdBff/2FmJgYbNiwASEhIYiKisLXX38Ng8Fg6/yIiIiIbK5aDZC3tzemTp2KlJQU7N27F3fffTcmTpyIgIAATJo0Cb/99put8yQiIiKymds+CbpDhw6YMWMGYmJicPXqVXz00Ufo2LEjevbsiSNHjtgiRyIiIiKbqnYDdOPGDWzYsAEPPvggmjVrhq1bt+Ldd99FVlYWTp8+jWbNmmHYsGG2zJWIiIjIJrTVedGLL76IL7/8EkIIPPnkk1i0aBHuuece+XlnZ2e8/fbbCAgIsFmiRERERLZSrQbo6NGjWLFiBR555BHo9XqLY7y9vXm5PBEREdVJ1ToEFhcXh2HDhpk1PyUlJfjpp58AAFqtFr179779DImIiIhsrFoN0P33349Lly6ZxfPz83H//fffdlJERERENalaDZAQApIkmcVzc3Ph7Ox820kRERER1aQqnQP0yCOPAAAkScK4ceMUh8CMRiMOHz6Mbt262TZDIiIiIhurUgPk5uYG4OYeoEaNGsHJyUl+TqfT4b777sOECRNsmyERERGRjVWpAVq7di0AICgoCNOmTePhLiIiIqqXqnUZfFxcnK3zICIiIqo1lT4JukOHDsjLywMA3HvvvejQoYPVf1WxcuVKBAUFwdHREV26dMG+ffusjj1y5AgeffRRBAUFQZIkLF261GzM7NmzIUmS4l9oaGiVciIiIqI7W6X3AA0ZMkQ+6Xno0KE2efP169cjNjYWq1evRpcuXbB06VJERUXhxIkT8PX1NRt/7do1hISEYNiwYZg6darV6bZp0wbbt2+XH2u11drRRURERHeoSncGZQ972eoQ2OLFizFhwgSMHz8eALB69Wr897//xUcffYRXX33VbHynTp3QqVMnALD4fCmtVgt/f3+b5EhERER3ntv+NfjqMhgMOHjwICIjI/9OxsEBkZGRSEpKuq1pnzp1CgEBAQgJCcHo0aNx9uzZ202XiIiI7iCV3gPk4eFh8eaHlli6S/StcnJyYDQa4efnp4j7+fnh+PHjlU3LTJcuXfDxxx+jVatWuHDhAubMmYOePXsiNTUVjRo1svia4uJiFBcXy48LCgoA3Pxpj5KSEgA3mzMHBweYTCaYTCZ5bGncaDRCCFFhXKPRQJIkebpl48DN+ylVJq7VaiGEUMQlSYJGozHL0Vq8vJoAQBJ/xwBAQAIkqWpxABJE5eKSAyCEIm40Gsut6dbxpblYi9dETSUlJRUuJ7PpWKjVWtxmNf3/DUzr+rp3J25PrIk1sabaq6myKt0AWTrhuC4aOHCg/P/t2rVDly5d0KxZM3z99dd4+umnLb4mPj4ec+bMMYsnJyfLl/r7+PigefPmSE9PR3Z2tjwmMDAQgYGBOHnyJPLz8+V4SEgIfH19kZqaiqKiIjkeGhoKd3d3JCcnKxZUu3btoNPpcODAAUUOERERMBgMOHz4sBzTaDTo1KkT8vPzFc2ik5MTwsPDkZOTg7S0NDnu5uaGsLAwZGRk4Pz583K8vJoAwP1GLnSm63K8oIEHijQu8LyRBa3p7w0kT+cNg+QEH0MGpDIrZI7OHyZJA9/ivxQ1XdQ3gYMwwtuQKceEJOGiPhA6cR0ehhw5nppaXG5NzsYCuJQUyPEijTMKGnjCtSQPTsZCOX5V64pCrVuN1HTgwOUKl1PZ8SUOWuTqGsPJVAjXG3ly3ODgiDydT43VlJ/vXy/WvTtxe2JNrIk11U5NR48eRWVJomwLVYsMBgMaNmyIDRs2KE6qHjt2LC5fvozNmzeX+/qgoCBMmTIFU6ZMqfC9OnXqhMjISMTHx1t83tIeoKZNmyI3Nxeurq4A1NdhL0s8XSf2AL3Yr2W5NS3ZdsLue4Bi+raocDktSzhRYa3W4raqafIDrerFuncnbk+siTWxptqpKS8vD56ensjPz5e/v62p9B6ggoICeWKlh4isqehNgZt3ju7YsSMSExPlBshkMiExMRExMTGVTatCV69exZkzZ/Dkk09aHaPX681+2R64uRBvvYKsdCbfqnRFqGzc2pVpVYlLkmQxbi3HqsaFZPkUsSrHYfnQqcW4JCnipfPPWo63jq8oXhM1lV0G1paTxelUMffbran0EHZ9WPfuxO2JNbEma3HWVPM1WVKlc4AuXLgAX19fuLu7WzwfqPQcg8oeg4uNjcXYsWMRERGBzp07Y+nSpSgsLJSvChszZgyaNGki77kxGAzy7i2DwYC//voLKSkpcHFxQYsWLQAA06ZNw+DBg9GsWTNkZGQgLi4OGo0Go0aNqmypREREdIerdAO0Y8cOeHp6AgB27txpkzcfMWIEsrOzMWvWLGRmZqJ9+/bYsmWLfGL02bNnFZ1fRkYG7r33Xvnx22+/jbfffhu9e/fGrl27AADnz5/HqFGjkJubCx8fH/To0QO//vorfHx8bJIzERER1X+VboB69+5t8f9vV0xMjNVDXqVNTamgoCBUdMrSV199ZavUiIiI6A5V7Vsk5+Xl4V//+heOHTsGAGjdujXGjx8v7yUiIiIiqquqdSPEn376CUFBQVi+fDny8vKQl5eH5cuXIzg4GD/99JOtcyQiIiKyqWrtAYqOjsaIESOwatUqxWVwEydORHR0NH7//XebJklERERkS9XaA3T69Gm89NJLisvNNBoNYmNjcfr0aZslR0RERFQTqtUAdejQQT73p6xjx44hPDz8tpMiIiIiqkmVPgRW9lbXkyZNwuTJk3H69Gncd999AIBff/0VK1euxIIFC2yfJREREZENVboBat++/c072pa5DP3ll182G/f4449jxIgRtsmOiIiIqAZUugFKT0+vyTyIiIiIak2lG6BmzZrVZB5EREREtabaN0IEgKNHj+Ls2bMwGAyK+MMPP3xbSRERERHVpGo1QGlpafjHP/6B33//XXFeUOkPpFb2x1CJiIiI7KFal8FPnjwZwcHBuHjxIho2bIgjR47gp59+QkREhNnvdxERERHVNdXaA5SUlIQdO3bA29sbDg4OcHBwQI8ePRAfH49JkyYhOTnZ1nkSERER2Uy19gAZjUY0atQIAODt7Y2MjAwAN0+UPnHihO2yIyIiIqoB1doDdM899+C3335DcHAwunTpgkWLFkGn02HNmjUICQmxdY5ERERENlWtBui1115DYWEhAGDu3Ll46KGH0LNnT3h5eWH9+vU2TZCIiIjI1qrVAEVFRcn/36JFCxw/fhyXLl2Ch4eHfCUYERERUV11W/cBAoBz584BAJo2bXrbyRARERHVhmqdBF1SUoLXX38dbm5uCAoKQlBQENzc3PDaa6/hxo0bts6RiIiIyKaqtQfoxRdfxH/+8x8sWrQIXbt2BXDz0vjZs2cjNzcXq1atsmmSRERERLZUrQboiy++wFdffYWBAwfKsXbt2qFp06YYNWoUGyAiIiKq06p1CEyv1yMoKMgsHhwcDJ1Od7s5EREREdWoajVAMTExmDdvHoqLi+VYcXEx3nzzTcTExNgsOSIiIqKaUOlDYI888oji8fbt2xEYGIjw8HAAwG+//QaDwYB+/frZNkMiIiIiG6t0A+Tm5qZ4/Oijjyoe8zJ4IiIiqi8q3QCtXbu2JvMgIiIiqjW3dSPE7Oxs+cdPW7VqBR8fH5skRURERFSTqnUSdGFhIZ566ik0btwYvXr1Qq9evRAQEICnn34a165ds3WORERERDZVrQYoNjYWu3fvxnfffYfLly/j8uXL2Lx5M3bv3o2XXnrJ1jkSERER2VS1DoH9+9//xoYNG9CnTx859uCDD8LJyQnDhw/njRCJiIioTqvWHqBr167Bz8/PLO7r68tDYERERFTnVasB6tq1K+Li4nD9+nU5VlRUhDlz5si/DUZERERUV1XrENjSpUsxYMAAsxshOjo6YuvWrTZNkIiIiMjWqtUAtW3bFqdOncLnn3+O48ePAwBGjRqF0aNHw8nJyaYJEhEREdlalRugGzduIDQ0FN9//z0mTJhQEzkRERER1agqnwPUoEEDxbk/RERERPVNtU6Cjo6OxsKFC1FSUmLrfIiIiIhqXLXOAdq/fz8SExOxbds2tG3bFs7Ozorn//Of/9gkOSIiIqKaUK0GyN3d3ezX4Imo/luScNLeKWDqA3fbOwUiUoEqNUAmkwlvvfUWTp48CYPBgL59+2L27Nm88ouIiIjqlSqdA/Tmm29i5syZcHFxQZMmTbB8+XJER0fXVG5ERERENaJKDdAnn3yC9957D1u3bsWmTZvw3Xff4fPPP4fJZKqp/IiIiIhsrkoN0NmzZ/Hggw/KjyMjIyFJEjIyMmyeGBEREVFNqVIDVFJSAkdHR0WsQYMGuHHjhk2TIiIiIqpJVToJWgiBcePGQa/Xy7Hr16/j+eefV1wKz8vgiYiIqC6rUgM0duxYs9gTTzxhs2SIiIiIakOVGqC1a9fWVB5EREREtaZaP4VBREREVJ+xASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkerYvQFauXIlgoKC4OjoiC5dumDfvn1Wxx45cgSPPvoogoKCIEkSli5detvTJCIiIvWxawO0fv16xMbGIi4uDocOHUJ4eDiioqJw8eJFi+OvXbuGkJAQLFiwAP7+/jaZJhEREamPXRugxYsXY8KECRg/fjxat26N1atXo2HDhvjoo48sju/UqRPeeustjBw5UvGL9LczTSIiIlKfKv0Yqi0ZDAYcPHgQM2bMkGMODg6IjIxEUlJSrU6zuLgYxcXF8uOCggIAQElJCUpKSuTpODg4wGQywWQyKabv4OAAo9EIIUSFcY1GA0mS5OmWjQOA0WisVFyr1UIIoYhLkgSNRmOWo7V4eTUBgCT+jgGAgARIUtXiACSIysUlB0AIRdxoNJZb063jS3OxFq+JmkpKSipcTmbTsVCrtbjNahKiwnWv7GuqspxsWtP/53InbU934mcEa2JNdbWmyrJbA5STkwOj0Qg/Pz9F3M/PD8ePH6/VacbHx2POnDlm8eTkZDg7OwMAfHx80Lx5c6SnpyM7O1seExgYiMDAQJw8eRL5+flyPCQkBL6+vkhNTUVRUZEcDw0Nxdr9F+FbfB5SmYWXo/OHSdLAt/gvRQ4X9U3gIIzwNmTKMSFJuKgPhM5UBA9DjhwvcdAiV9cYTsarcL2RJ8cNDo7I0/nAuSQfLiU3m7sQH+dyawIA9xu50Jmuy/GCBh4o0rjA80YWtKa/N5A8nTcMkhN8DBm3X5O4rqgpNbUY4eHhyMnJQVpamhx3c3NDWFgYnI0Fck0AUKRxRkEDT7iW5MHJWCjHr2pdUah1q5GaDhy4jIiICBgMBhw+fFiOazQadOrUCfn5+Yrx8nIyFVpeTjVUU36+P9zd3ZGcnKz4kGjXrh10Oh0OHDgA3+K/p1+V5WTLmgBUaXuqqKayKlpOZT8nnJycyl33MjIycP78eTluy88I1sSaWFP1ajp69CgqSxJlW6halJGRgSZNmmDPnj3o2rWrHH/55Zexe/du7N27t9zXBwUFYcqUKZgyZcptT9PSHqCmTZsiNzcXrq43P5Bt2WEv3X6qRvaWVCUe07dFuTUtSzxdJ/YAvdivZbl/NSzZdsLue4Bi+rao8C+hZQknKqzVWtxWNU1+oFWFf929u+N0ubVWNffq1DS1fyvV/cXKmlgTa7JNTXl5efD09ER+fr78/W2N3fYAeXt7Q6PRICsrSxHPysqyeoJzTU1Tr9dbPKdIq9VCq1XOotKZfKvSFaGycSFZPv2q9EunUnFJqna8bF3WarKaY1Xjt1FT6fyzlmNV50FN1FR2Xt66vtxMRbI8ndtYfpXK8Za4JJkv+7K0Wq3Fadl63atMvKrbU3k1VTYuSZLFuLV1r6px1sSarMVZU83XZIndToLW6XTo2LEjEhMT5ZjJZEJiYqJi7429p0lERER3HrvtAQKA2NhYjB07FhEREejcuTOWLl2KwsJCjB8/HgAwZswYNGnSBPHx8QBunuRcenzPYDDgr7/+QkpKClxcXNCiRYtKTZOIiIjIrg3QiBEjkJ2djVmzZiEzMxPt27fHli1b5JOYz549q9j1lZGRgXvvvVd+/Pbbb+Ptt99G7969sWvXrkpNk4iIiMiuDRAAxMTEICYmxuJzpU1NqaCgIFTmnO3ypklERERk95/CICIiIqptbICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFRHa+8EiIiqYknCSXunAACY+sDd9k6BiG5DndgDtHLlSgQFBcHR0RFdunTBvn37yh3/zTffIDQ0FI6Ojmjbti1++OEHxfPjxo2DJEmKfwMGDKjJEoiIiKgesXsDtH79esTGxiIuLg6HDh1CeHg4oqKicPHiRYvj9+zZg1GjRuHpp59GcnIyhg4diqFDhyI1NVUxbsCAAbhw4YL878svv6yNcoiIiKgesHsDtHjxYkyYMAHjx49H69atsXr1ajRs2BAfffSRxfHLli3DgAEDMH36dISFhWHevHno0KED3n33XcU4vV4Pf39/+Z+Hh0dtlENERET1gF3PATIYDDh48CBmzJghxxwcHBAZGYmkpCSLr0lKSkJsbKwiFhUVhU2bNiliu3btgq+vLzw8PNC3b1+88cYb8PLysjjN4uJiFBcXy48LCgoAACUlJSgpKZHzcnBwgMlkgslkUuTr4OAAo9EIIUSFcY1GAwCQxN/TAAAB6WYconJxyQEQotrxkpKScmuymqMkVS1+mzUZjUZoNBqzHCVJujkvbxlfmou1eE3UVFJSIi9Xo9GoGK/VaiGEMJ9OFZafzWoSApIkyet0qbK5l31NTa17Fdb0/7lY257Mcqyhda+iuLXPAlt8RlS0nCoTL133ysZLtxtr2xNrYk13Qk2VZdcGKCcnB0ajEX5+foq4n58fjh8/bvE1mZmZFsdnZmbKjwcMGIBHHnkEwcHBOHPmDGbOnImBAwciKSlJXmBlxcfHY86cOWbx5ORkODs7AwB8fHzQvHlzpKenIzs7Wx4TGBiIwMBAnDx5Evn5+XI8JCQEvr6+SE1NRVFRkRwPDQ29OT1DBqQyCy9H5w+TpIFv8V+KHC7qm8BBGOFt+Ls+IUm4qA+ETlyHhyFHjpc4aJGrawwnUyFcb+TJcYODI/J0PnA2FsCl5GZzd+DA5XJrAgD3G7nQma7L8YIGHijSuMDzRha0pr83kDydNwySU43UlJpajPDwcOTk5CAtLU2Ou7m5ISwsTFETABRpnFHQwBOuJXlwMhbK8ataVxRq3WqkpgMHLiMiIgIGgwGHDx+W4xqNBp06dUJ+fr5ifFWWky1rys/3h7u7O5KTkxUfEu3atYNOp8OBAwfgW/z39Gtq3auoJgDlbk++xX+/b02uexXVlJGRgfPnz8txW35GVLScyqpo3Sv7Werk5FTu9sSaWFN9r+no0aOoLEmUbaFqWUZGBpo0aYI9e/aga9eucvzll1/G7t27sXfvXrPX6HQ6rFu3DqNGjZJj7733HubMmYOsrCyL75OWlobmzZtj+/bt6Nevn9nzlvYANW3aFLm5uXB1vfmBbMsOe+n2U3bfAxTTt0W5NS1LPF0n9gC92K9luX81LNl2wu57gGL6tqjwL6FlCScqrNVa3FY1TX6gVYV/3b2743S5tVY19+rUNLV/q3K3pxWJpyo3DyzlbsOaJvdrwb/CWRNrqmM15eXlwdPTE/n5+fL3tzV23QPk7e0NjUZj1rhkZWXB39/f4mv8/f2rNB642Rl6e3vj9OnTFhsgvV4PvV5vFtdqtdBqlbOodCbfytKepfLiQrJ8+lXpB3Sl4pJU7XjZuqzVZDXHqsZvo6bS+Wctx6rOg5qoqey8vHV9uZmKZHk6t7H8KpXjLXFJMl/2ZWm1WovTsvW6V5l4eduTxRztsD1ZWydt9RlR3nKqbFySJIvxqubOmlhTVeN1rSZL7HoStE6nQ8eOHZGYmCjHTCYTEhMTFXuEyuratatiPAAkJCRYHQ8A58+fR25uLho3bmybxImIiKhes/tVYLGxsfjggw+wbt06HDt2DC+88AIKCwsxfvx4AMCYMWMUJ0lPnjwZW7ZswTvvvIPjx49j9uzZOHDgAGJiYgAAV69exfTp0/Hrr7/ijz/+QGJiIoYMGYIWLVogKirKLjUSERFR3WL3O0GPGDEC2dnZmDVrFjIzM9G+fXts2bJFPtH57Nmzit1f3bp1wxdffIHXXnsNM2fORMuWLbFp0ybcc889AG7u/jp8+DDWrVuHy5cvIyAgAP3798e8efMsHuYiIiIi9bF7AwQAMTEx8h6cW+3atcssNmzYMAwbNszieCcnJ2zdutWW6REREdEdxu6HwIiIiIhqGxsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFSHDRARERGpDhsgIiIiUh02QERERKQ6bICIiIhIddgAERERkeqwASIiIiLVYQNEREREqsMGiIiIiFRHa+8EiIjuREsSTto7BUx94G57p0BUZ3EPEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHa29EyAiIvtZknDS3ilg6gN32zsFUiHuASIiIiLVYQNEREREqsMGiIiIiFSnTjRAK1euRFBQEBwdHdGlSxfs27ev3PHffPMNQkND4ejoiLZt2+KHH35QPC+EwKxZs9C4cWM4OTkhMjISp06dqskSiIiIqB6xewO0fv16xMbGIi4uDocOHUJ4eDiioqJw8eJFi+P37NmDUaNG4emnn0ZycjKGDh2KoUOHIjU1VR6zaNEiLF++HKtXr8bevXvh7OyMqKgoXL9+vbbKIiIiojrM7leBLV68GBMmTMD48eMBAKtXr8Z///tffPTRR3j11VfNxi9btgwDBgzA9OnTAQDz5s1DQkIC3n33XaxevRpCCCxduhSvvfYahgwZAgD45JNP4Ofnh02bNmHkyJG1VxwREdkEr1YjW7PrHiCDwYCDBw8iMjJSjjk4OCAyMhJJSUkWX5OUlKQYDwBRUVHy+PT0dGRmZirGuLm5oUuXLlanSUREROpi1z1AOTk5MBqN8PPzU8T9/Pxw/Phxi6/JzMy0OD4zM1N+vjRmbcytiouLUVxcLD/Oz88HAFy6dAklJSUAbjZmDg4OMJlMMJlM8tjSuNFohBCiwrhGo8H1wquQxN/TAAABCQAgQVQuLjkAQlQ7funSpXJrspqjJFUtfps15eXlQaPRmOUoSdLNeXn1imJ8aS63TqdauVeypkuXLkGj0QAAjEajYrxWq4UQAsVXCyqs1VrcVjXl5+dDkiR5nS5VNveyedbUuldRTQUFBeVuT2Y51tC6V1H88uXLFj8LStfV0jxrct2rqKb8/HzFOlm63ZTdnuQ8a3Ddq6imS5cuKeKWtqfiqwU1vu5VlHvZPDUajdn2tGZ3Wq1/lluq6dneIQCq9/1U0WdEZeKln3sVrXvlxav7nZuXl3dzngjlfLPE7ofA6oL4+HjMmTPHLB4cHGyHbGrHDHsnUEn1Ic/6kCNQf/Kcae8EKqk+5FkfcgTqT571ZRuqL3nWpCtXrsDNza3cMXZtgLy9vaHRaJCVlaWIZ2Vlwd/f3+Jr/P39yx1f+t+srCw0btxYMaZ9+/YWpzljxgzExsbKj02mm52+l5cXJEmqcl01qaCgAE2bNsW5c+fg6upq73SsYp62VR/yrA85AszT1upDnvUhR4B52oIQAleuXEFAQECFY+3aAOl0OnTs2BGJiYkYOnQogJvNR2JiImJiYiy+pmvXrkhMTMSUKVPkWEJCArp27Qrg5l4bf39/JCYmyg1PQUEB9u7dixdeeMHiNPV6PfR6vSLm7u5+W7XVNFdX1zq34lnCPG2rPuRZH3IEmKet1Yc860OOAPO8XRXt+Sll90NgsbGxGDt2LCIiItC5c2csXboUhYWF8lVhY8aMQZMmTRAfHw8AmDx5Mnr37o133nkHgwYNwldffYUDBw5gzZo1AG4eT5wyZQreeOMNtGzZEsHBwXj99dcREBAgN1lERESkbnZvgEaMGIHs7GzMmjULmZmZaN++PbZs2SKfxHz27Fk4OPx9sVq3bt3wxRdf4LXXXsPMmTPRsmVLbNq0Cffcc4885uWXX0ZhYSGeffZZXL58GT169MCWLVvg6OhY6/URERFR3WP3BggAYmJirB7y2rVrl1ls2LBhGDZsmNXpSZKEuXPnYu7cubZKsc7Q6/WIi4szO2RX1zBP26oPedaHHAHmaWv1Ic/6kCPAPGubJCpzrRgRERHRHcTuP4VBREREVNvYABEREZHqsAEiIiIi1WEDRERERKrDBqieWblyJYKCguDo6IguXbpg37599k5J4aeffsLgwYMREBAASZKwadMme6dkJj4+Hp06dUKjRo3g6+uLoUOH4sSJE/ZOy8yqVavQrl07+WZjXbt2xY8//mjvtCq0YMEC+X5cdcns2bMhSZLiX2hoqL3TMvPXX3/hiSeegJeXF5ycnNC2bVscOHDA3mkpBAUFmc1LSZIQHR1t79QUjEYjXn/9dQQHB8PJyQnNmzfHvHnzKvU7UbXpypUrmDJlCpo1awYnJyd069YN+/fvt3daFX6eCyEwa9YsNG7cGE5OToiMjMSpU6fsk2w1sAGqR9avX4/Y2FjExcXh0KFDCA8PR1RUFC5evGjv1GSFhYUIDw/HypUr7Z2KVbt370Z0dDR+/fVXJCQk4MaNG+jfvz8KCwvtnZpCYGAgFixYgIMHD+LAgQPo27cvhgwZgiNHjtg7Nav279+P999/H+3atbN3Kha1adMGFy5ckP/9/PPP9k5JIS8vD927d0eDBg3w448/4ujRo3jnnXfg4eFh79QU9u/fr5iPCQkJAFDu7UnsYeHChVi1ahXeffddHDt2DAsXLsSiRYuwYsUKe6em8MwzzyAhIQGffvopfv/9d/Tv3x+RkZH466+/7JpXRZ/nixYtwvLly7F69Wrs3bsXzs7OiIqKwvXr12s502oSVG907txZREdHy4+NRqMICAgQ8fHxdszKOgBi48aN9k6jQhcvXhQAxO7du+2dSoU8PDzEhx9+aO80LLpy5Ypo2bKlSEhIEL179xaTJ0+2d0oKcXFxIjw83N5plOuVV14RPXr0sHcaVTZ58mTRvHlzYTKZ7J2KwqBBg8RTTz2liD3yyCNi9OjRdsrI3LVr14RGoxHff/+9It6hQwfxz3/+005Zmbv189xkMgl/f3/x1ltvybHLly8LvV4vvvzySztkWHXcA1RPGAwGHDx4EJGRkXLMwcEBkZGRSEpKsmNm9V9+fj4AwNPT086ZWGc0GvHVV1+hsLBQ/t27uiY6OhqDBg1SrKN1zalTpxAQEICQkBCMHj0aZ8+etXdKCt9++y0iIiIwbNgw+Pr64t5778UHH3xg77TKZTAY8Nlnn+Gpp56qcz8e3a1bNyQmJuLkyZMAgN9++w0///wzBg4caOfM/lZSUgKj0Wj2SwVOTk51bg9lWenp6cjMzFRs725ubujSpUu9+U6qE3eCporl5OTAaDTKPxFSys/PD8ePH7dTVvWfyWTClClT0L17d8XPqdQVv//+O7p27Yrr16/DxcUFGzduROvWre2dlpmvvvoKhw4dqhPnLVjTpUsXfPzxx2jVqhUuXLiAOXPmoGfPnkhNTUWjRo3snR4AIC0tDatWrUJsbCxmzpyJ/fv3Y9KkSdDpdBg7dqy907No06ZNuHz5MsaNG2fvVMy8+uqrKCgoQGhoKDQaDYxGI958802MHj3a3qnJGjVqhK5du2LevHkICwuDn58fvvzySyQlJaFFixb2Ts+qzMxMALD4nVT6XF3HBohULTo6GqmpqXX2L61WrVohJSUF+fn52LBhA8aOHYvdu3fXqSbo3LlzmDx5MhISEur07+2V/au/Xbt26NKlC5o1a4avv/4aTz/9tB0z+5vJZEJERATmz58PALj33nuRmpqK1atX19kG6F//+hcGDhyIgIAAe6di5uuvv8bnn3+OL774Am3atEFKSgqmTJmCgICAOjU/P/30Uzz11FNo0qQJNBoNOnTogFGjRuHgwYP2Tu2OxkNg9YS3tzc0Gg2ysrIU8aysLPj7+9spq/otJiYG33//PXbu3InAwEB7p2ORTqdDixYt0LFjR8THxyM8PBzLli2zd1oKBw8exMWLF9GhQwdotVpotVrs3r0by5cvh1arhdFotHeKFrm7u+Puu+/G6dOn7Z2KrHHjxmbNbVhYWJ07VFfqzz//xPbt2/HMM8/YOxWLpk+fjldffRUjR45E27Zt8eSTT2Lq1KmIj4+3d2oKzZs3x+7du3H16lWcO3cO+/btw40bNxASEmLv1Kwq/d6pz99JbIDqCZ1Oh44dOyIxMVGOmUwmJCYm1tlzQuoqIQRiYmKwceNG7NixA8HBwfZOqdJMJhOKi4vtnYZCv3798PvvvyMlJUX+FxERgdGjRyMlJQUajcbeKVp09epVnDlzBo0bN7Z3KrLu3bub3ZLh5MmTaNasmZ0yKt/atWvh6+uLQYMG2TsVi65duwYHB+XXnEajgclkslNG5XN2dkbjxo2Rl5eHrVu3YsiQIfZOyarg4GD4+/srvpMKCgqwd+/eevOdxENg9UhsbCzGjh2LiIgIdO7cGUuXLkVhYSHGjx9v79RkV69eVfxFnZ6ejpSUFHh6euKuu+6yY2Z/i46OxhdffIHNmzejUaNG8vFqNzc3ODk52Tm7v82YMQMDBw7EXXfdhStXruCLL77Arl27sHXrVnunptCoUSOz86ecnZ3h5eVVp86rmjZtGgYPHoxmzZohIyMDcXFx0Gg0GDVqlL1Tk02dOhXdunXD/PnzMXz4cOzbtw9r1qzBmjVr7J2aGZPJhLVr12Ls2LHQauvmV8ngwYPx5ptv4q677kKbNm2QnJyMxYsX46mnnrJ3agpbt26FEAKtWrXC6dOnMX36dISGhtr9s72iz/MpU6bgjTfeQMuWLREcHIzXX38dAQEBGDp0qP2Srgp7X4ZGVbNixQpx1113CZ1OJzp37ix+/fVXe6eksHPnTgHA7N/YsWPtnZrMUn4AxNq1a+2dmsJTTz0lmjVrJnQ6nfDx8RH9+vUT27Zts3dalVIXL4MfMWKEaNy4sdDpdKJJkyZixIgR4vTp0/ZOy8x3330n7rnnHqHX60VoaKhYs2aNvVOyaOvWrQKAOHHihL1TsaqgoEBMnjxZ3HXXXcLR0VGEhISIf/7zn6K4uNjeqSmsX79ehISECJ1OJ/z9/UV0dLS4fPmyvdOq8PPcZDKJ119/Xfj5+Qm9Xi/69etXp9eHW0lC1LFbYhIRERHVMJ4DRERERKrDBoiIiIhUhw0QERERqQ4bICIiIlIdNkBERESkOmyAiIiISHXYABEREZHqsAEiojrljz/+gCRJSElJsXcqsuPHj+O+++6Do6Mj2rdvb+90AABBQUFYunSpvdMgqrfYABGRwrhx4yBJEhYsWKCIb9q0CZIk2Skr+4qLi4OzszNOnDih+O2jW2VmZmLy5Mlo0aIFHB0d4efnh+7du2PVqlW4du1aLWZMRBWpmz/gQkR25ejoiIULF+K5556Dh4eHvdOxCYPBAJ1OV63XnjlzBoMGDSr3R0nT0tLQvXt3uLu7Y/78+Wjbti30ej1+//13rFmzBk2aNMHDDz9c3fSJyMa4B4iIzERGRsLf3x/x8fFWx8yePdvscNDSpUsRFBQkPx43bhyGDh2K+fPnw8/PD+7u7pg7dy5KSkowffp0eHp6IjAwEGvXrjWb/vHjx9GtWzc4Ojrinnvuwe7duxXPp6amYuDAgXBxcYGfnx+efPJJ5OTkyM/36dMHMTExmDJlCry9vREVFWWxDpPJhLlz5yIwMBB6vR7t27fHli1b5OclScLBgwcxd+5cSJKE2bNnW5zOxIkTodVqceDAAQwfPhxhYWEICQnBkCFD8N///heDBw+Wx549exZDhgyBi4sLXF1dMXz4cGRlZcnPnzlzBkOGDIGfnx9cXFzQqVMnbN++3eL7AoAQArNnz8Zdd90FvV6PgIAATJo0yep4ImIDREQWaDQazJ8/HytWrMD58+dva1o7duxARkYGfvrpJyxevBhxcXF46KGH4OHhgb179+L555/Hc889Z/Y+06dPx0svvYTk5GR07doVgwcPRm5uLgDg8uXL6Nu3L+69914cOHAAW7ZsQVZWFoYPH66Yxrp166DT6fDLL79g9erVFvNbtmwZ3nnnHbz99ts4fPgwoqKi8PDDD+PUqVMAgAsXLqBNmzZ46aWXcOHCBUybNs1sGrm5udi2bRuio6Ph7Oxs8X1KDx+aTCYMGTIEly5dwu7du5GQkIC0tDSMGDFCHnv16lU8+OCDSExMRHJyMgYMGIDBgwfj7NmzFqf973//G0uWLMH777+PU6dOYdOmTWjbtq3FsUT0/+z8Y6xEVMeMHTtWDBkyRAghxH333SeeeuopIYQQGzduFGU/MuLi4kR4eLjitUuWLBHNmjVTTKtZs2bCaDTKsVatWomePXvKj0tKSoSzs7P48ssvhRBCpKenCwBiwYIF8pgbN26IwMBAsXDhQiGEEPPmzRP9+/dXvPe5c+cUv07eu3dvce+991ZYb0BAgHjzzTcVsU6dOomJEyfKj8PDw0VcXJzVafz6668CgPjPf/6jiHt5eQlnZ2fh7OwsXn75ZSGEENu2bRMajUacPXtWHnfkyBEBQOzbt8/qe7Rp00asWLFCftysWTOxZMkSIYQQ77zzjrj77ruFwWCosF4iuol7gIjIqoULF2LdunU4duxYtafRpk0bODj8/VHj5+en2Duh0Wjg5eWFixcvKl7XtWtX+f+1Wi0iIiLkPH777Tfs3LkTLi4u8r/Q0FAANw8flerYsWO5uRUUFCAjIwPdu3dXxLt3735bNZfat28fUlJS0KZNGxQXFwMAjh07hqZNm6Jp06byuNatW8Pd3V1+z6tXr2LatGkICwuDu7s7XFxccOzYMat7gIYNG4aioiKEhIRgwoQJ2LhxI0pKSm47f6I7GRsgIrKqV69eiIqKwowZM8yec3BwgBBCEbtx44bZuAYNGigeS5JkMWYymSqd19WrVzF48GCkpKQo/p06dQq9evWSx1k7HGVrLVq0gCRJOHHihCIeEhKCFi1awMnJqUrTmzZtGjZu3Ij58+fjf//7H1JSUtC2bVsYDAaL45s2bYoTJ07gvffeg5OTEyZOnIhevXpZXB5EdBMbICIq14IFC/Ddd98hKSlJEffx8UFmZqaiCbLlvXt+/fVX+f9LSkpw8OBBhIWFAQA6dOiAI0eOICgoCC1atFD8q0rT4+rqioCAAPzyyy+K+C+//ILWrVtXejpeXl544IEH8O6776KwsLDcsWFhYTh37hzOnTsnx44ePYrLly/L7/nLL79g3Lhx+Mc//oG2bdvC398ff/zxR7nTdXJywuDBg7F8+XLs2rULSUlJ+P333ytdA5HasAEionK1bdsWo0ePxvLlyxXxPn36IDs7G4sWLcKZM2ewcuVK/PjjjzZ735UrV2Ljxo04fvw4oqOjkZeXh6eeegoAEB0djUuXLmHUqFHYv38/zpw5g61bt2L8+PEwGo1Vep/p06dj4cKFWL9+PU6cOIFXX30VKSkpmDx5cpWm895776GkpAQRERFYv349jh07hhMnTuCzzz7D8ePHodFoANy8wq50nh46dAj79u3DmDFj0Lt3b0RERAAAWrZsif/85z9ISUnBb7/9hscff7zcPWQff/wx/vWvfyE1NRVpaWn47LPP4OTkVO5l+0RqxwaIiCo0d+5csy/gsLAwvPfee1i5ciXCw8Oxb98+i1dIVdeCBQuwYMEChIeH4+eff8a3334Lb29vAJD32hiNRvTv3x9t27bFlClT4O7urjjfqDImTZqE2NhYvPTSS2jbti22bNmCb7/9Fi1btqzSdJo3b47k5GRERkZixowZCA8PR0REBFasWIFp06Zh3rx5AG4e7tu8eTM8PDzQq1cvREZGIiQkBOvXr5entXjxYnh4eKBbt24YPHgwoqKi0KFDB6vv7e7ujg8++ADdu3dHu3btsH37dnz33Xfw8vKqUg1EaiKJWw/iExEREd3huAeIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDpsgIiIiEh12AARERGR6rABIiIiItVhA0RERESqwwaIiIiIVIcNEBEREakOGyAiIiJSHTZAREREpDr/B8QwOD9A1J0zAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot "
+    "# Create a Poisson distribution object\n",
+    "poisson_dist = poisson(avg_number_goals)\n",
+    "\n",
+    "x = np.arange(0, 11)\n",
+    "\n",
+    "# Calculate the PMF values for the range of values\n",
+    "pmf_values = poisson_dist.pmf(x)\n",
+    "\n",
+    "# Create a bar plot to visualize the Poisson distribution\n",
+    "plt.bar(x, pmf_values, align='center', alpha=0.5)\n",
+    "plt.xticks(x)\n",
+    "plt.xlabel('Number of Goals')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Poisson Probability Distribution for Goals (0 to 10)')\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "\n",
+    "plt.show()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "ironhack",
    "language": "python",
-   "name": "python3"
+   "name": "ironhack"
   },
   "language_info": {
    "codemirror_mode": {
@@ -185,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,