CS166 – Random numbers and simulations

CS166_6-2_PCW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random numbers from simulations\n",
    "\n",
    "## Exercise 14, Chapter 2\n",
    "\n",
    "(Sampling bias for bus waiting times) Suppose the interarrival time\n",
    "for a city bus has an exponential distribution with parameter $1/\\lambda$. A passenger arrives at a uniformly random time and records the time until the next bus arrives. What is the expected waiting time? Use a simulation to get an answer. Is the answer surprising? Now suppose instead that the interarrival time is $U(0, 2\\lambda)$. How does this change the situation? (Notice that the expected interarrival time is λ in both cases.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we're trying to do is:\n",
    "1. Record the waiting time of the passenger who arrives according to a uniformly random time\n",
    "\n",
    "    a. For this step, we assume the passenger's arrival time is irrelevant. We simulate just the interarrival times and compare their means\n",
    "   \n",
    "\n",
    "2. Calculate the mean waiting time\n",
    "\n",
    "\n",
    "3. Change the interarrival time to be uniformly distributed\n",
    "\n",
    "    a. Simulate waiting times again\n",
    "    \n",
    "    b. Compute mean waiting time again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import numpy.random as rand\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "lambdas = np.linspace(0.2, 20.2, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_trials = 1000\n",
    "exp_waits = []\n",
    "uni_waits = []\n",
    "\n",
    "for l in lambdas:\n",
    "    waiting_exp = []\n",
    "    waiting_uni = []\n",
    "    for trial in range(n_trials):\n",
    "        waiting_exp.append(rand.exponential(l))\n",
    "        waiting_uni.append(rand.uniform(0, 2*l))\n",
    "    exp_waits.append(np.mean(waiting_exp))\n",
    "    uni_waits.append(np.mean(waiting_uni))\n",
    "\n",
    "plt.plot(lambdas, exp_waits, label = 'Exponential interrarival times')\n",
    "plt.plot(lambdas, uni_waits, label = 'Uniform interarrival times')\n",
    "plt.xlabel('Lambda')\n",
    "plt.ylabel('Expected waiting time')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "As we can see from the graph, the expected waiting time for the exponentially distributed interrarival times is roughly equal to $\\lambda$. The uniform distribution with boundaries in 0 and $2\\lambda$ will also yield the same expected waiting time, as we obtain the expected value by dividing the difference between the maximum and minimum of the range by 2. Thus, we can see that both distributions trail each other, but the exponential interrarival times have higher variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 24, Chapter 2\n",
    "\n",
    "*(Retirement benefit projection)* At age 50 Fannie Mae has \\\\$150,000 invested and will be investing another \\\\$10,000 per year until age 70. Each year the investment grows according to an interest rate that is normally distributed with mean 8% and standard deviation 9%. At age 70, Fannie Mae then retires and withdraws \\\\$65,000 per year until death. Below is given a conditional death probability table. Thus if Fannie Mae lives until age 70, then the probability of dying before age 71 is 0.04979. Simulate this process 1000 times and histogram the amount of money Fannie Mae has at death."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulations\n",
    "We define the 'data' dictionary below defining the probability of death at each age. After that, we proceed to use a few nested loops and conditionals to simulate a life for Fannie's investments 1000 times in an experiment, for 10 different experiments.\n",
    "\n",
    "The multiple experiments confirmed that the simulation yields little variability in the end. After all 10 experiments, the mean investment sum at death were all around \\\\$150,200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "50: 0.00832, 51: 0.00911, 52: 0.00996, 53: 0.01089, 54: 0.01190,\n",
    "55: 0.01300, 56: 0.01421, 57: 0.01554, 58: 0.01700, 59: 0.01859,\n",
    "60: 0.02034, 61: 0.02224, 62: 0.02431, 63: 0.02657, 64: 0.02904,\n",
    "65: 0.03175, 66: 0.03474, 67: 0.03804, 68: 0.04168, 69: 0.04561,\n",
    "70: 0.04979, 71: 0.05415, 72: 0.05865, 73: 0.06326, 74: 0.06812,\n",
    "75: 0.07337, 76: 0.07918, 77: 0.08570, 78: 0.09306, 79: 0.10119,\n",
    "80: 0.10998, 81: 0.11935, 82: 0.12917, 83: 0.13938, 84: 0.15001,\n",
    "85: 0.16114, 86: 0.17282, 87: 0.18513, 88: 0.19825, 89: 0.21246,\n",
    "90: 0.22814, 91: 0.24577, 92: 0.26593, 93: 0.28930, 94: 0.31666,\n",
    "95: 0.35124, 96: 0.40056, 97: 0.48842, 98: 0.66815, 99: 0.72000,\n",
    "100: 0.76000, 101: 0.80000, 102: 0.85000, 103: 0.90000,\n",
    "104: 0.96000, 105: 1.00000}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150091.485 \n",
      "Standard deviation of investment sum at death: 1532.075\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150242.678 \n",
      "Standard deviation of investment sum at death: 2689.472\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150054.963 \n",
      "Standard deviation of investment sum at death: 929.915\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150157.826 \n",
      "Standard deviation of investment sum at death: 1998.324\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150161.298 \n",
      "Standard deviation of investment sum at death: 2185.306\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEICAYAAACnL3iHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUk0lEQVR4nO3df7DddX3n8efLpOIPRJISaBqyDdpoC860QJbF2lq7dISiNUy37ITWbazMsFXqKrOtE7q71d1OdtBap3Zc3GJ1GysFUsqWtI5b01Tr2EEwQVYIMSUShFtiEqX82m3RwHv/OJ8sh8u9Fz/3JPfe3Pt8zNw53+/n+/mez+d9DuR1vt/v+ZGqQpKkHs+b7QlIko49hockqZvhIUnqZnhIkroZHpKkboaHJKmb4aFjVpKdSV432/OQFiLDQ3NSkvuS/PS4trck+cLh9ao6o6o+9xz3sypJJVl8lKY6Y5JcmOTatvyJJG8at31Zkj9O8nCSfzjcVzoaDA9pBDMcSmcDO4aWbx+3/SbgG8APACcDH5i5qWmhMTx0zBo+OklyTpLtSR5Nsj/JB1u3z7fbh5M8nuTVSZ6X5D8m+XqSA+1V/EuH7veX2rZvJflP48Z5b5Ibk3wyyaPAW9rYt7RX/PuSfDjJ84fur5K8Pck9SR5L8ltJXt72eTTJ5uH+U1gD7EjyYmBpVY0NjfF6YCXw61X1SFV9p6q+PMrjK03F8NB88SHgQ1V1AvByYHNrf227PbGqjq+qW4C3tL+fAl4GHA98GCDJ6cDVwC8Cy4GXAivGjbUWuBE4EbgWeBK4AjgJeDVwHvD2cftcwOBo4Vzg3cA1bYyVwKuASyYrLMnuJA8DbwS2APuBk1pY/X7rdi6wG9jUQu9LSX5ysvuURmV4aC77s/YP5MPtH8+rp+j7HeAHk5xUVY9X1Ren6PuLwAer6t6qehy4EljXTkH9PPDnVfWFqvo28JvA+C+Au6Wq/qyqnqqqf6yqHVX1xao6VFX3Ab8PjP+H+31V9WhV7QTuAj7Txn8E+DRw5mSTrapXtnltqaqXAn8M/EJVnVhV/7Z1OxV4PfBZ4PuA3wFuTnLSFI+DNG2Gh+ayi9o/kCdW1Yk8+9X8sEuBVwBfba+63zhF3+8Hvj60/nVgMXBK2/bA4Q1V9X+Bb43b/4HhlSSvSPIXSb7RTmX9VwZHIcP2Dy3/4wTrx0800STvb8H5KeD1bflS4KNJvjHuPu6rqo+1U1bXt3m+ZqL7lUZleGheqKp7quoSBheK3wfc2K4NTPS10Q8yuKh82D8DDjH4B30fg1fxACR5IfC944cbt/4R4KvA6nba7DeATL+aoYGq3t2Ccy/wgwyOaG5pgfp9Q12/MsG8pKPG8NC8kOTNSZZV1VPAw635SeAg8BSDaxuHXQdckeS0JMczOFK4oaoOMbiW8bNJfqxdxP7PPHcQvAR4FHg8yQ8BbztSdQEkeQnwkqraB5wFbJ+g2/8EliRZn2RRkp9ncK3mb4/kXKTDDA/NFxcAO5M8zuDi+bqq+qd22mkj8Lft2sm5wMeBP2LwTqy9wD8B7wBo1yTeAVzP4CjkMeAA8MQUY/8a8Aut70eBG45wbWcCd7Tls3j67br/X1U9BLypzeURYAOwtqq+eYTnIgEQfwxKmlw7MnmYwSmpvbM8HWnO8MhDGifJzyZ5Ubtm8gHgTuC+2Z2VNLcYHtKzrWVwUf1BYDWDU2AeoktDPG0lSermkYckqduc/6bRk046qVatWjXb05CkY8qOHTu+WVXLjtb9z/nwWLVqFdu3T/S2dknSZJJ8/bl7TZ+nrSRJ3QwPSVI3w0OS1M3wkCR1e87wSPLx9mtrdw21LU2ytf0y2tYkS4a2XZlkT/sBm/OH2s9Ocmfb9ntJjsi3jkqSZt53c+Txhwy+dG7YBmBbVa0GtrX1w7/Ctg44o+1zdZJFbZ+PAJcx+MTu6gnuU5J0jHjO8KiqzwMPjWteC2xqy5uAi4bar6+qJ9qXyO0BzkmyHDihqm5pX/PwiaF9JEnHmOle8zil/bYA7fbk1r6CZ/7K2lhrW9GWx7dPKMllSbYn2X7w4MFpTlGSdLQc6QvmE13HqCnaJ1RV11TVmqpas2zZUfuApCRpmqb7CfP9SZZX1b52SupAax8DVg71O5XBN5OOMfTTnkPtR9WqDZ862kNM6L6r3jAr40rSTJnukccWYH1bXg/cPNS+LslxSU5jcGH8tnZq67Ek57Z3Wf3S0D6SpGPMcx55JLkOeB1wUpIx4D3AVcDmJJcC9wMXw+AnPJNsBu4GDgGXV9WT7a7exuCdWy8EPt3+JEnHoOcMj6q6ZJJN503SfyOD34we374deFXX7CRJc5KfMJckdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1Gyk8klyRZGeSu5Jcl+QFSZYm2Zrknna7ZKj/lUn2JNmd5PzRpy9Jmg3TDo8kK4B/B6ypqlcBi4B1wAZgW1WtBra1dZKc3rafAVwAXJ1k0WjTlyTNhlFPWy0GXphkMfAi4EFgLbCpbd8EXNSW1wLXV9UTVbUX2AOcM+L4kqRZMO3wqKq/Bz4A3A/sAx6pqs8Ap1TVvtZnH3By22UF8MDQXYy1tmdJclmS7Um2Hzx4cLpTlCQdJaOctlrC4GjiNOD7gRcnefNUu0zQVhN1rKprqmpNVa1ZtmzZdKcoSTpKRjlt9dPA3qo6WFXfAW4CfgzYn2Q5QLs90PqPASuH9j+VwWkuSdIxZpTwuB84N8mLkgQ4D9gFbAHWtz7rgZvb8hZgXZLjkpwGrAZuG2F8SdIsWTzdHavq1iQ3ArcDh4AvA9cAxwObk1zKIGAubv13JtkM3N36X15VT444f0nSLJh2eABU1XuA94xrfoLBUchE/TcCG0cZU5I0+/yEuSSpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkbiOFR5ITk9yY5KtJdiV5dZKlSbYmuafdLhnqf2WSPUl2Jzl/9OlLkmbDqEceHwL+V1X9EPAjwC5gA7CtqlYD29o6SU4H1gFnABcAVydZNOL4kqRZMO3wSHIC8FrgYwBV9e2qehhYC2xq3TYBF7XltcD1VfVEVe0F9gDnTHd8SdLsGeXI42XAQeB/JPlykj9I8mLglKraB9BuT279VwAPDO0/1tokSceYUcJjMXAW8JGqOhP4P7RTVJPIBG01YcfksiTbk2w/ePDgCFOUJB0No4THGDBWVbe29RsZhMn+JMsB2u2Bof4rh/Y/FXhwojuuqmuqak1VrVm2bNkIU5QkHQ3TDo+q+gbwQJJXtqbzgLuBLcD61rYeuLktbwHWJTkuyWnAauC26Y4vSZo9i0fc/x3AtUmeD9wL/DKDQNqc5FLgfuBigKramWQzg4A5BFxeVU+OOL4kaRaMFB5VdQewZoJN503SfyOwcZQxJUmzz0+YS5K6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkrqNHB5JFiX5cpK/aOtLk2xNck+7XTLU98oke5LsTnL+qGNLkmbHkTjyeCewa2h9A7CtqlYD29o6SU4H1gFnABcAVydZdATGlyTNsJHCI8mpwBuAPxhqXgtsasubgIuG2q+vqieqai+wBzhnlPElSbNj1COP3wXeDTw11HZKVe0DaLcnt/YVwAND/cZa27MkuSzJ9iTbDx48OOIUJUlH2rTDI8kbgQNVteO73WWCtpqoY1VdU1VrqmrNsmXLpjtFSdJRsniEfV8DvCnJhcALgBOSfBLYn2R5Ve1Lshw40PqPASuH9j8VeHCE8SVJs2TaRx5VdWVVnVpVqxhcCP/rqnozsAVY37qtB25uy1uAdUmOS3IasBq4bdozlyTNmlGOPCZzFbA5yaXA/cDFAFW1M8lm4G7gEHB5VT15FMaXJB1lRyQ8qupzwOfa8reA8ybptxHYeCTGlCTNHj9hLknqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSepmeEiSuhkekqRuhockqZvhIUnqZnhIkroZHpKkboaHJKmb4SFJ6mZ4SJK6GR6SpG6GhySpm+EhSeo27fBIsjLJZ5PsSrIzyTtb+9IkW5Pc026XDO1zZZI9SXYnOf9IFCBJmnmjHHkcAv59Vf0wcC5weZLTgQ3AtqpaDWxr67Rt64AzgAuAq5MsGmXykqTZMe3wqKp9VXV7W34M2AWsANYCm1q3TcBFbXktcH1VPVFVe4E9wDnTHV+SNHuOyDWPJKuAM4FbgVOqah8MAgY4uXVbATwwtNtYa5vo/i5Lsj3J9oMHDx6JKUqSjqCRwyPJ8cCfAu+qqken6jpBW03Usaquqao1VbVm2bJlo05RknSEjRQeSb6HQXBcW1U3teb9SZa37cuBA619DFg5tPupwIOjjC9Jmh2jvNsqwMeAXVX1waFNW4D1bXk9cPNQ+7okxyU5DVgN3Dbd8SVJs2fxCPu+Bvg3wJ1J7mhtvwFcBWxOcilwP3AxQFXtTLIZuJvBO7Uur6onRxhfkjRLph0eVfUFJr6OAXDeJPtsBDZOd0xJ0tzgJ8wlSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3QwPSVI3w0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdTM8JEndDA9JUjfDQ5LUzfCQJHUzPCRJ3WY8PJJckGR3kj1JNsz0+JKk0c1oeCRZBPw34GeA04FLkpw+k3OQJI1u8QyPdw6wp6ruBUhyPbAWuHuG56F5ZNWGT83KuPdd9YZZGXchmq3nGHyeJzPT4bECeGBofQz4F+M7JbkMuKytPp5k9zTHOwn45jT3nba8b6ZHnNSs1D9HHPXa59DzPBGf+yNkjj/PEzlc/w8czUFmOjwyQVs9q6HqGuCakQdLtlfVmlHv51i1kOtfyLXDwq5/IdcOM1f/TF8wHwNWDq2fCjw4w3OQJI1opsPjS8DqJKcleT6wDtgyw3OQJI1oRk9bVdWhJL8K/CWwCPh4Ve08ikOOfOrrGLeQ61/ItcPCrn8h1w4zVH+qnnXJQZKkKfkJc0lSN8NDktRtToZHko8nOZDkrqG29yb5+yR3tL8Lh7Zd2b7uZHeS84faz05yZ9v2e0nS2o9LckNrvzXJqqF91ie5p/2tn6GSn2Gi+lv7O1qNO5O8f6h93tQ/yXN/w9Dzfl+SO4a2zZva2xwmqv9Hk3yx1b89yTlD2+ZN/ZPU/iNJbmm1/HmSE4a2zZva2xxWJvlskl3t//F3tvalSba2uW1NsmRon9l7DKpqzv0BrwXOAu4aansv8GsT9D0d+N/AccBpwNeARW3bbcCrGXy+5NPAz7T2twP/vS2vA25oy0uBe9vtkra8ZI7U/1PAXwHHtfWT52P9E9U+bvvvAL85H2uf4rn/zND8LwQ+Nx/rn6T2LwE/2ZbfCvzWfKy9zWM5cFZbfgnwd63O9wMbWvsG4H1z4TGYk0ceVfV54KHvsvta4PqqeqKq9gJ7gHOSLAdOqKpbavDofAK4aGifTW35RuC8lsznA1ur6qGq+gdgK3DBESmqwyT1vw24qqqeaH0OtPZ5Vf9Uz32b478GrmtN86p2mLT+Ag6/4n4pT382al7VP0ntrwQ+35a3Av+qLc+r2gGqal9V3d6WHwN2MfhWjuF5b+KZ9czaYzAnw2MKv5rkK+3w9vCh20RfebKi/Y1N0P6MfarqEPAI8L1T3Ndc8ArgJ9qh5t8k+eetfaHUD/ATwP6quqetL5Ta3wX8dpIHgA8AV7b2hVD/XcCb2vLFPP0h43ldezuddCZwK3BKVe2DQcAAJ7dus/oYHEvh8RHg5cCPAvsYnL6Ayb/yZKqvQpnOPrNtMYPDyXOBXwc2t1cMC6V+gEt4+qgDFk7tbwOuqKqVwBXAx1r7Qqj/rcDlSXYwOJXz7dY+b2tPcjzwp8C7qurRqbpO0DZjj8ExEx5Vtb+qnqyqp4CPMviGXpj8K0/G2vL49mfsk2Qxg1MBD01xX3PBGHBTDdwGPMXgC9AWRP1tnj8H3DDUvCBqB9YDN7XlP2EB/bdfVV+tqtdX1dkMXjh8rW2al7Un+R4GwXFtVR1+zve3U1G028OnrGf3MZjpi0IdF49W8cwLZ8uHlq9gcK4P4AyeedHoXp6+aPQlBq/UD180urC1X84zLxptrqcvGu1l8Ap/SVteOkfq/xXgv7TlVzA4xMx8rH987a3tAuBvxrXNu9onee53Aa9ry+cBO+Zr/RPUfviNIc9jcO7+rfO49rQaf3dc+2/zzAvm758Lj8GM/4/xXT6I1zE4NfUdBol4KfBHwJ3AVxh8H9ZwmPwHBq9IdtPeVdDa1zA4Z/o14MM8/Yn6FzB4BbeHwbsSXja0z1tb+x7gl+dQ/c8HPtnquR34l/Ox/olqb+1/CPzKBP3nTe1TPPc/Duxg8A/FrcDZ87H+SWp/J4N3Hf0dcNXhOuZb7W0OP87gVNFXgDva34UMrklsA+5pt0uH9pm1x8CvJ5EkdTtmrnlIkuYOw0OS1M3wkCR1MzwkSd0MD0lSN8NDktTN8JAkdft/jXNylOLlbckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150235.398 \n",
      "Standard deviation of investment sum at death: 2865.766\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150146.924 \n",
      "Standard deviation of investment sum at death: 1898.868\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150115.985 \n",
      "Standard deviation of investment sum at death: 1619.531\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150126.267 \n",
      "Standard deviation of investment sum at death: 1777.925\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean investment sum at death: 150062.311 \n",
      "Standard deviation of investment sum at death: 1189.190\n"
     ]
    }
   ],
   "source": [
    "n_sims = 1000\n",
    "\n",
    "# Let's do 10 simulations of 1000 runs of Fannie's life\n",
    "for hist in range(10):\n",
    "    money_at_end = []\n",
    "    for trial in range(n_sims):\n",
    "        # Initial sum invested\n",
    "        sum_ = 150000\n",
    "        # One run of Fannie's life\n",
    "        for year in range(50, 105):\n",
    "            if rand.uniform(0,1) < data[year]: # Survival condition\n",
    "                if year < 70: # While she doesn't turn 70, Fannie Mae just keeps adding to her investments\n",
    "                    sum_ *= 1+rand.normal(loc = 0.08, scale = 0.09) # Compound investments by the interest rate of the year\n",
    "                    sum_ += 10000 # Add another $10,000 to the pot every year while she's below 70\n",
    "                else: # After she turns 70, we start subtracting funds from the investment pot\n",
    "                    sum_ -= 65000\n",
    "                    sum_ *= 1+rand.normal(loc = 0.08, scale = 0.09) # Assume Fannie lets her investments keep growing after retirement\n",
    "            else: # Dies\n",
    "                break\n",
    "        money_at_end.append(sum_)\n",
    "\n",
    "    plt.hist(money_at_end)\n",
    "    plt.title(f'Histogram #{hist+1}')\n",
    "    plt.show()\n",
    "    print(f'Mean investment sum at death: {np.mean(money_at_end):.3f} \\nStandard deviation of investment sum at death: {np.std(money_at_end):.3f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}