lab01_course3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1: Variance of Sample Means and Correlation\n",
    "\n",
    "Welcome to Lab 1 and Data 8.3x!\n",
    "\n",
    "In this lab we will learn about [the variance of sample means](https://www.inferentialthinking.com/chapters/14/5/variability-of-the-sample-mean.html) as well as ways to understand and quantify [the association between two variables](https://www.inferentialthinking.com/chapters/15/1/correlation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell, but please don't change it.\n",
    "\n",
    "# These lines import the Numpy and Datascience modules.\n",
    "import numpy as np\n",
    "from datascience import *\n",
    "\n",
    "# These lines do some fancy plotting magic.\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "import warnings\n",
    "warnings.simplefilter('ignore', FutureWarning)\n",
    "warnings.simplefilter('ignore', UserWarning)\n",
    "\n",
    "# These lines load the tests.\n",
    "from gofer.ok import check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. How Faithful is Old Faithful? \n",
    "\n",
    "(Note: clever title comes from [here](http://web.pdx.edu/~jfreder/M212/oldfaithful.pdf).)\n",
    "\n",
    "Old Faithful is a geyser in Yellowstone National Park in the central United States.  It's famous for erupting on a fairly regular schedule.  You can see a video below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fbbb80f6978>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the curious: this is how to display a YouTube video in a\n",
    "# Jupyter notebook.  The argument to YouTubeVideo is the part\n",
    "# of the URL (called a \"query parameter\") that identifies the\n",
    "# video.  For example, the full URL for this video is:\n",
    "#   https://www.youtube.com/watch?v=wE8NDuzt8eg\n",
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"wE8NDuzt8eg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of Old Faithful's eruptions last longer than others.  When it has a long eruption, there's generally a longer wait until the next eruption.\n",
    "\n",
    "If you visit Yellowstone, you might want to predict when the next eruption will happen, so you can see the rest of the park and come to see the geyser when it erupts.  To predict one variable from another, the first step is to understand the association between them.\n",
    "\n",
    "The dataset has one row for each observed eruption.  It includes the following columns:\n",
    "- **duration**: Eruption duration, in minutes\n",
    "- **wait**: Time between this eruption and the next, also in minutes\n",
    "\n",
    "Run the next cell to load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>duration</th> <th>wait</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>3.6     </td> <td>79  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1.8     </td> <td>54  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3.333   </td> <td>74  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2.283   </td> <td>62  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4.533   </td> <td>85  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2.883   </td> <td>55  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4.7     </td> <td>88  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3.6     </td> <td>85  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1.95    </td> <td>51  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4.35    </td> <td>85  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (262 rows omitted)</p>"
      ],
      "text/plain": [
       "duration | wait\n",
       "3.6      | 79\n",
       "1.8      | 54\n",
       "3.333    | 74\n",
       "2.283    | 62\n",
       "4.533    | 85\n",
       "2.883    | 55\n",
       "4.7      | 88\n",
       "3.6      | 85\n",
       "1.95     | 51\n",
       "4.35     | 85\n",
       "... (262 rows omitted)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faithful = Table.read_table(\"faithful.csv\")\n",
    "faithful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 1.1 **\n",
    "<br/>\n",
    "Make a scatter plot of the data.  It's conventional to put the column we will try to predict on the vertical axis and the other column on the horizontal axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.scatter(x=faithful['duration'],y=faithful['wait']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the scatter plot. Does the association between wait times and eruption durations appear to be linear? \n",
    "\n",
    "There's more going on than just a linear association. The eruption durations seem to cluster; there are a bunch of short eruptions and a bunch of longer ones.  Within each of the clusters, these values appear to be roughly linearly correlated, but perhaps with a different correlation coefficient.\n",
    "\n",
    "The overall relationship is positive, which means that longer eruptions have longer waiting times. Even when the association is more nuanced than a simple linear association, we can still compute the correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll plot the data in standard units.  Recall that, if `nums` is an array of numbers, then\n",
    "\n",
    "    (nums - np.mean(nums)) / np.std(nums)\n",
    "\n",
    "is an array of those numbers in standard units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 1.2 **\n",
    "<br/>\n",
    "Compute the mean and standard deviation of the eruption durations and waiting times.  **Then** create a table called `faithful_standard` containing the eruption durations and waiting times in standard units.  (The columns should be named `\"duration (standard units)\"` and `\"wait (standard units)\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "for_assignment_type": "student"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>duration (standard units)</th> <th>wait (standard units)</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0.0984989                </td> <td>0.597123             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>-1.48146                 </td> <td>-1.24518             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>-0.135861                </td> <td>0.228663             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>-1.0575                  </td> <td>-0.655644            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0.917443                 </td> <td>1.03928              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>-0.530851                </td> <td>-1.17149             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1.06403                  </td> <td>1.26035              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0.0984989                </td> <td>1.03928              </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>-1.3498                  </td> <td>-1.46626             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0.756814                 </td> <td>1.03928              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (262 rows omitted)</p>"
      ],
      "text/plain": [
       "duration (standard units) | wait (standard units)\n",
       "0.0984989                 | 0.597123\n",
       "-1.48146                  | -1.24518\n",
       "-0.135861                 | 0.228663\n",
       "-1.0575                   | -0.655644\n",
       "0.917443                  | 1.03928\n",
       "-0.530851                 | -1.17149\n",
       "1.06403                   | 1.26035\n",
       "0.0984989                 | 1.03928\n",
       "-1.3498                   | -1.46626\n",
       "0.756814                  | 1.03928\n",
       "... (262 rows omitted)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "duration_mean = faithful['duration'].mean()\n",
    "duration_std = faithful['duration'].std()\n",
    "wait_mean = faithful['wait'].mean()\n",
    "wait_std = faithful['wait'].std()\n",
    "\n",
    "faithful_standard = Table().with_columns(\n",
    "    \"duration (standard units)\", (faithful['duration'] - duration_mean) / duration_std,\n",
    "    \"wait (standard units)\", (faithful['wait'] - wait_mean) / wait_std)\n",
    "faithful_standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb63ee76a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check('tests/q1_2.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 1.3 **\n",
    "<br/>\n",
    "Plot the data again, but this time in standard units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots.scatter(x=faithful_standard['duration (standard units)'],y=faithful_standard['wait (standard units)']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that this plot looks exactly the same as the last one!  The data really are different, but the axes are scaled differently.  (The method `scatter` scales the axes so the data fill up the available space.)  So it's important to read the ticks on the axes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 1.4 **\n",
    "Among the following numbers, which would you guess is closest to the correlation between eruption duration and waiting time in this dataset?\n",
    "\n",
    "* -1\n",
    "* 0\n",
    "* 1\n",
    "\n",
    "Assign your answer to `closest_correlation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_correlation = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb641bf828>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check('tests/q1_4.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 1.5 **\n",
    "<br/>\n",
    "Compute the correlation `r`.  *Hint:* Use `faithful_standard`.  Section [15.1](https://www.inferentialthinking.com/chapters/15/1/correlation.html) explains how to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9008111683218132"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faithful_standard = faithful_standard.with_column('product of standard units', faithful_standard.column(0) *faithful_standard.column(1))\n",
    "faithful_standard\n",
    "r = np.mean(faithful_standard.column(2))\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb64c3bc50>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check('tests/q1_5.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Variability of the Sample Mean\n",
    "\n",
    "By the Central Limit Theorem, the probability distribution of the mean of a large random sample is roughly normal. The bell curve is centered at the population mean. Some of the sample means are higher, and some lower, but the deviations from the population mean are roughly symmetric on either side, as we have seen repeatedly. Formally, probability theory shows that the sample mean is an unbiased estimate of the population mean.\n",
    "\n",
    "In our simulations, we also noticed that the means of larger samples tend to be more tightly clustered around the population mean than means of smaller samples. In this section, we will quantify the variability of the sample mean and develop a relation between the variability and the sample size.\n",
    "\n",
    "Let's take a look at the salaries of employees of the City of San Francisco in 2014. The mean salary reported by the city government was about $75463.92."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>salary</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>471953</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>390112</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>339654</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>326717</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>326233</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>344187</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>311299</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>310161</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>335485</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>329391</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (38113 rows omitted)</p>"
      ],
      "text/plain": [
       "salary\n",
       "471953\n",
       "390112\n",
       "339654\n",
       "326717\n",
       "326233\n",
       "344187\n",
       "311299\n",
       "310161\n",
       "335485\n",
       "329391\n",
       "... (38113 rows omitted)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries = Table.read_table('sf_salaries_2014.csv').select(\"salary\")\n",
    "salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75463.91814023031"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean = np.mean(salaries.column('salary'))\n",
    "salary_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salaries.hist('salary', bins=np.arange(0, 300000+10000*2, 10000))\n",
    "plots.scatter(salary_mean, 0, marker='^', color='red', s=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 2.1 **\n",
    "<br/>\n",
    "Clearly, the population does not follow a normal distribution. Keep that in mind as we progress through these exercises.\n",
    "\n",
    "Let's take random samples and look at the probability distribution of the sample mean. As usual, we will use simulation to get an empirical approximation to this distribution.\n",
    "\n",
    "We will define a function `simulate_sample_mean` to do this, because we are going to vary the sample size later. The arguments are the name of the table, the label of the column containing the variable, the sample size, and the number of simulations.\n",
    "\n",
    "Complete the function `simulate_sample_mean`. It will not be graded, but if you haven't implemented it correctly, the rest of the lab won't work properly, so this step is crucial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Empirical distribution of random sample means\"\"\"\n",
    "\n",
    "def simulate_sample_mean(table, label, sample_size, repetitions):\n",
    "    \n",
    "    means = []\n",
    "\n",
    "    for i in np.arange(repetitions):\n",
    "        new_sample = ...\n",
    "        new_sample_mean = ...\n",
    "        ...\n",
    "\n",
    "    sample_means = Table().with_column('Sample Means', means)\n",
    "    \n",
    "    # Display empirical histogram and print all relevant quantities – don't change this!\n",
    "    sample_means.hist(bins=20)\n",
    "    plots.xlabel('Sample Means')\n",
    "    plots.title('Sample Size ' + str(sample_size))\n",
    "    print(\"Sample size: \", sample_size)\n",
    "    print(\"Population mean:\", np.mean(table.column(label)))\n",
    "    print(\"Average of sample means: \", np.mean(means))\n",
    "    print(\"Population SD:\", np.std(table.column(label)))\n",
    "    print(\"SD of sample means:\", np.std(means))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 2.2 **\n",
    "<br/>\n",
    "In the following cell, we will create a sample of size 100 from the salaries table and graph it using our new `simulate_sample_mean` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/lib/histograms.py:898: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return n/db/n.sum(), bin_edges\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/_methods.py:217: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  keepdims=keepdims)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/_methods.py:186: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 100, 10000) \n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following two cells, simulate the mean of a random sample of 400 salaries and 625 salaries, respectively. In each case, perform 10,000 repetitions of each of these processes. Don't worry about the `plots.xlim` line – it just makes sure that all of the plots have the same x-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "for_assignment_type": "student"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  400\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 400, 10000)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "for_assignment_type": "student"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  625\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TE4PLly/j2bNnMDIyQv369TFy5EiMHz9evk5UkqL1o0ePHmHRokVK32NhYSG/nNq0aVOcPXsWa9euxYEDB7Bjxw7IZDKYmpqicePG8v+/QhTdtmv58uXYu3cvNmzYAAMDA9jZ2WHixInyDUNFii735ubmYsWKFUrntLe3VyhoQ4YMQWhoKC5duoSjR4/Kc+3i4gI3N7d3nsFS+cfGaqK3FDXnCmlIJiLtwjU0IiISBRY0IiISBRY0IiISBa6hERGRKPAMjYiIRIEFjYiIRIEFjYiIRIEF7RMRFxen6RDKDeZKOOZKOOZK/VjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFLS6oC1btgxSqRQzZszQdChERKTltLagRUVF4d///jeaNWum6VCIiKgc0MqC9vz5c7i5uWHVqlWQSqWaDoeIiMoBrSxoU6ZMgbOzM7p27arpUIiIqJzQ03QA/2vLli24d+8e1q9fL+j9cXFxao5IPJgr4Zgr4Zgr4Zird7O2tn7vr9WqghYXF4cFCxbg0KFDqFChgqCv+ZAf/lMSFxfHXAnEXAnHXAnHXKmfVhW0CxcuID09HXZ2dvJjBQUFOHfuHDZt2oQnT55AX19fgxESEZG20qqC5uTkhDZt2igc8/DwQP369TFt2jRUrFhRQ5EREZG206qCJpVKi+1qrFSpEoyNjdG0aVMNRUVEROWBVu5yJCIiKiutOkNTJiwsTNMhEBFROcAzNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgUWNCIiEgXBBW3RokW4ceNGieM3b97EokWLVBIUERFRWQkuaN7e3rh+/XqJ4yxoRESkSSq75JiVlYUKFSqoajoiIqIyKfXxMdeuXcPVq1flryMiIpCfn1/sfZmZmdi0aROsra1VHyEREZEApRa00NBQ+WVEiUSCzZs3Y/PmzUrfK5VKsX79etVHSEREJECpBW306NFwdHSETCZDjx498NNPP6Fnz57F3mdkZITPP/8cenpa/7xQIiISqVIrkJmZGczMzAAA+/fvR6NGjWBiYvJRAiMiIioLwadUnTt3VmccREREH6TEgubh4QGJRII//vgDurq68PDweOdkEokEq1atUmmAREREQpRY0E6dOgUdHR0UFhZCV1cXp06dgkQiKXWyd40TERGpS4kF7e3t+speExERaRPey5GIiEThvfbZZ2VlITMzEzKZrNhY3bp1PzgoIiKishJc0HJycrBo0SJs27YNGRkZJb6vtDEiIiJ1EVzQpk+fjh07dsDJyQkdO3aEVCpVZ1xERERlIrig7d+/HyNHjoSvr6864yEiInovgjeFSCQStGrVSp2xEBERvTfBBa1v3744ceKEGkMhIiJ6f4IL2vTp03H//n1MmjQJ0dHRSE5ORmpqarH/iIiINEHwGlr79u0BvGmwDggIKPF93OVIRESaILigzZw5U+23tlq2bBn279+Pu3fvomLFimjXrh3mzZuHpk2bqvX7EhFR+Se4oM2ePVudcQAAzpw5g7Fjx8LGxgYymQy///47Bg4ciPPnz8PY2Fjt35+IiMovrXoi5549exRer1u3DhYWFoiMjESfPn00FBUREZUHggvaokWL3vkeiUSCmTNnflBAb8vKykJhYSGbuImI6J0kmZmZxW/IqERpl/wkEglkMhkkEolKN4WMHj0a8fHxOHHiBHR1dZW+Jy4uTmXfj4iINMva2vq9v1bwGdqzZ8+KHSssLERiYiL8/f1x7tw57N69+70D+V8//fQTIiMjcejQoRKLGfBhP/ynJC4ujrkSiLkSjrkSjrlSvw96fIyOjg6srKzg5eWF+vXrq+xy4+zZsxEUFISQkBBYWVmpZE4iIhI3lT0PrVOnTjh8+PAHz+Pp6SkvZg0bNlRBZERE9ClQ2S7HmJgY6Oh8WH388ccf8ddffyEgIABSqRQpKSkAACMjI1SuXFkVYRIRkUgJLmg7duxQevz58+c4d+6c/G78H8Lf3x8A4OzsrHDc09Pzo/TBERFR+SW4oLm7u5c4VqNGDUydOvWD19AyMzM/6OuJiOjTJbigXb58udgxiUQCqVSKKlWqqDQoIiKishJc0CwsLNQZBxER0QdR2S5HIiIiTWJBIyIiUWBBIyIiUWBBIyIiUWBBIyIiURBU0F69eoXq1atjyZIl6o6HiIjovQgqaJUqVULNmjVRtWpVdcdDRET0XgRfchw4cCCCg4NRWFiozniIiIjei+DG6n79+uH06dNwdHTEyJEjYWVlBUNDw2Lva9u2rUoDJCIiEkJwQXv7hsFRUVGQSCQK4+p4YjUREZFQggva6tWr1RkHERHRBxFc0IYNG6bOOIiIiD7Ie/WhxcfHIzIyEs+fP1d1PERERO+lTAVt165daN68Odq3b4++ffsiNjYWAJCeno62bdsiODhYLUESERG9i+CCtm/fPvzrX/9Cw4YNsWDBAshkMvlYjRo10LBhQ+zcuVMtQRIREb2L4IK2dOlSdOvWDXv27FG6ntauXTtcu3ZNpcEREREJJbig3blzB/369Stx3MTEBGlpaSoJioiIqKwEF7RKlSohOzu7xPH79++jRo0aKgmKiIiorAQXtK5du2L79u3Izc0tNpaUlIQtW7agR48eKg2OiIhIKMF9aHPnzoWDgwO6deuGgQMHQiKR4MiRIwgPD8eWLVugq6sLT09PdcZKRERUIsFnaPXr18fff/8NU1NTeHt7QyaTYfXq1fjjjz/QokULHDp0CHXr1lVnrERERCUSfIYGAI0aNUJwcDAyMzNx7949FBYWwsrKCjVr1lRXfERERIKUqaAVkUqlsLGxUXUsRERE761MBS0zMxOrV6/G33//jcTERACAhYUFevfuDQ8PD0ilUrUESURE9C6C19Du3buHzp07Y8mSJcjPz0eXLl3QpUsX5OfnY8mSJbC3t0d8fLw6YyUiIiqR4DO0GTNm4MWLF9i3bx+6du2qMHby5El8++238PT0xO7du1UeJBER0bsIPkOLiIjA+PHjixUzAPjiiy/w/fff49y5cyoNjoiISCjBBa1atWqlrpFJpVJUq1ZNJUERERGVleCC9u233yIgIAAvX74sNvb8+XMEBARg5MiRKg2OiIhIKMFraNbW1pBIJGjXrh2GDh2KevXqAXjzsM+dO3fCxMQE1tbWxZ6J5uLiUuag/P39sWLFCqSkpKBx48ZYuHAhOnXqVOZ5iIjo0yHJzMyUvfttgLGx8bsnk0gUnpMmkUiQkZFRpoD27NmDf/3rX1i6dCns7Ozg7++P7du3IzIyknci+QBxcXGwtrbWdBjlAnMlHHMlHHOlfoLP0Pbv36/OOORWr16NYcOGYdSoUQCAxYsX49ixY9i0aRPmzZv3UWIgIqLyR3BB69y5szrjAADk5uYiNjYWEydOVDjeo0cPnD9/Xu3fn4iIyi/Bm0I+hvT0dBQUFMDExEThuImJCZ4+faqhqIiIqDx4r3s5apO4uDhNh1BuMFfCMVfCMVfCMVfv9iHrjFpV0GrUqAFdXV2kpqYqHE9NTUWtWrWUfg0XWYXhgrRwzJVwzJVwzJX6adUlx4oVK6J169YIDw9XOB4eHo4OHTpoKCoiIioPtOoMDQA8PDzw/fffo23btujQoQM2bdqE5ORkfPfdd5oOjYiItJjggrZo0SL0798fTZs2VTp+8+ZNhISEwNPT84MC+uqrr5CRkYHFixcjJSUFTZo0QWBgICwsLD5oXiIiEjfBlxy9vb1x/fr1Esdv3ryJRYsWqSSocePG4erVq3j69ClOnjwJe3t7lcxLRETipbI1tKysLFSoUEFV0xEREZVJqZccr127hqtXr8pfR0REID8/v9j7MjMzsWnTJu7gISIijSm1oIWGhsovI0okEmzevBmbN29W+l6pVIr169erPkIiIiIBSi1oo0ePhqOjI2QyGXr06IGffvoJPXv2LPY+IyMjfP7559DT07pNk0RE9IkotQKZmZnBzMwMwJubEzdq1KjYbamIiIi0gVbdnJiIiOh9leka4bFjx7Bt2zYkJCQgMzNT4dlnwJt1ttjYWJUGSEREJITggrZixQrMnz8ftWrVgo2NTYkN1kRERJoguKD5+fmha9eu2LVrF/vNiIhI6whurM7MzISzszOLGRERaSXBBa1t27Z8lg8REWktwQVtyZIlCA0NRWBgoDrjISIiei+C19BGjhyJ3NxcjB8/HlOnToW5uTl0dXUV3iORSBAZGanyIImIiN5FcEGrWbMmTExM0KBBA3XGQ0RE9F4EF7SwsDB1xkFERPRBVPb4GCIiIk0qU0HLyMiAl5cXevfuDRsbG1y4cEF+fNGiRbh9+7ZagiQiInoXwZccHzx4gD59+iAjIwNNmzZFQkIC/vnnHwBA9erVsWfPHqSlpWHx4sVqC5aIiKgkggvavHnzIJPJEBkZiSpVqhTbHNK3b1+usxERkcYIvuR44sQJuLm5wcrKChKJpNi4paUlnjx5otLgiIiIhBJc0F6/fg2pVFri+PPnz6Gjwz0mRESkGYIrUJMmTXD27NkSx8PCwtCyZUuVBEVERFRWggvaDz/8gODgYCxZsgTPnj0DABQWFuLOnTsYN24coqOj4eHhobZAiYiISiN4U8jgwYPx6NEj/P777/j9998BAIMGDQIA6Ojo4JdffkGfPn3UEyUREdE7lOmJ1VOnTsXgwYMREhKCe/fuobCwEJ9//jn69+8PKysrNYVIRET0bmUqaABQp04duLu7qyMWIiKi9yZ4DS0yMhLLli0rcXz58uXyO4cQERF9bILP0BYtWlTqtv1r167hzJkzCAoKUklgREREZSH4DO3KlSuwtbUtcbx9+/a4fPmySoIiIiIqK8EF7dWrV0rvEPK2rKysDw6IiIjofQguaA0aNMDx48dLHD969Cjq1aunkqCIiIjKSnBBGzlyJI4cOYKZM2fKG6uBN4+OmTFjBo4fP45vv/1WLUESERG9i+BNIW5ubrh69So2bNgAf39/1KpVCwDw9OlTyGQyDBs2DD/88MN7B5KXlwcvLy8cOXIECQkJqFKlCrp06YJ58+ahbt267z0vERF9GsrUh7ZixQp5Y3VCQgIAwMrKCs7OzujcufMHBfLq1StcvnwZP/74I1q0aIEXL17g559/hqurK86ePQs9vTK3zBER0SdEUJXIzc1FVFQUzMzM0KVLF3Tp0kXlgVSrVg179+5VOLZ8+XLY2dnh9u3baNasmcq/JxERiYegNTQ9PT0MHDiw1E0h6vDy5UsAKLX/jYiICBB4hqajowMLC4uPui0/NzcXP//8MxwdHfHZZ5+V+L64uLiPFlN5x1wJx1wJx1wJx1y9m7W19Xt/rSQzM1Mm5I0bNmzAqlWrcPToUZiYmLz3NywSGBiIqVOnyl/v2rULnTp1AgDk5+dj3LhxuHXrFg4cOIDq1at/8Pf71MXFxX3QP5RPCXMlHHMlHHOlfoJ3Wrx69QqVKlWCjY0NnJycYGVlBUNDQ4X3SCQSTJo0SdB8ffr0Qbt27eSvzc3NAbwpZmPHjsWNGzcQGhrKYkZERIIIPkMzNjZ+92QSCTIyMt47mLy8PIwZMwY3b95EaGgozMzM3nsuUsRPh8IxV8IxV8IxV+on+AxN3fdpzM/Px6hRoxATE4MdO3ZAIpEgJSUFAFC1atViZ4NERERvE1zQLCws1BkHHj9+jAMHDgAAunXrpjC2evVqDB8+XK3fn4iIyrcydyvHx8fjzJkzSE1NxeDBg2FpaYnc3FykpKTA1NQUFStWfK9ALC0tkZmZ+V5fS0REJLigFRYWYurUqdi2bRtkMhkkEgnat28vL2j29vaYMWMGJk6cqM54iYiIlBJ8c+KlS5ciICAAc+bMwZEjRyCT/XcvSeXKldG/f3+EhoaqJUgiIqJ3EVzQ/vzzT4wYMQLTp09X+piYpk2s7cYKAAAgAElEQVSbIj4+XqXBERERCSW4oD158gRt27YtcdzQ0JAP+CQiIo0RXNBq1aqFxMTEEsdjY2P5mBciItIYwQVtwIAB2LRpk8JlRYlEAgA4cuQIdu7ciYEDB6o+QiIiIgEEF7RZs2ahTp066Nq1K9zc3CCRSLBs2TJ8+eWXGDJkCJo3b45p06apM1YiIqISCS5oVatWxeHDhzFt2jQ8ffoUBgYGiIyMRHZ2NmbNmoUDBw7wbh5ERKQxZWqsNjAwwPTp0zF9+nR1xUNERPRe3lnQcnJycODAATx48ADVq1dH7969edNgIiLSOqUWtKSkJPTt2xcPHjyQN1JXqlQJO3fuRJcuXT5KgEREREKUuobm5eWFxMREuLu746+//sLChQthYGAAT0/PjxUfERGRIKWeoZ04cQJDhw6Fl5eX/FitWrUwbtw4PH78GJ999pnaAyQiIhKi1DO0lJQUdOjQQeGYnZ0dZDIZHj16pNbAiIiIyqLUglZQUAADAwOFY0Wvc3Jy1BcVERFRGb1zl2NCQgIuXrwof/3ixQsAbx4nXrly5WLvL+1+j0REROoiyczMlJU0aGxsLL+91duKnoem7FhGRobqo6QPFhcXB2tra02HUS4wV8IxV8IxV+pX6hna6tWrP1YcREREH6TUgjZs2LCPFQcREdEHEXwvRyIiIm3GgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKKgtQVtypQpkEqlWLlypaZDISKickArC9q+fftw8eJFmJubazoUIiIqJ7SuoCUmJmLWrFnw9/eHnt47H6hNREQEQMsKWn5+PsaNG4cff/wRjRo10nQ4RERUjmhVQVu4cCGqV6+OsWPHajoUIiIqZzR2TS8wMBBTp06Vv548eTK2b9+O06dPl2meuLg4VYcmWsyVcMyVcMyVcMzVu1lbW7/310oyMzNlKoxFsJcvXyI1NVX+esuWLVixYgV0dP570lhQUAAdHR2YmZnhxo0bmghTNOLi4j7oH8qnhLkSjrkSjrlSP42doVWpUgVVqlSRv54wYQKGDBmi8J5BgwZh0KBBGDVq1McOj4iIyhmt2UZoYmICExMThWN6enowNTXlpxoiInonrdoUQkRE9L605gxNmatXr2o6BCIiKid4hkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKLAgkZERKIgyczMlGk6CCIiog/FMzQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiKpOCggI8ffoUT58+RUFBgabDKTfi4+ORn5+v6TBETesLWlxcHJ48eSJ/feTIEXz99dfo1KkThgwZgqNHj2owuvKhefPmiI+P13QYWmXGjBmIiIjQdBjlyv79+9G7d2+Ym5ujcePGaNy4MczNzdG7d2+EhoZqOjytZ2dnx9/DMrp69SqqV68u+P16aoxFJdzc3PDzzz+jdu3aCA4OhpubG1xcXNC+fXvcvXsXw4YNw7p16+Di4qLpUDVu1apVSo8nJSVhy5YtqFWrFgBgwoQJHzMsreTv74+NGzeiQYMGGDVqFIYOHVqmX5xPzebNmzFz5kwMHToU7u7uMDExAQCkpqbi+PHjGDt2LHx8fDBq1CgNR6p533zzjdLjBQUFmD59OipXrgwA2Llz58cMq9ySyYTf+0Pr7xRSu3ZtREREwNLSEj169MCgQYPg4eEhH9+yZQs2bNiAM2fOaDBK7WBsbIzatWtDV1dX4fjDhw9hbm4OPT09SCQSXL58WUMRag9jY2Ps3LkTYWFhCA4ORl5eHpycnDBy5Eh88cUXmg5P67Rp0wZTp07FyJEjlY5v3boVy5YtQ2xs7EeOTPsYGxujU6dOsLS0VDi+c+dO9OnTB9WqVQMArFmzRhPhaZX+/fuXOp6dnY3Y2FhkZGQImk/rC5qlpSVCQ0PRokULNGjQAHv37kXz5s3l4/fv34e9vb3CZclP1ZQpU3Dp0iVs3LgR1tbW8uM1a9bEmTNn0LhxYw1Gp12MjY1x584dmJiYIDs7G7t27cLWrVsRExMDKysrjBw5EsOGDYOpqammQ9UKZmZmOH36tMK/q7fduXMHXbt2RXJy8keOTPvs3r0b8+bNw9y5cxXO1vh7WJyJiYn8MrYyGRkZCA4OFlzQtP6SY9euXbFr1y60aNECrVu3xunTpxUK2qlTp1C7dm0NRqg9fH19sW/fPgwcOBAzZszA6NGjNR1SuWBkZITRo0dj9OjRuHLlCrZs2YLly5fj999/R2pqqqbD0wqNGzfGxo0b4e3trXR88+bN/EP9H66urmjXrh3GjRuHo0ePYvny5ahSpYqmw9JKDRs2RK9evUo8879y5QqCg4MFz6f1BW3evHlwdHREcnIyOnXqhN9++w0xMTFo1KgR4uLiEBwcDF9fX02HqTWcnZ3Rpk0buLm54ciRI1i5cqWmQ9JKEolE6fGWLVti6dKl8PLywp49ez5yVNrLy8tLvgmre/fu8vXYp0+f4sSJE0hKSkJgYKCGo9QeVlZWOHToEBYsWIDOnTtj7dq1Jf6b+5S1atWq1CUQfX191KlTR/B8Wn/JEQAePHgALy8vHDp0CFlZWQAAPT092NjYYNKkSXByctJwhNqnoKAAv//+O3bs2IGUlBScPXuWn6Df8vYlRxLmwYMH2LRpE6KiovD06VMAQK1atWBra4vvvvuu2JoRvXH8+HF4eHggJSUF586d4+/hW16/fo2CggJUqlRJJfOVi4JWRCaTITU1FYWFhahRowYqVKig6ZC03unTp3H27Fm4ubmhRo0amg6H6JOUnp6OmzdvwsbGRmV/vKm4clXQiIjKk8TERIWzWQsLCw1HpJ3i4+Nx/vx5hVx16NAB9evXL9M8Wr+GBgCXLl3C2rVrlf7A7u7uaNOmjYYj1B7JycnYuHEjIiMjkZKSAh0dHVhaWsLJyQnDhw8vtqX/U8Zcqc748ePx5MkThISEaDoUrbB69WqsWbMGSUlJ8j4qiUQCc3NzeHh4wN3dXcMRaofnz59j/PjxOHToEIyMjFCzZk0AQFpaGl69egVHR0f4+fmhatWqgubT+juFhIaGolevXkhNTYWbmxt8fX3h6+sLNzc3pKeno3fv3ggLC9N0mFohJiYGtra2OHLkCPLz8xEfH49WrVrByMgIc+fORd++ffHy5UtNh6kVmCvVkslkKCws1HQYWsHHxwfe3t747rvvcOzYMVy9ehVXr17FsWPHMGbMGHh7e2Px4sWaDlMrzJw5EwkJCTh48CAePXqE2NhYxMbG4tGjRzh48CASEhIwc+ZMwfNp/SXHjh07wtXVFdOnT1c6vmzZMgQGBiIyMvIjR6Z9HB0d0a1bN8yaNQsA8Ndff2HDhg04evQoMjMz0b9/f3Tq1AmLFi3ScKSax1yRujRr1gwLFy7EgAEDlI6HhITA09MTN2/e/MiRaR8LCwvs2bMH7dq1Uzp+4cIFuLq6IjExUdB8Wn+Gdu/evRL/YQBvOs3v37//ESPSXpcvX1Zo5Bw8eDAuX76Mp0+fQiqVYsGCBbwk9B/MVdk9fvwYv/76K/r16wdbW1vY2tqiX79+8PLywuPHjzUdntbIyMhAo0aNShy3trZGZmbmR4xIu5XWzlDWVgetL2iff/55qTc+DQsLg5WV1ccLSIvVrFlT4Y4pKSkpyM/Plzd11qtXD8+ePdNUeFqFuSqbiIgIdOjQAXv37kWTJk3g6uoKV1dXNGnSBHv37oWdnR2vkvyHjY0NfHx8kJubW2wsNzcXS5cuhY2NjQYi0z6Ojo6YOHEioqKiio1FRUVhypQp6NOnj+D5tH5TyOzZszF27FicPn1aaUPnqVOnsHHjRg1HqR2cnJwwbdo0zJ8/H/r6+li8eDHs7e1haGgI4M2TC0q6xcynhrkqm9mzZ2PYsGHw8fFROu7p6YnZs2cjPDz8I0emfRYvXgwXFxc0aNAAHTt2VPibFRERgUqVKpXp7hdi5uPjg3HjxqFXr16oUqWKvLUoPT0dWVlZcHBwKPHfnDJav4YGvLmO6ufnhwsXLhRr6Bw/fjxsbW01HKF2yMrKwsSJE7F//34UFBTA1tYW69atk5/BHj9+HC9evMDAgQM1G6gWYK7KhvdyLJuXL18iMDBQaRO6q6ur4F17n4rbt2/jwoUL8lvNFeWqYcOGZZqnXBQ0KpucnBzk5+fLH1NBJWOuhGnVqhV+/PFHfPvtt0rHt27diqVLl/JJDqRRWn/JkcrOwMBA0yGUG8yVMBMnTsS0adNw6dIldOvWrdil/+3bt2PhwoUajlK7qKpZWOyys7Oxe/fuYrmys7PDoEGDYGRkJHiucn+G9uuvvyIlJaXEh1t+aq5evYpVq1YpNAtbWVmhb9++mDRpEi91vIW5Kps9e/ZgzZo1iI2NRUFBAQBAV1cXrVu3hoeHBx+y+x+qbhYWs1u3bsHFxQVZWVno1KmTwoNjIyIiULlyZezZs0fw/S/LfUEbMGAAHjx4wEsdAI4dO4YRI0agZ8+eMDAwQGhoKIYPHw4jIyOEhIRAJpPh0KFDfMYXmKsPkZeXh/T0dADgPVWV+P7773HlyhUsX74cdnZ2CmPnz5/H1KlT0bJlS/j5+WkoQu3Rr18/mJiYYO3atcWuluTk5MDd3R1Pnz4tdaf728p9QaP/6tKlC7777juMGTMGABAeHg5PT09cuHABeXl5cHV1xWeffcYn5YK5IvVRdbOwmJmbmyM8PLzEM7AbN27AwcEBSUlJgubT+j40Ei4uLg4ODg7y1926dcP9+/eRnJyMChUqwNPTE4cPH9ZghNqDuSJ1UmWzsJhJpVLcvXu3xPH4+HhIpVLB85WLTSGqXDQUM3Nzc8TFxcmfSxUfH4/CwkJUr14dAFC7dm1kZ2drMkStwVyRuhQ1C//xxx9o3769wtj7NAuL2ciRI+Hu7o64uDh0795dYQ0tPDwcvr6+ZbqRs9YXtP9dNDQzMwPw5gf++eef4e3tXaZFQzH75ptvMHHiREydOhX6+vpYu3Yt+vTpg4oVKwJ4swmCD2F8g7kidVF1s7CYzZ49G4aGhvDz88OCBQvkZ68ymQympqaYNm0aJk+eLHg+rV9DU/WioZjl5+fj119/RWBgIF6/fg0HBwd4e3vLf6EuXryInJwc2NvbazhSzWOuSN1U1Sz8qUhISFC4Avc+tzTU+oKm6kVDIiISJ62/5Fi0aFhSQSvrouGnoqCgAOnp6dDR0ZH3wZByzBWpGtf9hVPlA5y1vqCpetFQ7P7++2/4+vri0qVLyMvLAwBUrlwZjo6OmDt3LurWravhCLUHc0XqwHV/4UJDQzF69Gh07twZbm5uxf6+9+7dG5s3b4aTk5Og+bT+kiMA+Pr6ws/PDykpKZBIJPJHmpuamuKHH34o06KhmO3cuRMzZszAqFGjYGBggG3btmHYsGGoW7cugoKCcOvWLRw+fJi33gFzRerDdX/hVP0A53JR0IoULRrm5eWhRo0a/ITzP2xtbTFr1ix89dVXAICYmBiMGDEC165dg0QiwZgxY5Cbm4uAgAANR6p5zBWpC9f9hTM1NcWZM2dKfIpDXFwcOnfujJSUFEHzlavGaisrK9ja2uKbb77hTWWVePjwIdq2bSt/3aZNG6SkpMgf6eHh4YHTp09rKjytwlyRuqi6WVjMVP0AZ61fQ1Om6JIjKbKwsMClS5fk/VMxMTHQ0dGR3xnd2NgY+fn5mgxRazBXpC5c9xdO1Q9wLpcFjZQbN24cJk2ahOjoaBgYGCAgIABDhgyBrq4ugDd3KeCa0BvMFamLqpuFxczZ2Rnm5ubw8/PDunXrij0MNSwsrEwPcC5Xa2hFdu3ahb59+3LrqxIbN25UaBaeMWOG/PJsfHw8CgoK2Nj5H8wVqZsqmoVJuHJZ0IiISFwSExPlxd/U1PS92mZ4yVGkHj58qPDQyqKb7lJxzBWpmiqbhcVu9erVWLNmDZKSkuT7IyQSCczNzeHh4SGumxNT2fj7+8PX1xdPnjxROG5rawtvb2+0bt1aQ5FpH+aK1EHVzcJi5uPjg5UrV2Ly5MlwcHBQyNXx48fh7e2N7OxszJgxQ9B8vOQoIitXrsSaNWswdepUGBgYYPXq1Rg0aBBsbGywa9cu7N+/H2FhYfx0COaK1EfVzcJi1qxZMyxcuBADBgxQOh4SEgJPT0/cvHlT0HwsaCLSsmVLLF26FD179gQA3L17F7169cKdO3egp6cHT09P3LlzB8HBwRqOVPOYK1IXVTcLi5m5uTlOnDiBRo0aKR2/efMmevTowSdWf4rS0tIUduXVr18fL168QFpaGgBgxIgRiIqK0lR4WoW5InVRdbOwmNnY2MDHxwe5ubnFxnJzc7F06VLY2NgIno9raCJSv359HD9+HN999x0A4MSJE6hYsSJMTU0BAPr6+nz8+38wV6Quqm4WFrPFixfDxcUFDRo0QMeOHRVyFRERgUqVKpXpKgkLmohMmzYNbm5uOH78OAwMDHDgwAF8//338j/MZ86cQZMmTTQcpXZgrkhdVN0sLGZNmzZFdHQ0AgMDERUVhcePHwN4k6v58+fD1dUVVatWFTwf19BE5siRIwrNwqNGjZKPZWRkAAC3pf8Hc0UkLixoRERqoopm4U9BfHy80p69st5+jpccRSg7OxuxsbEKzcKtWrXimpASzBWpgyqbhcXs+fPnGD9+PA4dOgQjIyP5E+PT0tLw6tUrODo6ws/PT/BlRxY0ESksLMT8+fOxYcMGvH79GsB/n0xQp04d+Pj4oE+fPpoMUWswV6Quqm4WFrOZM2ciISEBBw8ehJ2dncLY+fPnMXXqVMycORN+fn6C5uMlRxGZP38+Dh48iF9++QUGBgbw8fFB79690adPH+zatQsrVqzAjh070KNHD02HqnHMFamLqpuFxczCwgJ79uxBu3btlI5fuHABrq6uSExMFDQfz9BEZOfOndi0aRM6deoEAGjYsCFsbW0xfvx4zJkzBxUqVIC3tzf/SIO5IvXJyMgosVEYAKytrZGZmfkRI9JupV3eL+ulfzZWi0h2djZq164tf21qaoqcnBz5L8+AAQNw7do1TYWnVZgrUhdVNwuLmaOjIyZOnKj0JgZRUVGYMmVKmS798wxNRJo2bYq//voLnp6eAICgoCAYGRnJm4ULCwtRsWJFTYaoNZgrUhdVNwuLmY+PD8aNG4devXqhSpUqqFGjBgAgPT0dWVlZcHBwgI+Pj+D5WNBE5KeffsLXX3+NAwcOwMDAANHR0fj111/l48eOHUPLli01GKH2YK5IXVTdLCxmUqkUu3fvxu3bt3HhwgWkpqYC+G8TelkfsMtNISJz9epV7N27V94s3L17d02HpLWYKyJxYUEjIlIDVTULi112djZ2795dLFd2dnYYNGgQjIyMBM/FgiZCCQkJiIiIkDcLW1paonv37rzMoQRzRaqm6mZhMbt16xZcXFyQlZWFTp06KfTsRUREoHLlytizZw8aN24saD4WNBHJzs6Gu7s7QkJCALzZ8mpiYoK0tDQYGhpi3rx5cHNz03CU2oG5InX5/vvvceXKFSxfvrzEZuGWLVsKbhYWs379+sHExARr166FgYGBwlhOTg7c3d3x9OnTUh/H8zZuChGROXPmICUlBWfPnoWBgQF++eUXWFlZwdPTE0FBQfD09IRUKsXgwYM1HarGMVekLgcPHiyxWbhDhw7w9fWFq6urBiLTPhcvXkR4eHixYgYABgYG+PHHH+Hg4CB4PhY0Edm/fz+CgoLQtGlTAMAff/yBxo0bw9PTE99++y1ycnKwYsUK/pEGc0XqpcpmYTGTSqW4e/duiZcU4+PjIZVKBc/HxmoRyc/PR5UqVeSvjYyMkJeXh1evXgEAevTogbi4OE2Fp1WYK1IXVTcLi9nIkSPh7u6O5cuXIzY2Fo8fP8bjx48RGxuL5cuXY8KECRg9erTg+XiGJiI2NjZYs2YNli5dCuDNHb9NTEzki9IvX74s044hMWOuSF1U3SwsZrNnz4ahoSH8/PywYMEC+dmrTCaDqakppk2bhsmTJwuej5tCRCQ2NhYuLi7Q1dVFhQoVkJ6ejrVr12LQoEEAgA0bNuDixYtcjAZzReqnqmbhT0VCQoLCtn0rK6syz8GCJjLJycn4+++/8fr1a3Tt2lXwdtdPEXNFJC4saEREKqbKZmGxu3TpEtauXau0Cd3d3R1t2rQRPBcLmgidPHkSkZGRCs3Cffv25R0KlGCuSNVU3SwsZqGhoRg9ejQ6d+5c7GGo4eHhOHPmDDZv3gwnJydB87GgiUhqaiq++eYbxMTEQEdHB4WFhWjZsiWSkpKQlpYGDw8PLFiwQNNhagXmitRF1c3CYtaxY0e4urpi+vTpSseXLVuGwMBAREZGCpqP2/ZFxNPTE2ZmZkhISMDjx48xduxYNG7cGLdv38aePXsQEBCAtWvXajpMrcBckbpcvHgRnp6epTYLX7x4UQORaZ979+6V+GRvAOjfvz/u378veD4WNBE5evQofv75Z1StWhX6+vqYP38+goKC8OLFC3zxxRdYuHAhNm3apOkwtQJzRepS1CxckrI2C4vZ559/XuqZalhYWJl2O7IPTUQqVqyocBcCiUSCgoICFBQUAHhz253ExERNhadVmCtSl6Jm4bi4OHTv3r3YupCvry/c3d01HKV2mD17NsaOHYvTp0+je/fuCg9DPXHiBE6dOoWNGzcKno8FTUTs7Ozg5eWFNWvWQF9fH/PmzYOVlRWMjY0BvPmF4ifDN5grUhdVNwuLmbOzM8zNzeHn54d169Yp7HK0tbVFWFgYbG1tBc/HTSEikpCQABcXFyQmJkIikcDIyAhbtmxBt27dAAB//vkn7t69i3nz5mk2UC3AXNHHoIpmYRKOBU1kXr16hcjISOTm5qJ9+/by2+5QccwVkfZITEyUF39TU1PUrVu3zHOwoBERqZgqm4XFbvXq1VizZg2SkpIgk70pRxKJBObm5vDw8CjTeiPX0ETmn3/+we7du4s1C/fr1w9ffPGFpsPTKswVqcPbzcJubm7FNoX07t27TM3CYubj44OVK1di8uTJxRqrjx8/Dm9vb2RnZ2PGjBmC5uMZmojcu3cPzs7OyMnJgb6+Ph4/foxevXohPT0dMTEx6N+/P/z9/aGnx88xzBWpi6qbhcWsWbNmWLhwYYm9aCEhIfD09MTNmzcFzcc+NBHx9PTEl19+iTt37uDatWuYN28eCgsLcfToUVy4cAGXLl3C4sWLNR2mVmCuSF1U3SwsZhkZGWjUqFGJ49bW1sjMzBQ8HwuaiJw9exYTJkyQbxN2d3fHiRMnkJGRgfr162PhwoXYsWOHhqPUDswVqYuqm4XFzMbGBj4+PsjNzS02lpubi6VLl8LGxkbwfLyeIiLVqlXDy5cv5a9fvXqF/Px8VKhQAcCb0/uUlBRNhadVmCtSF1U3C4vZ4sWL4eLiggYNGqBjx44KuYqIiEClSpUQHBwseD4WNBHp1q0bZs+ejaVLl0JfXx8LFixAixYtUKVKFQDAw4cP5YuunzrmitRF1c3CYta0aVNER0cjMDAQUVFRePz4MYA3uZo/fz5cXV1RtWpVwfNxU4iIpKamYtiwYYiOjoZEIsFnn32GgIAAtGrVCgCwb98+JCcn4/vvv9dwpJrHXBGJDwuaCMXHx+P169do2LAhd+m9A3NF6qSKZuFPQXx8vNKevbI+l5AFjYhIxVTZLCxmz58/x/jx43Ho0CEYGRmhZs2aAIC0tDS8evUKjo6O8PPzE3zZkR9JRSY5ORkbN24s1izs5OSE4cOHQ1dXV9Mhag3mitRB1c3CYjZz5kwkJCTg4MGDsLOzUxg7f/48pk6dipkzZ8LPz0/QfDxDE5GYmBg4OzujXr16MDQ0xIULF+Dq6oq8vDwcO3YMjRs3xu7du+UbHz5lzBWpi6qbhcXMwsICe/bsQbt27ZSOF/1eCn2UE/vQRGT27NnyfqqDBw9izZo1iI+Px6ZNm3D58mW8evUKXl5emg5TKzBXpC6qbhYWu7efS1iWMWVY0ETk8uXL+Oabb+SvBw8ejMuXL+Pp06eQSqVYsGABQkJCNBih9mCuSF1U3SwsZo6Ojpg4cSKioqKKjUVFRWHKlCno06eP4Pm4hiYiNWvWxJMnT+R3IUhJSUF+fr78slm9evXw7NkzDUaoPZgrUhdVNwuLmY+PD8aNG4devXqhSpUq8kc4paenIysrCw4ODvDx8RE8HwuaiDg5OWHatGmYP38+9PX1sXjxYtjb28PQ0BAAEBcXB3Nzcw1HqR2YK1IXVTcLi5lUKsXu3btx+/ZtXLhwAampqQD+24TesGHDMs3HTSEikpWVhYkTJ2L//v0oKCiAra0t1q1bJz8LOX78OF68eIGBAwdqNlAtwFwRiQ8Lmgjl5OQgPz8flStX1nQoWo+5InVRVbOw2GVnZ2P37t3FcmVnZ4dBgwbByMhI8FwsaEREKqTqZmExu3XrFlxcXJCVlYVOnTop9OxFRESgcuXK2LNnDxo3bixoPhY0kbl69SpWrVql0CxsZWWFvn37YtKkSfwlegtzRerw/fff48qVK1i+fHmJzcItW7YU3CwsZv369YOJiQnWrl0LAwMDhbGcnBy4u7vj6dOnpT6O520saCJy7NgxjBgxAj179oSBgQFCQ0MxfPhwGBkZISQkBDKZDIcOHYKpqammQ9U45orURdXNwmJmbm6O8PDwEs/Abty4AQcHByQlJQmaj31oIjJ//nz89ttv2Lp1K9avX48///wTJ57bAuoAAByoSURBVE+exPz583H+/HlYWFjgl19+0XSYWoG5InVSZbOwmEmlUty9e7fE8fj4eEilUsHzsaCJSFxcHBwcHOSvu3Xrhvv37yM5ORkVKlSAp6cnDh8+rMEItQdzReqi6mZhMRs5ciTc3d2xfPlyxMbG4vHjx3j8+DFiY2OxfPlyTJgwAaNHjxY8H/vQRMTc3BxxcXGwtLQE8ObTTWFhIapXrw4AqF27NrKzszUZotZgrkhdVN0sLGazZ8+GoaEh/Pz8sGDBAvnZq0wmg6mpKaZNm4bJkycLno8FTUS++eYbTJw4EVOnToW+vj7Wrl2LPn36oGLFigDebIIo+gP+qWOuSF1U3SwsdlOmTMGUKVOQkJCgsG2/qCe0LLgpRETy8/Px66+/IjAwEK9fv4aDgwO8vb3lnxAvXryInJwc2NvbazhSzWOuiMSHBY2ISMVU2SwsdpcuXcLatWuVNqG7u7ujTZs2gudiQROpgoICpKenQ0dHR97YScoxV6RKqm4WFrPQ0FCMHj0anTt3LvYw1PDwcJw5cwabN2+Gk5OToPlY0ETm77//hq+vLy5duoS8vDwAQOXKleHo6Ii5c+eibt26Go5QezBXpA6qbhYWs44dO8LV1RXTp09XOr5s2TIEBgYiMjJS0HwsaCKyc+dOzJgxA6NGjYKBgQG2bduGYcOGoW7duggKCsKtW7dw+PBh3ksOzBWpj6qbhcXM1NQUZ86cgbW1tdLxuLg4dO7cGSkpKYLmY0ETEVtbW8yaNQtfffUVACAmJgYjRozAtWvXIJFIMGbMGOTm5iIgIEDDkWoec0Xq0qRJEyxevBj9+vVTOr5//37MnDkTN2/e/MiRaR87OzsMGTIEU6dOVTru6+uLHTt24Pz584Lm47Z9EXn48CHatm0rf92mTRukpKQgOTkZ5ubm8PDwkP8B/9QxV6QuRc3CcXFx6N69e7F1IV9fX7i7u2s4Su0we/ZsjB07FqdPn0b37t0VHoZ64sQJnDp1Chs3bhQ8HwuaiFhYWODSpUvy/qmYmBjo6OjI/5EYGxsjPz9fkyFqDeaK1EXVzcJi5uzsDHNzc/j5+WHdunUKuxxtbW0RFhYGW1tbwfOxoInIuHHjMGnSJERHR8PAwAABAQEYMmQIdHV1Aby57Q7XhN5grkidVNksLHa2trZlKlql4RqayGzcuFGhWXjGjBnynVbx8fEoKCjgnQr+g7ki0h6JiYny4m9qavpeu4xZ0IiIVEyVzcJit3r1aqxZswZJSUmQyd6UI4lEIl/LLst6Iy85itTDhw8VHlpZdNNdKo65IlV6u1nYzc2t2KaQ3r17l6lZWMx8fHywcuVKTJ48uVhj9fHjx+Ht7Y3s7GzMmDFD0Hw8QxMZf39/+Pr64smTJwrHbW1t4e3tjdatW2soMu3DXJE6qLpZWMyaNWuGhQsXYsCAAUrHQ0JC4OnpKbjFgc9DE5GVK1di6dKlmDRpEnx9fWFtbY1Zs2YhMDAQlpaW6Nu3L2JiYjQdplZgrkhd7t27V+IfaADo378/7t+//xEj0l4ZGRlo1KhRiePW1tbIzMwUPB/P0ESkZcuWWLp0KXr27AkAuHv3Lnr16oU7d+5AT08Pnp6euHPnDoKDgzUcqeYxV6Quqm4WFjMnJyeYmZlh7dq18kc3FcnNzYW7uzuSkpIQFhYmaD6uoYlIWlqawq68+vXr48WLF0hLS4OZmRlGjBjBJ+X+B3NF6qLqZmExW7x4MVxcXNCgQQN07NhRIVcRERGoVKlSmT5UsqCJSP369XH8+HF89913AIATJ06gYsWKMDU1BQDo6+vLmzw/dcwVqYuqm4XFrGnTpoiOjkZgYCCioqLw+PFjAG9yNX/+fLi6uqJq1f9v796DmrqzOIB/EUEEkVgJgUUeDriiBRSRh8IiD5UqdCigFi1FK+IjLFNkl5erorhKQVS6pYAVpThaRQWrQCko2BUQMK2ya1UEsajV8LYswoK89g81NQuxwSYELucz40zJL/nl5FQ5+d37O/dOFHs+KmgMEhQUBD8/PxQUFEBJSQnffPMNNmzYIPjFXFRUhBkzZsg4yuGBckWkSZLNwkynqqoKX19f+Pr6/u656Bwaw1y4cEGoWXj16tWCsebmZgCgbekvUK6ItEmiWXg0qK6uHrBnb7BX66GCRgghEibJZmEma2lpwcaNG/Htt99CRUVFcIPdxsZGtLe345133kFSUpLYhx3pkCMDtbW1oby8XKhZeNasWXROaACUKyJpkm4WZrKQkBDU1NQgJycH1tbWQmNlZWXYvHkzQkJCkJSUJNZ8tEJjkN7eXuzYsQOHDh1CZ2cnAAi+HU6ZMgUxMTG0c+8FyhWRFkk3CzOZrq4uMjIyMHfu3AHHr169imXLluHBgwdizUeN1QwSGRmJ3NxcpKSkICMjA9bW1tixYwfKysrg5eWFNWvWoKCgQNZhDguUKyItkm4WZrrXHQ0Z7JESWqExiJGREY4cOYL58+cDAB4/fgxLS0tUV1dj3LhxiImJwcWLF5GXlyfjSGWPckWkRdLNwky2fv16/Pjjj/j0009hYWEhNMbj8RAYGAhjY2McPHhQrPmooDGIjo4OCgsLBfdc6unpAYfDwc2bN8HhcFBRUQFHR8d+1y4cjShXRFpu3boFd3d3/Pe//31tszC1hQC//PIL1q1bh/z8fKiqqmLy5MkAgKamJjx9+hROTk5ITk6GmpqaWPPRphAGmTlzJtLS0hAaGgoASE9Ph4qKiqBZuLe3t983xtGKckWkRdLNwkzGYrFw5swZ3LlzB1evXkVDQwOAX5vQB3s/QipoDLJlyxasWLEC33zzDZSUlPD9999j165dgvH8/HyYmprKMMLhg3JFpEmSzcKjwfTp01973lFcdMiRYW7cuIGvv/5a0Czs4OAg65CGLcoVkSZJNQszXVtbG86cOdMvV9bW1vD09ISKiorYc1FBI4QQCZJ0szCTVVRUwN3dHU+fPsX8+fOFevZKSkowYcIEZGRkwMjISKz5qKAxUE1NDUpKSgTNwnp6enBwcKB/QAOgXBFJ27BhA/7973/jwIEDIpuFTU1NxW4WZjJXV1ew2WwkJiZCSUlJaKyjowNcLhf19fXIysoSaz4qaAzS1tYGLpeL8+fPA3jew8Fms9HY2Ijx48cjIiICfn5+Mo5yeKBcEWmRdLMwk2lpaeHSpUsiV2C3bt2Ck5MT+Hy+WPNRYzWD/O1vf0NdXR2Ki4vxww8/4N1334WXlxcePnyIqKgoRERE4PTp07IOc1igXBFpkmSzMJOxWCzcvXtX5Hh1dTVYLJbY89EKjUEMDAyQnp6O2bNnA3je42FkZIR79+5BWVkZhw4dwtGjR1FYWCjjSGWPckWkRdLNwkwWFRWFxMREbN68GQ4ODkLn0C5duoS4uDhwuVxBe81voYLGIHp6eigoKBDsourq6oKmpibu3LkDdXV1VFdXw8bGBrW1tTKOVPYoV0RaJN0szHRxcXFISkpCXV2dYPXa19cHDoeDTZs24eOPPxZ7LupDY5A5c+YgISEB+/btA/D8FhZsNluwy6q1tXVQW2CZjHJFpEXSzcJMFxgYiMDAQNTU1Aht2395FZ/BoILGIBEREXB3d8e5c+egoKCApqYmJCYmCsZ5PB4WLVokwwiHD8oVkTZJNQuPFvr6+oIi1tXV9UZz0CFHhqmtrUVubi46OzthZ2cndv/GaES5ItIiyWbh0YjNZqOoqGjQXwiooBFCiARJulmYyUJCQgZ8PDk5GcuWLRPscIyJiRFrPipoDPTPf/4TpaWlQs3CS5cupUvuDIByRSRN0s3CTDZp0iQYGxv32yBTXFwMMzMzKCsrQ05ODpmZmWLNRwWNQRoaGuDl5YXr169jzJgx6O3thampKfh8PhobG+Hv74/IyEhZhzksUK6ItEi6WZjJ9u/fj9TUVHz++eewtbUVPK6uro6ioqJBr2KpsZpBQkNDoampiZqaGjx69Ai+vr4wMjLCnTt3kJGRgWPHjgltfBjNKFdEWiTdLMxkQUFBSE5ORkBAACIjI9Hb2/u75qMVGoPo6uoiNzdXcOPAtrY26Ovro7q6GhMnTkRaWhpiY2PB4/FkHKnsUa6ItEi6WXg0aGlpEWzdT05OhpWV1Rut0GjbPoMoKioKXVZHTk4OPT096OnpAQBYWVnR9eNeoFwRaQkPD8f48eORlJSEyMjIfs3CQUFBg2oWHg3U1NSQkpKC1NRUODs7v/FKjVZoDOLt7Q0ASEhIwLhx47B161bk5+fj2rVrAJ73Vnl7e+POnTuyDHNYoFyRoSCJZuHRpqqqCjweD66uroO+6wWt0Bjk73//O9zd3TF16lTIyclBRUUFqampgvHKykqsWrVKhhEOH5QrMhRebRYmr9fT04Ompiaoqanh/fffh7y8/KDnoBUaw7S3t6O0tBTPnj2DhYWF4DpypL/29naUlZWhs7OTckUk6pdffkFZWRlYLBYsLS2FDm+3tbUhPj6ezqG9kJmZifj4eFy/fh3d3d0AgLFjx8LMzAwBAQFwdXUVey4qaIQQIkG3b9/Ge++9h8bGRvT29mLWrFk4evQodHV1AQD19fUwMjJCc3OzjCOVvZSUFISEhGDlypVwcnIS2kBTUFCAkydPIiYmBqtXrxZrPipoDNPY2IgbN27AxMQE6urqqK+vx9GjR9HZ2Ql3d3fMnDlT1iGOCPX19UhJSaFv0WTQvLy8MHbsWBw8eBCtra0ICwvD1atXkZmZCQMDAyporzAzM8PmzZvh4+Mz4PjRo0exf/9+lJeXizUfFTQGeXkn3NbWVkycOBFff/01Vq9eDQUFBfT29oLP5yMnJwdmZmayDnXYu3HjBhYsWEC/dMigGRoaIjMzU9ASAgBbtmzB2bNnkZmZiYkTJ1JBe0FTUxOFhYWYNm3agOOVlZWws7MT+zZOtCmEQXbv3o333nsPu3fvxpdffglvb284OTnhH//4BwDA398fsbGxOH78uIwjlb3i4uLXjldXVw9RJIRpnj171u+u1Hv27EFfXx9cXV1x6NAhGUU2/BgZGeHw4cP45JNPBhxPSUkZVC8ardAYRF9fH7m5uZg+fTq6urrA4XBw4cIFmJubAwDKy8uxatUq3Lp1S8aRyt6kSZMgJyeHvj7Rf/3l5OToWzQZNEdHR/j5+WHlypX9xkJDQ3HixAk8ffqU/m4BKCoqwvvvvw8tLS04ODhAQ0MDwPND/t999x34fD5OnTqF+fPnizUfrdAYpKurS3AxVAUFBSgrKwvt3FNXV6d/RC9MnjwZe/bswcKFCwccv3nzJtzc3IY4KsIErq6uSE9PH7CgRUdHo6enB4cPH5ZBZMOPra0trly5giNHjoDH4+HSpUsAnvfsubi44KOPPoKenp7Y89EKjUEsLS0RGxsLOzs7AEBubi7s7Owwfvx4AMAPP/yADz/8kFZoADw9PWFlZSXy9hU3btyAnZ0dnjx5MsSREULeFK3QGMTNzQ11dXWCn52dnYXGc3JyMGfOnKEOa1has2YN2tvbRY7r6Ojg888/H8KICBO9bBYGnh8VeJNmYSI+WqGNIq2trRg7dqxgxUYIkQ5JNguPZhs3bsTjx49x/vx5sZ5PK7RRRFVVVdYhEMJ4rzYLc7ncfs3Cvr6+g2oWHs36+voGdaFiWqExzLVr15CYmIiysjKhi6JaWVmBy+VSD9orKFdEGiTdLEzERys0BsnKysKaNWtga2sLPz+/fvdhcnZ2RkpKClxcXGQcqexRroi08Pl8zJs3T+S4tbW12I3Co8GjR49w5MiRfl8sra2t8dFHH0FbW1vsuWiFxiDz5s3DsmXL8Je//GXA8f379+PUqVMoLS0d4siGH8oVkRZ7e3tYW1uLbBYODw9HSUkJvvvuu6ENbBgqKSnB8uXLweFw4Ojo2O+LZV1dHU6fPg1ra2ux5qOCxiAcDgdFRUUiLyNTVVUFW1tboZ2QoxXlikiLpJuFmcze3h6WlpaIiYkZcDw0NBRXr14V9Kf9ljGSDI7I1tSpU5GVlSVyPDs7m+7N9ALlikjLy2ZhFxcX3Lx5E2lpaUhLS8PNmzfh4uKC4uJiKmYvVFRUwM/PT+S4r68vbt++LfZ8dA6NQcLDw+Hr64vCwsIBvxlevnyZrlDwAuWKSJOenh527twp6zCGPQ6Hg9LSUpFHSkpLS8HhcMSejwoag7i5uUFLSwtJSUk4ePCg0AlWS0tLZGdnw9LSUsZRDg+UK0JkLyAgAEFBQbh27Rrs7e37fbH86quvEBUVJfZ8dA6NEEKG0GCbhZkuIyMDCQkJKC8vR09PDwBAXl4es2fPhr+/P9zd3cWei1ZohBAyhAbbLMx0Hh4e8PDwQFdXl9BlwhQUFAY9F63QRpFdu3ahrq4O8fHxsg5l2KNcETLy0AptFOHxeLh//76swxgRKFfk95BkszARH63QCCFEgiTdLEzERwWNEEIkSNLNwkR8VNAYpq2tDWfOnBnwUIenpydUVFRkHOHwQbki0qCpqYnCwkKRvVWVlZWws7Oj6zlKAV0phEEqKiowd+5cbN26FU1NTdDU1ISmpiaampqwdetWWFhYoKKiQtZhDguUKyItL5uFRRlsszARH63QGMTV1RVsNhuJiYlQUlISGuvo6ACXy0V9ff1rL/k0WlCuiLQkJycjPDwc3t7er20WXrt2rYwjZR4qaAyipaWFS5cuwcjIaMDxW7duwcnJCXw+f4gjG34oV0SaJNksTMRH2/YZhMVi4e7duyJ/SVdXV4PFYg1xVMMT5YpIkySbhYn4qKAxiI+PD7hcLqqqquDg4NBvu3BcXBy4XK6MoxweKFdkKCgoKEBTU1PWYYwadMiRYeLi4pCUlIS6ujrIycmhr+/5/14Oh4NNmzbh448/lnGEwwflihBmoYLGUDU1Naivr0dXVxcmT54s8tAaoVwRwhRU0BhOR0cHhYWFdLNKMVCuCBnZqA+N4V4eRiO/jXJFyMhGBY0QQggjUEFjuAMHDgh28JHXo1wRMrLROTRCCCGMQCs0QgghjEAFjRBCCCNQQSOMkJWVhSVLlsDQ0BCampowNjbGqlWrcPHiRVmHJtKmTZtgYmIisflYLBZYLBYiIyP7jfX19WHWrFlgsVhYv369xN6TkOGEChoZ8ZKSkuDt7Q0DAwN89tlnSEtLQ3BwMADg8uXLMo5uaKmqquLUqVP9WhCuXLmCBw8e0D3eCKPRtRzJiBcfHw8XFxfEx8cLHluwYAFWr16N3t5eGUY29JYuXYpTp06hqKgIf/rTnwSPnzx5EjY2Nrh//74MoyNEumiFRka8J0+eiLxh4pgxv/4Vb2xsRGBgIMzNzaGlpYW3334b69atw+PHj4VeExUVBRaLhcrKSnh4eOAPf/gDjI2NcezYMQDPi4OFhQW0tbXh6uqKn376Sej1JiYmWL9+PVJTU2FmZgYOhwM7OzuxVovt7e2IiIiAqakp2Gw2TE1NERsbK3Zh1tHRga2tLdLS0gSPdXR04Ny5c/Dy8hrwNY2Njdi8eTNmzJgBDQ0NWFhY4Msvv+z3nMHkrrq6GitWrIC2tjaMjY0RHR0t9BmePn2K4OBgGBsbQ0NDA4aGhnBzc0NlZaVYn5OQgdAKjYx4c+bMwYkTJ6Cvr4+lS5fC0NBwwOc9efIE48aNw/bt26Guro7a2lrEx8fD2dkZPB6v340+16xZAx8fHwQEBCA5ORl//vOfce/ePRQVFSEiIgLd3d0ICwvDunXrkJ+fL/TaoqIilJeXY9u2bVBUVMSnn36K5cuXo6ioCNOmTRswvu7ubnh6eqKiogLBwcF4++23wePxsHfvXjx58gS7d+8WKx9eXl4IDw9HbGwslJSUkJ2dje7ubri5uSE6Olrouf/5z3/wzjvvoKOjA2FhYdDT00N+fj6CgoLQ2dmJDRs2vFHuvL29sWrVKnC5XOTk5CAqKgra2trw9vYGAGzZsgU5OTnYtm0bDAwM0NzcjLKyMrS0tIj1GQkZCBU0MuIdOHAAPj4+2L59O7Zv34633noLDg4O+OCDD+Do6Ch43rRp04R+off09MDKygrGxsa4cOEC3n33XaF5AwICsHLlSgCAmZkZvv32W6SkpOBf//oXJk6cCACora1FWFgYHjx4AF1dXcFrGxoakJeXhylTpgB4fgjUxMQEe/fuxRdffDHg5zhz5gxKSkqQnZ0NGxsbwesAIDo6GoGBgWI1fru5uSE4OBjZ2dnw9PTEyZMn4eLiAlVV1X7PTUpKwsOHD3HlyhUYGBgAAOzt7dHS0oLo6Gj4+vpi7Nixg86dv7+/oHjZ29ujsLAQ6enpgsd4PB6WL18OHx8fwWv+fw5CBosOOZIRz9DQEIWFhcjOzsZf//pXmJiYICsrCx4eHti7d6/Qcw8fPgwbGxtoa2tj8uTJMDY2BgDcvXu337yLFi0S/DeLxQKbzYaFhYWgmAHAH//4RwDAo0ePhF47d+5cQTEDnm/WWLx4MXg8nsjPkZ+fDx0dHVhZWaG7u1vwx9HREV1dXa997asmTJgAV1dXpKWloa6uDgUFBSIPN+bn58Pc3Bx6enpC7+nk5ITm5mZUVFQInjuY3Dk7Owv9PGPGDPz888+Cn83MzPDVV19h3759uH79uuCuzoT8HrRCI4wgLy8PGxsbwcqGz+fD09MT0dHR8PPzA4vFwsGDBxEaGgp/f384OTmBxWKht7cXCxcuREdHR785//+O1QoKCgM+BgCdnZ1Cj2toaPSbT0NDA3w+X+RnaGhowMOHD6Gurj7geHNzs8jX/j8vLy+sWLECCQkJYLPZsLe3F/me9+7d+833HGzuJk2aJPSzoqKi0PNiYmLA4XBw7Ngx7Nq1C5MmTYKXlxe2bdsGZWVlsT8nIa+igkYYSUtLCz4+PggLC0N1dTXMzc2RkZGBBQsWCJ2Lqqmpkcr719fXD/iYlpaWyNe89dZb0NPT67ch46VXD2n+Fnt7e7DZbHz22Wfw9/eHvLy8yPdks9n45JNPBhx/eT5S0rmbMGECIiIiEBERgQcPHuDcuXPYuXMnFBUVsXPnzjeel4xuVNDIiFdbWzvgbe6rqqoA/Lpaam9v73ce6fjx41KJ6fvvv8fPP/8sOOzY2tqKvLw8LF68WORrnJyccP78eaioqAgOZb6pMWPGIDg4GBcvXhSctxL1nl988QWmTJny2vNz0sydrq4uAgICcPr0ady+fVsic5LRiQoaGfHmzZsHe3t7LFq0CHp6emhtbcWFCxdw5MgRuLu7Q0dHBwCwcOFCxMXFYd++fTA3N8fly5dx7tw5qcSkoaEBDw8PhIWFCXY5tre3IyQkRORrVqxYgePHj8PNzQ3+/v4wMTHBs2fP8NNPPyEnJwfHjx8f1OG4tWvXYu3ata99DpfLxdmzZ7FkyRJwuVwYGhqivb0dVVVVuHLlCk6cOAFA8rlbtGgRlixZgpkzZ0JFRQXFxcX48ccfBZtwCHkTVNDIiLdt2zbk5eVhz549aGhogLy8PAwMDLBjxw5s2rRJ8LyQkBC0tLQgISEBnZ2dsLGxQXp6OmbPni3xmGxsbGBra4vIyEg8fvwY06dPx+nTp0W2FADPz8dlZGTgwIEDSE1Nxf3796GsrIypU6di8eLFUFRUlHicampqyMvLQ3R0NOLi4sDn86GmpoZp06YJ7TqUdO7mz5+Ps2fPIi4uDt3d3dDX18eePXuwceNGSX00MgrR7WMIkTATExPMmzdP5PZ8Qoh00LZ9QgghjEAFjRBCCCPQIUdCCCGMQCs0QgghjEAFjRBCCCNQQSOEEMIIVNAIIYQwAhU0QgghjEAFjRBCCCP8D62vFj6Mlj4VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 625, 10000)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the Central Limit Theorem in action – the histograms of the sample means are roughly normal, even though the histogram of the salaries themselves is far from normal.\n",
    "\n",
    "We can also see that each of the three histograms of the sample means is centered very close to the population mean. In each case, the \"average of sample means\" is very close to the population mean. Both values are provided in the printout above each histogram. As expected, the sample mean is an unbiased estimate of the population mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 2.3 **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign the variable `bootstrap_sampled_SD` to the integer corresponding to your answer to the following question:\n",
    "\n",
    "When I increase the number of bootstrap samples that I take, for a fixed sample size, the SD of my sample mean will...\n",
    "\n",
    "1. Increase\n",
    "2. Decrease\n",
    "3. Stay about the same\n",
    "4. Vary widly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "bootstrap_sampled_SD = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb638d2ba8>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check('tests/q2_3.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we'll look at what happens when we take a fixed sample, then bootstrap from it with different numbers of resamples. How does the distribution of the resampled means change?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 100, 500)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 100, 1000)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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osKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEosKAREZEoaHVBW7ZsGaRSKWbNmqXpUIiISMtpbUE7f/48/u///g8tWrTQdChERFQBaGVBe/78Odzc3PDbb79BKpVqOhwiIqoAtLKgTZ8+HU5OTujevbumQyEiogqikqYD+F+bN2/GnTt3sG7dOkHvj4+PV3NE4sFcCcdcCcdcCcdcvZ21tfU7f1arClp8fDzmzZuHgwcPonLlyoI+8z7f/MckPj6euRKIuRKOuRKOuVI/rSpo586dQ3p6Ouzt7eXHCgsLcebMGWzcuBGPHj2Cnp6eBiMkIiJtpVUFzdHREe3atVM45uHhASsrK8yYMQNVqlTRUGRERKTttKqgSaXSErsaq1WrBiMjIzRv3lxDURERUUWglbsciYiIykurztCUCQ8P13QIRERUAfAMjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIRIEFjYiIREFwQVu0aBGuX79e6viNGzewaNEilQRFRERUXoILmp+fH/7+++9Sx1nQiIhIk1R2yTErKwuVK1dW1XRERETlUubjY65du4arV6/KX0dHR6OgoKDE+zIzM7Fx40ZYW1urPkIiIiIByixo+/btk19GlEgk2LRpEzZt2qT0vVKpFOvWrVN9hERERAKUWdBcXV3h4OAAmUyG3r174/vvv8fnn39e4n0GBgZo2LAhKlXS+ueFEhGRSJVZgUxNTWFqagoA2Lt3L5o0aQJjY+MPEhgREVF5CD6l6tq1qzrjICIiei+lFjQPDw9IJBL8+uuv0NXVhYeHx1snk0gk+O2331QaIBERkRClFrSTJ09CR0cHRUVF0NXVxcmTJyGRSMqc7G3jRERE6lJqQXtzu76y10RERNqE93IkIiJReKd99llZWcjMzIRMJisx1qBBg/cOioiIqLwEF7Tc3FwsWrQIv//+OzIyMkp9X1ljRERE6iK4oM2cORPbt2+Ho6MjOnXqBKlUqs64iIiIykVwQdu7dy/GjBmDgIAAdcZDRET0TgRvCpFIJGjTpo06YyEiInpnggvagAEDcPz4cTWGQkRE9O4EF7SZM2fi7t27mDp1Ki5cuIAnT54gLS2txH9ERESaIHgNrUOHDgCKG6y3bt1a6vu4y5GIiDRBcEHz8vJS+62tli1bhr179yIhIQFVqlSBra0t5s6di+bNm6v16xIRUcUnuKD5+PioMw4AwKlTpzBu3DjY2NhAJpNhwYIFGDJkCM6ePQsjIyO1f30iIqq4tOqJnCEhIQqv165dC3Nzc8TExKB///4aioqIiCoCwQVt0aJFb32PRCKBl5fXewX0pqysLBQVFbGJm4iI3kqSmZlZ8oaMSpR1yU8ikUAmk0Eikah0U4irqysSExNx/Phx6OrqKn1PfHy8yr4eERFplrW19Tt/VvAZ2rNnz0ocKyoqwv379xEUFIQzZ85g165d7xzI//r+++8RExODgwcPllrMgPf75j8m8fHxzJVAzJVwzJVwzJX6vdfjY3R0dGBpaYn58+fDyspKZZcbfXx8sHv3boSFhcHS0lIlcxIRkbip7HlonTt3xuHDh997Hm9vb3kxa9y4sQoiIyKij4HKdjnGxsZCR+f96uN3332HP//8E1u3boVUKkVKSgoAwMDAANWrV1dFmEREJFKCC9r27duVHn/+/DnOnDkjvxv/+wgKCgIAODk5KRz39vb+IH1wRERUcQkuaO7u7qWO1apVC56enu+9hpaZmflenycioo+X4IJ2+fLlEsckEgmkUikMDQ1VGhQREVF5CS5o5ubm6oyDiIjovahslyMREZEmsaAREZEosKAREZEosKAREZEosKAREZEoCCpoOTk5qFmzJpYsWaLueIiIiN6JoIJWrVo11K5dGzVq1FB3PERERO9E8CXHIUOGIDQ0FEVFReqMh4iI6J0IbqweOHAgoqKi4ODggDFjxsDS0hJVq1Yt8b727durNEAiIiIhBBe0N28YfP78eUgkEoVxdTyxmoiISCjBBW3VqlXqjIOIiOi9CC5oo0aNUmccRERE7+Wd+tASExMRExOD58+fqzoeIiKid1KugrZz5060bNkSHTp0wIABAxAXFwcASE9PR/v27REaGqqWIImIiN5GcEHbs2cP/vOf/6Bx48aYN28eZDKZfKxWrVpo3LgxduzYoZYgiYiI3kZwQVu6dCl69uyJkJAQpetptra2uHbtmkqDIyIiEkpwQbt9+zYGDhxY6rixsTGePn2qkqCIiIjKS3BBq1atGrKzs0sdv3v3LmrVqqWSoIiIiMpLcEHr3r07tm3bhry8vBJjjx8/xubNm9G7d2+VBkdERCSU4D60OXPmoE+fPujZsyeGDBkCiUSCI0eOIDIyEps3b4auri68vb3VGSsREVGpBJ+hWVlZ4dChQzAxMYGfnx9kMhlWrVqFX3/9Fa1atcLBgwfRoEEDdcZKRERUKsFnaADQpEkThIaGIjMzE3fu3EFRUREsLS1Ru3ZtdcVHREQkSLkK2mtSqRQ2NjaqjoWIiOidlaugZWZmYtWqVTh06BDu378PADA3N0e/fv3g4eEBqVSqliCJiIjeRvAa2p07d9C1a1csWbIEBQUF6NatG7p164aCggIsWbIEXbp0QWJiojpjJSIiKpXgM7RZs2bhxYsX2LNnD7p3764wduLECXz99dfw9vbGrl27VB4kERHR2wg+Q4uOjsbEiRNLFDMA6NGjByZMmIAzZ86oNDgiIiKhBBe0Tz75pMw1MqlUik8++UQlQREREZWX4IL29ddfY+vWrXj58mWJsefPn2Pr1q0YM2aMSoMjIiISSvAamrW1NSQSCWxtbTFy5Eh8+umnAIof9rljxw4YGxvD2tq6xDPRnJ2dyx1UUFAQVqxYgZSUFDRt2hQLFy5E586dyz0PERF9PCSZmZmyt78NMDIyevtkEonCc9IkEgkyMjLKFVBISAj+85//YOnSpbC3t0dQUBC2bduGmJgY3onkPcTHx8Pa2lrTYVQIzJVwzJVwzJX6CT5D27t3rzrjkFu1ahVGjRqFsWPHAgAWL16MY8eOYePGjZg7d+4HiYGIiCoewQWta9eu6owDAJCXl4e4uDhMmTJF4Xjv3r1x9uxZtX99IiKquARvCvkQ0tPTUVhYCGNjY4XjxsbGSE1N1VBURERUEbzTvRy1SXx8vKZDqDCYK+GYK+GYK+GYq7d7n3VGrSpotWrVgq6uLtLS0hSOp6WloU6dOko/w0VWYbggLRxzJRxzJRxzpX5adcmxSpUqaNu2LSIjIxWOR0ZGomPHjhqKioiIKgKtOkMDAA8PD0yYMAHt27dHx44dsXHjRjx58gTffPONpkMjIiItJrigLVq0CIMGDULz5s2Vjt+4cQNhYWHw9vZ+r4C++OILZGRkYPHixUhJSUGzZs0QHBwMc3Pz95qXiIjETfAlRz8/P/z999+ljt+4cQOLFi1SSVDjx4/H1atXkZqaihMnTqBLly4qmZeIiMRLZWtoWVlZqFy5sqqmIyIiKpcyLzleu3YNV69elb+Ojo5GQUFBifdlZmZi48aN3MFDREQaU2ZB27dvn/wyokQiwaZNm7Bp0yal75VKpVi3bp3qIyQiIhKgzILm6uoKBwcHyGQy9O7dG99//z0+//zzEu8zMDBAw4YNUamS1m2aJCKij0SZFcjU1BSmpqYAim9O3KRJkxK3pSIiItIGWnVzYiIiondVrmuEx44dw++//46kpCRkZmYqPPsMKF5ni4uLU2mAREREQgguaCtWrICvry/q1KkDGxubUhusiYiINEFwQQsMDET37t2xc+dO9psREZHWEdxYnZmZCScnJxYzIiLSSoILWvv27fksHyIi0lqCC9qSJUuwb98+BAcHqzMeIiKidyJ4DW3MmDHIy8vDxIkT4enpCTMzM+jq6iq8RyKRICYmRuVBEhERvY3ggla7dm0YGxujUaNG6oyHiIjonQguaOHh4eqMg4iI6L2o7PExREREmlSugpaRkYH58+ejX79+sLGxwblz5+THFy1ahFu3bqklSCIiorcRfMnx3r176N+/PzIyMtC8eXMkJSXhn3/+AQDUrFkTISEhePr0KRYvXqy2YImIiEojuKDNnTsXMpkMMTExMDQ0LLE5ZMCAAVxnIyIijRF8yfH48eNwc3ODpaUlJBJJiXELCws8evRIpcEREREJJbigvXr1ClKptNTx58+fQ0eHe0yIiEgzBFegZs2a4fTp06WOh4eHo3Xr1ioJioiIqLwEF7RJkyYhNDQUS5YswbNnzwAARUVFuH37NsaPH48LFy7Aw8NDbYESERGVRfCmkGHDhuHhw4dYsGABFixYAAAYOnQoAEBHRwc//fQT+vfvr54oiYiI3qJcT6z29PTEsGHDEBYWhjt37qCoqAgNGzbEoEGDYGlpqaYQiYiI3q5cBQ0A6tevD3d3d3XEQkRE9M4Er6HFxMRg2bJlpY4vX75cfucQIiKiD03wGdqiRYvK3LZ/7do1nDp1Crt371ZJYEREROUh+AztypUrsLOzK3W8Q4cOuHz5skqCIiIiKi/BBS0nJ0fpHULelJWV9d4BERERvQvBBa1Ro0aIiIgodfzo0aP49NNPVRIUERFReQkuaGPGjMGRI0fg5eUlb6wGih8dM2vWLERERODrr79WS5BERERvI3hTiJubG65evYr169cjKCgIderUAQCkpqZCJpNh1KhRmDRp0jsHkp+fj/nz5+PIkSNISkqCoaEhunXrhrlz56JBgwbvPC8REX0cytWHtmLFCnljdVJSEgDA0tISTk5O6Nq163sFkpOTg8uXL+O7775Dq1at8OLFC/z4449wcXHB6dOnUalSuVvmiIjoIyKoSuTl5eH8+fMwNTVFt27d0K1bN5UH8sknn+Cvv/5SOLZ8+XLY29vj1q1baNGihcq/JhERiYegNbRKlSphyJAhZW4KUYeXL18CQJn9b0RERIDAMzQdHR2Ym5t/0G35eXl5+PHHH+Hg4IB69eqV+r74+PgPFlNFx1wJx1wJx1wJx1y9nbW19Tt/VpKZmSkT8sb169fjt99+w9GjR2FsbPzOX/C14OBgeHp6yl/v3LkTnTt3BgAUFBRg/PjxuHnzJvbv34+aNWu+99f72MXHx7/XP5SPCXMlHHMlHHOlfoJ3WuTk5KBatWqwsbGBo6MjLC0tUbVqVYX3SCQSTJ06VdB8/fv3h62trfy1mZkZgOJiNm7cOFy/fh379u1jMSMiIkEEn6EZGRm9fTKJBBkZGe8cTH5+Pr799lvcuHED+/btg6mp6TvPRYr416FwzJVwzJVwzJX6CT5DU/d9GgsKCjB27FjExsZi+/btkEgkSElJAQDUqFGjxNkgERHRmwQXNHNzc3XGgeTkZOzfvx8A0LNnT4WxVatW4auvvlLr1yciooqt3N3KiYmJOHXqFNLS0jBs2DBYWFggLy8PKSkpMDExQZUqVd4pEAsLC2RmZr7TZ4mIiAQXtKKiInh6euL333+HTCaDRCJBhw4d5AWtS5cumDVrFqZMmaLOeImIiJQSfHPipUuXYuvWrfjhhx9w5MgRyGT/3UtSvXp1DBo0CPv27VNLkERERG8juKD98ccfGD16NGbOnKn0MTHNmzdHYmKiSoMjIiISSnBBe/ToEdq3b1/qeNWqVfmATyIi0hjBBa1OnTq4f/9+qeNxcXF8zAsREWmM4II2ePBgbNy4UeGyokQiAQAcOXIEO3bswJAhQ1QfIRERkQCCC9rs2bNRv359dO/eHW5ubpBIJFi2bBk+++wzDB8+HC1btsSMGTPUGSsREVGpBBe0GjVq4PDhw5gxYwZSU1Ohr6+PmJgYZGdnY/bs2di/fz/v5kFERBpTrsZqfX19zJw5EzNnzlRXPERERO/krQUtNzcX+/fvx71791CzZk3069ePNw0mIiKtU2ZBe/z4MQYMGIAvcrkbAAAgAElEQVR79+7JG6mrVauGHTt2oFu3bh8kQCIiIiHKXEObP38+7t+/D3d3d/z5559YuHAh9PX14e3t/aHiIyIiEqTMM7Tjx49j5MiRmD9/vvxYnTp1MH78eCQnJ6NevXpqD5CIiEiIMs/QUlJS0LFjR4Vj9vb2kMlkePjwoVoDIyIiKo8yC1phYSH09fUVjr1+nZubq76oiIiIyumtuxyTkpJw8eJF+esXL14AKH6cePXq1Uu8v6z7PRIREamLJDMzU1baoJGRkfz2Vm96/Tw0ZccyMjJUHyW9t/j4eFhbW2s6jAqBuRKOuRKOuVK/Ms/QVq1a9aHiICIiei9lFrRRo0Z9qDiIiIjei+B7ORIREWkzFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFFjQiIhIFrS1o06dPh1QqxcqVKzUdChERVQBaWdD27NmDixcvwszMTNOhEBFRBaF1Be3+/fuYPXs2goKCUKnSWx+oTUREBEDLClpBQQHGjx+P7777Dk2aNNF0OEREVIFoVUFbuHAhatasiXHjxmk6FCIiqmA0dk0vODgYnp6e8tfTpk3Dtm3bEBUVVa554uPjVR2aaDFXwjFXwjFXwjFXb2dtbf3On5VkZmbKVBiLYC9fvkRaWpr89ebNm7FixQro6Pz3pLGwsBA6OjowNTXF9evXNRGmaMTHx7/XP5SPCXMlHHMlHHOlfho7QzM0NIShoaH89eTJkzF8+HCF9wwdOhRDhw7F2LFjP3R4RERUwWjNNkJjY2MYGxsrHKtUqRJMTEz4Vw0REb2VVm0KISIieldac4amzNWrVzUdAhERVRA8QyMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlFgQSMiIlGQZGZmyjQdBBER0fviGRoREYkCCxoREYkCCxoREYkCCxoREYkCCxoREYkCCxoREYkCCxoRlUthYSFSU1ORmpqKwsJCTYdTYSQmJqKgoEDTYYia1he0+Ph4PHr0SP76yJEj+PLLL9G5c2cMHz4cR48e1WB0FUPLli2RmJio6TC0yqxZsxAdHa3pMCqUvXv3ol+/fjAzM0PTpk3RtGlTmJmZoV+/fti3b5+mw9N69vb2/Dksp6tXr6JmzZqC319JjbGohJubG3788UfUrVsXoaGhcHNzg7OzMzp06ICEhASMGjUKa9euhbOzs6ZD1bjffvtN6fHHjx9j8+bNqFOnDgBg8uTJHzIsrRQUFIQNGzagUaNGGDt2LEaOHFmuH5yPzaZNm+Dl5YWRI0fC3d0dxsbGAIC0tDRERERg3Lhx8Pf3x9ixYzUcqeaNGDFC6fHCwkLMnDkT1atXBwDs2LHjQ4ZVYclkwu/9ofV3Cqlbty6io6NhYWGB3r17Y+jQofDw8JCPb968GevXr8epU6c0GKV2MDIyQt26daGrq6tw/MGDBzAzM0OlSpUgkUhw+fJlDUWoPYyMjLBjxw6Eh4cjNDQU+fn5cHR0xJgxY9CjRw9Nh6d12rVrB09PT4wZM0bp+JYtW7Bs2TLExcV94Mi0j5GRETp37gwLCwuF4zt27ED//v3xySefAABWr16tifC0yqBBg8ocz87ORlxcHDIyMgTNp/UFzcLCAvv27UOrVq3QqFEj/PXXX2jZsqV8/O7du+jSpYvCZcmP1fTp03Hp0iVs2LAB1tbW8uO1a9fGqVOn0LRpUw1Gp12MjIxw+/ZtGBsbIzs7Gzt37sSWLVsQGxsLS0tLjBkzBqNGjYKJiYmmQ9UKpqamiIqKUvh39abbt2+je/fuePLkyQeOTPvs2rULc+fOxZw5cxTO1vhzWJKxsbH8MrYyGRkZCA0NFVzQtP6SY/fu3bFz5060atUKbdu2RVRUlEJBO3nyJOrWravBCLVHQEAA9uzZgyFDhmDWrFlwdXXVdEgVgoGBAVxdXeHq6oorV65g8+bNWL58ORYsWIC0tDRNh6cVmjZtig0bNsDPz0/p+KZNm/iL+l8uLi6wtbXF+PHjcfToUSxfvhyGhoaaDksrNW7cGH379i31zP/KlSsIDQ0VPJ/WF7S5c+fCwcEBT548QefOnfHLL78gNjYWTZo0QXx8PEJDQxEQEKDpMLWGk5MT2rVrBzc3Nxw5cgQrV67UdEhaSSKRKD3eunVrLF26FPPnz0dISMgHjkp7zZ8/X74Jq1evXvL12NTUVBw/fhyPHz9GcHCwhqPUHpaWljh48CDmzZuHrl27Ys2aNaX+m/uYtWnTpswlED09PdSvX1/wfFp/yREA7t27h/nz5+PgwYPIysoCAFSqVAk2NjaYOnUqHB0dNRyh9iksLMSCBQuwfft2pKSk4PTp0/wL+g1vXnIkYe7du4eNGzfi/PnzSE1NBQDUqVMHdnZ2+Oabb0qsGVGxiIgIeHh4ICUlBWfOnOHP4RtevXqFwsJCVKtWTSXzVYiC9ppMJkNaWhqKiopQq1YtVK5cWdMhab2oqCicPn0abm5uqFWrlqbDIfoopaen48aNG7CxsVHZL28qqUIVNCKiiuT+/fsKZ7Pm5uYajkg7JSYm4uzZswq56tixI6ysrMo1j9avoQHApUuXsGbNGqXfsLu7O9q1a6fhCLXHkydPsGHDBsTExCAlJQU6OjqwsLCAo6MjvvrqqxJb+j9mzJXqTJw4EY8ePUJYWJimQ9EKq1atwurVq/H48WN5H5VEIoGZmRk8PDzg7u6u4Qi1w/PnzzFx4kQcPHgQBgYGqF27NgDg6dOnyMnJgYODAwIDA1GjRg1B82n9nUL27duHvn37Ii0tDW5ubggICEBAQADc3NyQnp6Ofv36ITw8XNNhaoXY2FjY2dnhyJEjKCgoQGJiItq0aQMDAwPMmTMHAwYMwMuXLzUdplZgrlRLJpOhqKhI02FoBX9/f/j5+eGbb77BsWPHcPXqVVy9ehXHjh3Dt99+Cz8/PyxevFjTYWoFLy8vJCUl4cCBA3j48CHi4uIQFxeHhw8f4sCBA0hKSoKXl5fg+bT+kmOnTp3g4uKCmTNnKh1ftmwZgoODERMT84Ej0z4ODg7o2bMnZs+eDQD4888/sX79ehw9ehSZmZkYNGgQOnfujEWLFmk4Us1jrkhdWrRogYULF2Lw4MFKx8PCwuDt7Y0bN2584Mi0j7m5OUJCQmBra6t0/Ny5c3BxccH9+/cFzaf1Z2h37twp9R8GUNxpfvfu3Q8Ykfa6fPmyQiPnsGHDcPnyZaSmpkIqlWLevHm8JPQv5qr8kpOT8fPPP2PgwIGws7ODnZ0dBg4ciPnz5yM5OVnT4WmNjIwMNGnSpNRxa2trZGZmfsCItFtZ7QzlbXXQ+oLWsGHDMm98Gh4eDktLyw8XkBarXbu2wh1TUlJSUFBQIG/q/PTTT/Hs2TNNhadVmKvyiY6ORseOHfHXX3+hWbNmcHFxgYuLC5o1a4a//voL9vb2vEryLxsbG/j7+yMvL6/EWF5eHpYuXQobGxsNRKZ9HBwcMGXKFJw/f77E2Pnz5zF9+nT0799f8HxavynEx8cH48aNQ1RUlNKGzpMnT2LDhg0ajlI7ODo6YsaMGfD19YWenh4WL16MLl26oGrVqgCKn1xQ2i1mPjbMVfn4+Phg1KhR8Pf3Vzru7e0NHx8fREZGfuDItM/ixYvh7OyMRo0aoVOnTgq/s6Kjo1GtWrVy3f1CzPz9/TF+/Hj07dsXhoaG8tai9PR0ZGVloU+fPqX+m1NG69fQgOLrqIGBgTh37lyJhs6JEyfCzs5OwxFqh6ysLEyZMgV79+5FYWEh7OzssHbtWvkZbEREBF68eIEhQ4ZoNlAtwFyVD+/lWD4vX75EcHCw0iZ0FxcXwbv2Pha3bt3CuXPn5Leae52rxo0bl2ueClHQqHxyc3NRUFAgf0wFlY65EqZNmzb47rvv8PXXXysd37JlC5YuXconOZBGaf0lRyo/fX19TYdQYTBXwkyZMgUzZszApUuX0LNnzxKX/rdt24aFCxdqOErtoqpmYbHLzs7Grl27SuTK3t4eQ4cOhYGBgeC5KvwZ2s8//4yUlJRSH275sbl69Sp+++03hWZhS0tLDBgwAFOnTuWljjcwV+UTEhKC1atXIy4uDoWFhQAAXV1dtG3bFh4eHnzI7r9U3SwsZjdv3oSzszOysrLQuXNnhQfHRkdHo3r16ggJCRF8/8sKX9AGDx6Me/fu8VIHgGPHjmH06NH4/PPPoa+vj3379uGrr76CgYEBwsLCIJPJcPDgQT7jC8zV+8jPz0d6ejoA8J6qSkyYMAFXrlzB8uXLYW9vrzB29uxZeHp6onXr1ggMDNRQhNpj4MCBMDY2xpo1a0pcLcnNzYW7uztSU1PL3On+pgpf0Oi/unXrhm+++QbffvstACAyMhLe3t44d+4c8vPz4eLignr16vFJuWCuSH1U3SwsZmZmZoiMjCz1DOz69evo06cPHj9+LGg+re9DI+Hi4+PRp08f+euePXvi7t27ePLkCSpXrgxvb28cPnxYgxFqD+aK1EmVzcJiJpVKkZCQUOp4YmIipFKp4PkqxKYQVS4aipmZmRni4+Plz6VKTExEUVERatasCQCoW7cusrOzNRmi1mCuSF1eNwv/+uuv6NChg8LYuzQLi9mYMWPg7u6O+Ph49OrVS2ENLTIyEgEBAeW6kbPWF7T/XTQ0NTUFUPwN//jjj/Dz8yvXoqGYjRgxAlOmTIGnpyf09PSwZs0a9O/fH1WqVAFQvAmCD2EsxlyRuqi6WVjMfHx8ULVqVQQGBmLevHnys1eZTAYTExPMmDED06ZNEzyf1q+hqXrRUMwKCgrw888/Izg4GK9evUKfPn3g5+cn/4G6ePEicnNz0aVLFw1HqnnMFambqpqFPxZJSUkKV+De5ZaGWl/QVL1oSERE4qT1lxxfLxqWVtDKu2j4sSgsLER6ejp0dHTkfTCkHHNFqsZ1f+FU+QBnrS9oql40FLtDhw4hICAAly5dQn5+PgCgevXqcHBwwJw5c9CgQQMNR6g9mCtSB677C7dv3z64urqia9eucHNzK/H7vV+/fti0aRMcHR0Fzaf1lxwBICAgAIGBgUhJSYFEIpE/0tzExASTJk0q16KhmO3YsQOzZs3C2LFjoa+vj99//x2jRo1CgwYNsHv3bty8eROHDx/mrXfAXJH6cN1fOFU/wLlCFLTXXi8a5ufno1atWvwL53/Y2dlh9uzZ+OKLLwAAsbGxGD16NK5duwaJRIJvv/0WeXl52Lp1q4Yj1TzmitSF6/7CmZiY4NSpU6U+xSE+Ph5du3ZFSkqKoPkqVGO1paUl7OzsMGLECN5UVokHDx6gffv28tft2rVDSkqK/JEeHh4eiIqK0lR4WoW5InVRdbOwmKn6Ac5av4amzOtLjqTI3Nwcly5dkvdPxcbGQkdHR35ndCMjIxQUFGgyRK3BXJG6cN1fOFU/wLlCFjRSbvz48Zg6dSouXLgAfX19bN26FcOHD4euri6A4rsUcE2oGHNF6qLqZmExc3JygpmZGQIDA7F27doSD0MNDw8v1wOcK9Qa2ms7d+7EgAEDuPVViQ0bNig0C8+aNUt+eTYxMRGFhYVs7PwXc0XqpopmYRKuQhY0IiISl/v378uLv4mJyTu1zfCSo0g9ePBA4aGVr2+6SyUxV6RqqmwWFrtVq1Zh9erVePz4sXx/hEQigZmZGTw8PMR1c2Iqn6CgIAQEBODRo0cKx+3s7ODn54e2bdtqKDLtw1yROqi6WVjM/P39sXLlSkybNg19+vRRyFVERAT8/PyQnZ2NWbNmCZqPlxxFZOXKlVi9ejU8PT2hr6+PVatWYejQobCxscHOnTuxd+9ehIeH869DMFekPqpuFhazFi1aYOHChRg8eLDS8bCwMHh7e+PGjRuC5mNBE5HWrVtj6dKl+PzzzwEACQkJ6Nu3L27fvo1KlSrB29sbt2/fRmhoqIYj1TzmitRF1c3CYmZmZobjx4+jSZMmSsdv3LiB3r1784nVH6OnT58q7MqzsrLCixcv8PTpUwDA6NGjcf78eU2Fp1WYK1IXVTcLi5mNjQ38/f2Rl5dXYiwvLw9Lly6FjY2N4Pm4hiYiVlZWiIiIwDfffAMAOH78OKpUqQITExMAgJ6eHh///i/mitRF1c3CYrZ48WI4OzujUaNG6NSpk0KuoqOjUa1atXJdJWFBE5EZM2bAzc0NERER0NfXx/79+zFhwgT5L+ZTp06hWbNmGo5SOzBXpC6qbhYWs+bNm+PChQsIDg7G+fPnkZycDKA4V76+vnBxcUGNGjUEz8c1NJE5cuSIQrPw2LFj5WMZGRkAwG3p/2KuiMSFBY2ISE1U0Sz8MUhMTFTas1fe28/xkqMIZWdnIy4uTqFZuE2bNlwTUoK5InVQZbOwmD1//hwTJ07EwYMHYWBgIH9i/NOnT5GTkwMHBwcEBgYKvuzIgiYiRUVF8PX1xfr16/Hq1SsA/30yQf369eHv74/+/ftrMkStwVyRuqi6WVjMvLy8kJSUhAMHDsDe3l5h7OzZs/D09ISXlxcCAwMFzcdLjiLi6+uLAwcO4KeffoK+vj78/f3Rr18/9O/fHzt37sSKFSuwfft29O7dW9OhahxzReqi6mZhMTM3N0dISAhsbW2Vjp87dw4uLi64f/++oPl4hiYiO3bswMaNG9G5c2cAQOPGjWFnZ4eJEyfihx9+QOXKleHn58df0mCuSH0yMjJKbRQGAGtra2RmZn7AiLRbWZf3y3vpn43VIpKdnY26devKX5uYmCA3N1f+wzN48GBcu3ZNU+FpFeaK1EXVzcJi5uDggClTpii9icH58+cxffr0cl365xmaiDRv3hx//vknvL29AQC7d++GgYGBvFm4qKgIVapU0WSIWoO5InVRdbOwmPn7+2P8+PHo27cvDA0NUatWLQBAeno6srKy0KdPH/j7+wuejwVNRL7//nt8+eWX2L9/P/T19XHhwgX8/PPP8vFjx46hdevWGoxQezBXpC6qbhYWM6lUil27duHWrVs4d+4c0tLSAPy3Cb28D9jlphCRuXr1Kv766y95s3CvXr00HZLWYq6IxIUFjYhIDVTVLCx22dnZ2LVrV4lc2dvbY+jQoTAwMBA8FwuaCCUlJSE6OlreLGxhYYFevXrxMocSzBWpmqqbhcXs5s2bcHZ2RlZWFjp37qzQsxcdHY3q1asjJCQETZs2FTQfC5qIZGdnw93dHWFhYQCKt7waGxvj6dOnqFq1KubOnQs3NzcNR6kdmCtSlwkTJuDKlStYvnx5qc3CrVu3FtwsLGYDBw6EsbEx1qxZA319fYWx3NxcuLu7IzU1tczH8byJm0JE5IcffkBKSgpOnz4NfX19/PTTT7C0tIS3tzd2794Nb29vSKVSDBs2TNOhahxzRepy4MCBUpuFO3bsiICAALi4uGggMu1z8eJFREZGlihmAKCvr4/vvvsOffr0ETwfC5qI7N27F7t370bz5s0BAL/++iuaNm0Kb29vfP3118jNzcWKFSv4SxrMFamXKpuFxUwqlSIhIaHUS4qJiYmQSqWC52NjtYgUFBTA0NBQ/trAwAD5+fnIyckBAPTu3Rvx8fGaCk+rMFekLqpuFhazMWPGwN3dHcuXL0dcXBySk5ORnJyMuLg4LF++HJMnT4arq6vg+XiGJiI2NjZYvXo1li5dCqD4jt/GxsbyRemXL1+Wa8eQmDFXpC6qbhYWMx8fH1StWhWBgYGYN2+e/OxVJpPBxMQEM2bMwLRp0wTPx00hIhIXFwdnZ2fo6uqicuXKSE9Px5o1azB06FAAwPr163Hx4kUuRoO5IvVTVbPwxyIpKUlh276lpWW552BBE5knT57g0KFDePXqFbp37y54u+vHiLkiEhcWNCIiFVNls7DYXbp0CWvWrFHahO7u7o527doJnosFTYROnDiBmJgYhWbhAQMG8A4FSjBXpGqqbhYWs3379sHV1RVdu3Yt8TDUyMhInDp1Cps2bYKjo6Og+VjQRCQtLQ0jRoxAbGwsdHR0UFRUhNatW+Px48d4+vQpPDw8MG/ePE2HqRWYK1IXVTcLi1mnTp3g4uKCmTNnKh1ftmwZgoODERMTI2g+btsXEW9vb5iamiIpKQnJyckYN24cmjZtilu3biEkJARbt27FmjVrNB2mVmCuSF0uXrwIb2/vMpuFL168qIHItM+dO3dKfbI3AAwaNAh3794VPB8LmogcPXoUP/74I2rUqAE9PT34+vpi9+7dePHiBXr06IGFCxdi48aNmg5TKzBXpC6vm4VLU95mYTFr2LBhmWeq4eHh5drtyD40EalSpYrCXQgkEgkKCwtRWFgIoPi2O/fv39dUeFqFuSJ1ed0sHB8fj169epVYFwoICIC7u7uGo9QOPj4+GDduHKKiotCrVy+Fh6EeP34cJ0+exIYNGwTPx4ImIvb29pg/fz5Wr14NPT09zJ07F5aWljAyMgJQ/APFvwyLMVekLqpuFhYzJycnmJmZITAwEGvXrlXY5WhnZ4fw8HDY2dkJno+bQkQkKSkJzs7OuH//PiQSCQwMDLB582b07NkTAPDHH38gISEBc+fO1WygWoC5og9BFc3CJBwLmsjk5OQgJiYGeXl56NChg/y2O1QSc0WkPe7fvy8v/iYmJmjQoEG552BBIyJSMVU2C4vdqlWrsHr1ajx+/BgyWXE5kkgkMDMzg4eHR7nWG7mGJjL//PMPdu3aVaJZeODAgejRo4emw9MqzBWpw5vNwm5ubiU2hfTr169czcJi5u/vj5UrV2LatGklGqsjIiLg5+eH7OxszJo1S9B8PEMTkTt37sDJyQm5ubnQ09NDcnIy+vbti/T0dMTGxmLQoEEICgpCpUr8O4a5InVRdbOwmLVo0QILFy4stRctLCwM3t7euHHjhqD52IcmIt7e3vjss89w+/ZtXLt2DXPnzkVRURGOHj2Kc+fO4dKlS1i8eLGmw9QKzBWpi6qbhcUsIyMDTZo0KXXc2toamZmZgudjQROR06dPY/LkyfJtwu7u7jh+/DgyMjJgZWWFhQsXYvv27RqOUjswV6Quqm4WFjMbGxv4+/sjLy+vxFheXh6WLl0KGxsbwfPxeoqIfPLJJ3j58qX8dU5ODgoKClC5cmUAxaf3KSkpmgpPqzBXpC6qbhYWs8WLF8PZ2RmNGjVCp06dFHIVHR2NatWqITQ0VPB8LGgi0rNnT/j4+GDp0qXQ09PDvHnz0KpVKxgaGgIAHjx4IF90/dgxV6Quqm4WFrPmzZvjwoULCA4Oxvnz55GcnAygOFe+vr5wcXFBjRo1BM/HTSEikpaWhlGjRuHChQuQSCSoV68etm7dijZt2gAA9uzZgydPnmDChAkajlTzmCsi8WFBE6HExES8evUKjRs35i69t2CuSJ1U0Sz8MUhMTFTas1fe5xKyoBERqZgqm4XF7Pnz55g4cSIOHjwIAwMD1K5dGwDw9OlT5OTkwMHBAYGBgYIvO/JPUpF58uQJNmzYUKJZ2NHREV999RV0dXU1HaLWYK5IHVTdLCxmXl5eSEpKwoEDB2Bvb68wdvbsWXh6esLLywuBgYGC5uMZmojExsbCyckJn376KapWrYpz587BxcUF+fn5OHbsGJo2bYpdu3bJNz58zJgrUhdVNwuLmbm5OUJCQmBra6t0/PXPpdBHObEPTUR8fHzk/VQHDhzA6tWrkZiYiI0bN+Ly5cvIycnB/PnzNR2mVmCuSF1U3Swsdm8+l7A8Y8qwoInI5cuXMWLECPnrYcOG4fLly0hNTYVUKsW8efMQFhamwQi1B3NF6qLqZmExc3BwwJQpU3D+/PkSY+fPn8f06dPRv39/wfNxDU1EateujUePHsnvQpCSkoKCggL5ZbNPP/0Uz54902CE2oO5InVRdbOwmPn7+2P8+PHo27cvDA0N5Y9wSk9PR1ZWFvr06QN/f3/B87GgiYijoyNmzJgBX19f6OnpYfHixejSpQuqVq0KAIiPj4eZmZmGo9QOzBWpi6qbhcVMKpVi165duHXrFs6dO4e0tDQA/21Cb9y4cbnm46YQEcnKysKUKVOwd+9eFBYWws7ODmvXrpWfhURERODFixcYMmSIZgPVAswVkfiwoIlQbm4uCgoKUL16dU2HovWYK1IXVTULi112djZ27dpVIlf29vYYOnQoDAwMBM/FgkZEpEKqbhYWs5s3b8LZ2RlZWVno3LmzQs9edHQ0qlevjpCQEDRt2lTQfCxoInP16lX89ttvCs3ClpaWGDBgAKZOncofojcwV6QOEyZMwJUrV7B8+fJSm4Vbt24tuFlYzAYOHAhjY2OsWbMG+vr6CmO5ublwd3dHampqmY/jeRMLmogcO3YMo0ePxueffw59fX3s27cPX331FQwMDBAWFgaZTIaDBw/CxMRE06FqHHNF6qLqZmExMzMzQ2RkZKlnYNevX0efPn3w+PFjQfOxD01EfH198csvv2DLli1Yt24d/vjjD5w4cQK+vr44e/YszM3N8dNPP2k6TK3AXJE6qbJZWMykUikSEhJKHU9MTIRUKhU8HwuaiMTHx6NPnz7y1z179sTdu3fx5MkTVK5cGd7e3jh8+LAGI9QezBWpi6qbhcVszJgxcHd3x/LlyxEXF4fk5GQkJycjLi4Oy5cvx+TJk+Hq6ip4PvahiYiZmRni4+NhYWEBoPivm6KiItSsWRMAULduXWRnZ2syRK3BXJG6qLpZWMx8fHxQtWpVBAYGYt68efKzV5lMBhMTE8yYMQPTpk0TPB8LmoiMGDECU6ZMgaenJ/T09LBmzRr0798fVapUAVC8CRJneWMAABuySURBVOL1L/CPHXNF6qLqZmGxmz59OqZPn46kpCSFbfuve0LLg5tCRKSgoAA///wzgoOD8erVK/Tp0wd+fn7yvxAvXryI3NxcdOnSRcORah5zRSQ+LGhERCqmymZhsbt06RLWrFmjtAnd3d0d7dq1EzwXC5pIFRYWIj09HTo6OvLGTlKOuSJVUnWzsJjt27cPrq6u6Nq1a4mHoUZGRuLUqVPYtGkTHB0dBc3HgiYyhw4dQkBAAC5duoT8/HwAQPXq1eHg4IA5c+agQYMGGo5QezBXpA6qbhYWs06dOsHFxQUzZ85UOr5s2TIEBwcjJiZG0HwsaCKyY8cOzJo1C2PHjoW+vj5+//13jBo1Cg0aNMDu3btx8+ZNHD58mPeSA3NF6qPqZmExMzExwalTp2Btba10PD4+Hl27dkVKSoqg+VjQRMTOzg6zZ8/GF198AQCIjY3F6NGjce3aNUgkEnz77bfIy8vD1q1bNRyp5jFXpC7NmjXD4sWLMXDgQKXje/fuhZeXF27cuPGBI9M+9vb2GD58ODw9PZWOBwQEYPv27Th79qyg+bhtX0QePHiA9u3by1+3a9cOKSkpePLkCczMzODh4SH/Bf6xY65IXV43C8fHx6NXr14l1oUCAgLg7u6u4Si1g4+PD8aNG4eoqCj06tVL4WGox48fx8mTJ7FhwwbB87GgiYi5uTkuXbok75+KjY2Fjo6O/B+JkZERCgoKNBmi1mCuSF1U3SwsZk5OTjAzM0NgYCDWrl2rsMvRzs4O4eHhsLOzEzwfC5qIjB8/HlOnTsWFCxegr6+PrVu3Yvjw4dDV1QVQfNsdrgkVY65InVTZLCx2dnZ25SpaZeEamshs2LBBoVl41qxZ8p1WiYmJKCws5J0K/sVcEWmP+/fvy4u/iYnJO+0yZkEjIlIxVTYLi92qVauwevVqPH78GDJZcTmSSCTytezyrDfykqNIPXjwQOGhla9vukslMVekSm82C7u5uZXYFNKvX79yNQuLmb+/P1auXIlp06aVaKyOiIiAn58fsrOzMWvWLEHz8QxNZIKCghAQEIBHjx4pHLezs4Ofnx/atm2roci0D3NF6qDqZmExa9GiBRYuXIjBgwcrHQ8LC4O3t7fgFgc+D01EVq5ciaVLl2Lq1KkICAiAtbU1Zs+ejeDgYFhYWGDAgAGIjY3VdJhagbkidblz506pv6ABYNCgQbh79+4HjEh7ZWRkoEmTJqWOW1tbIzMzU/B8PEMTkdatW2Pp0qX4/PPPAQAJCQno27cvbt++jUqVKsHb2xu3b99GaGiohiPVPOaK1EXVzcJi5ujoCFNTU6xZs0b+6KbX8vLy4O7ujsePHyM8PFzQfFxDE5GnT58q7MqzsrLCixcv8PTpU5iammL06NF8Uu6/mCtSF1U3C4vZ4sWL4ezsjEaNGqFTp04KuYqOjka1atXK9UclC5qIWFlZISIiAt988w0A4Pjx46hSpQpMTEwAAHp6evImz48dc0XqoupmYTFr3rw5Lly4gODgYJw/fx7JyckAinPl6+sLFxcX1KhRQ/B8LGgiMmPGDLi5uSEiIgL6+vrYv38/JkyYIP/FfOrUKTRr1kzDUWoH5orUSZXNwmJnaGiIcePGYdy4ce89F9fQRObIkSMKzcJjx46Vj2VkZAAAt6X/i7kidVNFs/DHIDExUWnPXnnv1sOCRkSkYqpsFhaz58+fY+LEiTh48CAMDAzkD9h9+vQpcnJy4ODggMDAQMGXHXnJUYSys7MRFxen0Czcpk0brgkpwVyRqqm6WVjMvLy8kJSUhAMHDsDe3l5h7OzZs/D09ISXlxcCAwMFzcczNBEpKiqCr68v1q9fj1evXgGA/K/D+vXrw9/fnzv3/sVckbqoullYzMzNzRESEgJbW1ul4+fOnYOLi8v/t3fnUVHdVwDHv6i44TJGYKAG0QNWYsCIhkWhCOJShRyCRIOWonFLHMqJ2qpoVSJWDa6kIYoJSvRoFBeMCqUuaCq4kqitUXHBuEQRRI1FqcjWP4yTTAEz2BkGH/dzjueE+b33mzs3yp3fe+++x7Vr1/SaTxqrFSQmJobdu3eTlJRESkoKnp6efPDBBxw7dozQ0FBGjx7N/v37TR1mvSC5EsZi6GZhpXvW0ZDaHimRFZqCODk5sWbNGvr06QPAzZs3cXd3Jzc3l2bNmrFo0SL27dvHnj17TByp6UmuhLEYullYySZMmMC3337LRx99hJubm85YdnY2kyZNwtnZmVWrVuk1nxQ0BbGzsyMzM1P7zKXy8nLUajVnzpxBrVaTk5NDv379qty7sCGSXAljOXv2LMHBwfznP/95ZrOwtIXADz/8wLhx48jIyKB169a0b98egDt37vDgwQP8/f1JTEykbdu2es0nF4UoSLdu3UhOTmb69OkAbNu2DQsLC22zcEVFRZVvjA2V5EoYi6GbhZVMpVKxdetWzp8/z/Hjx7l9+zbwUxN6bZ9HKAVNQWbOnMnw4cP529/+RvPmzfn666+ZN2+edjwjI4Pu3bubMML6Q3IljMmQzcINQdeuXZ953lFfcshRYU6fPs2XX36pbRb28/MzdUj1luRKGJOhmoWV7uHDh2zdurVKrjw9PQkJCcHCwkLvuaSgCSGEARm6WVjJcnJyCA4O5sGDB/Tp00enZ+/IkSO0atWKlJQUnJyc9JpPCpoCXblyhSNHjmibhe3t7fHz85N/QNWQXAlDe/fdd/nXv/7F8uXLa2wW7t69u97NwkoWGBiIlZUVK1eupHnz5jpjjx49QqPRUFBQQGpqql7zSUFTkIcPH6LRaNi5cyfwpIfDysqKwsJCWrRoQXR0NOPHjzdxlPWD5EoYi6GbhZXM1taWAwcO1LgCO3v2LP7+/uTl5ek1nzRWK8if//xn8vPzOXToEN988w1vvPEGoaGhXL9+nYULFxIdHc2WLVtMHWa9ILkSxmTIZmElU6lUXLp0qcbx3NxcVCqV3vPJCk1BHBwc2LZtGz169ACe9Hg4OTlx+fJlWrZsyWeffca6devIzMw0caSmJ7kSxmLoZmElW7hwIStXrmTy5Mn4+fnpnEM7cOAAcXFxaDQabXvNL5GCpiD29vbs379fexVVaWkpNjY2nD9/HktLS3Jzc/Hy8uLWrVsmjtT0JFfCWAzdLKx0cXFxJCQkkJ+fr129VlZWolarmThxIu+//77ec0kfmoL07NmTFStWsHTpUuDJIyysrKy0V1kVFRXV6hJYJZNcCWMxdLOw0k2aNIlJkyZx5coVncv2n97FpzakoClIdHQ0wcHB7NixA3Nzc+7cucPKlSu149nZ2QwYMMCEEdYfkithbIZqFm4oOnXqpC1ipaWlzzWHHHJUmFu3brF7925KSkrw8fHRu3+jIZJcCWMxZLNwQ2RlZUVWVlatvxBIQRNCCAMydLOwkk2bNq3a1xMTE3nrrbe0VzguWrRIr/mkoCnQP/7xD44eParTLDxkyBC55U41JFfC0AzdLKxk7dq1w9nZucoFMocOHcLV1ZWWLVtiZmbGrl279JpPCpqC3L59m9DQUE6ePEmjRo2oqKige/fu5OXlUVhYSEREBDExMaYOs16QXAljMXSzsJItW7aMtWvX8sknn+Dt7a193dLSkqysrFqvYqWxWkGmT5+OjY0NV65c4caNG4wdOxYnJyfOnz9PSkoK69ev17nwoSGTXAljMXSzsJJNmTKFxMREIiMjiYmJoaKi4v+aT1ZoCtKxY0d2796tfXDgw4cP6dSpE7m5ubRp04bk5GSWLFlCdna2iSM1PcmVMBZDNws3BPfv39deup+YmIiHh8dzrdDksn0Fadq0qc5tdczMzCgvL6e8vBwADw8PuX/cjyRXwlhmzJhBixYtSEhIICYmpkqz8JQpU2rVLNwQtG3blqSkJNauXcugQYOee6UmKzQFCQsLA2DFihU0a9aMWbNmkZGRwYkTJ4AnvVVhYWGcP3/elGHWC5IrURcM0Szc0Fy8eJHs7GwCAwNr/dQLWaEpyF/+8heCg4Pp3LkzZmZmWFhYsHbtWu34hQsXGDlypAkjrD8kV6Iu/LxZWDxbeXk5d+7coW3btrz99ts0bty41nPICk1hiouLOXr0KI8fP8bNzU17HzlRVXFxMceOHaOkpERyJQzqhx9+4NixY6hUKtzd3XUObz98+JD4+Hg5h/ajXbt2ER8fz8mTJykrKwOgSZMmuLq6EhkZSWBgoN5zSUETQggDOnfuHG+++SaFhYVUVFTw2muvsW7dOjp27AhAQUEBTk5O3L1718SRml5SUhLTpk1jxIgR+Pv761xAs3//fjZt2sSiRYsYNWqUXvNJQVOYwsJCTp8+jYuLC5aWlhQUFLBu3TpKSkoIDg6mW7dupg7xhVBQUEBSUpJ8ixa1FhoaSpMmTVi1ahVFRUVERUVx/Phxdu3ahYODgxS0n3F1dWXy5MmEh4dXO75u3TqWLVvGqVOn9JpPCpqCPH0SblFREW3atOHLL79k1KhRmJubU1FRQV5eHunp6bi6upo61Hrv9OnT9O3bV37piFpzdHRk165d2pYQgJkzZ7J9+3Z27dpFmzZtpKD9yMbGhszMTLp06VLt+IULF/Dx8dH7MU5yUYiCzJ8/nzfffJP58+fz+eefExYWhr+/P3/9618BiIiIYMmSJWzYsMHEkZreoUOHnjmem5tbR5EIpXn8+HGVp1IvWLCAyspKAgMD+eyzz0wUWf3j5OTE6tWr+fDDD6sdT0pKqlUvmqzQFKRTp07s3r2brl27UlpailqtZu/evfTq1QuAU6dOMXLkSM6ePWviSE2vXbt2mJmZUVlZ819/MzMz+RYtaq1fv36MHz+eESNGVBmbPn06Gzdu5MGDB/J3C8jKyuLtt9/G1tYWPz8/rK2tgSeH/L/66ivy8vLYvHkzffr00Ws+WaEpSGlpqfZmqObm5rRs2VLnyj1LS0v5R/Sj9u3bs2DBAvr371/t+JkzZwgKCqrjqIQSBAYGsm3btmoLWmxsLOXl5axevdoEkdU/3t7eHD58mDVr1pCdnc2BAweAJz17AQEBvPPOO9jb2+s9n6zQFMTd3Z0lS5bg4+MDwO7du/Hx8aFFixYAfPPNN/z+97+XFRoQEhKCh4dHjY+vOH36ND4+Pty7d6+OIxNCPC9ZoSlIUFAQ+fn52p8HDRqkM56enk7Pnj3rOqx6afTo0RQXF9c4bmdnxyeffFKHEQkletosDE+OCjxPs7DQn6zQGpCioiKaNGmiXbEJIYzDkM3CDdl7773HzZs32blzp17bywqtAWndurWpQxBC8X7eLKzRaKo0C48dO7ZWzcINWWVlZa1uVCwrNIU5ceIEK1eu5NixYzo3RfXw8ECj0UgP2s9IroQxGLpZWOhPVmgKkpqayujRo/H29mb8+PFVnsM0aNAgkpKSCAgIMHGkpie5EsaSl5dH7969axz39PTUu1G4Ibhx4wZr1qyp8sXS09OTd955hw4dOug9l6zQFKR379689dZb/PGPf6x2fNmyZWzevJmjR4/WcWT1j+RKGIuvry+enp41NgvPmDGDI0eO8NVXX9VtYPXQkSNHGDZsGGq1mn79+lX5Ypmfn8+WLVvw9PTUaz4paAqiVqvJysqq8TYyFy9exNvbW+dKyIZKciWMxdDNwkrm6+uLu7s7ixYtqnZ8+vTpHD9+XNuf9ksaGTI4YVqdO3cmNTW1xvG0tDR5NtOPJFfCWJ42CwcEBHDmzBmSk5NJTk7mzJkzBAQEcOjQISlmP8rJyWH8+PE1jo8dO5Zz587pPZ+cQ1OQGTNmMHbsWDIzM6v9Znjw4EG5Q8GPJFfCmOzt7Zk7d66pw6j31Go1R48erfFIydGjR1Gr1XrPJwVNQYKCgrC1tSUhIYFVq1bpnGB1d3cnLS0Nd3d3E0dZP0iuhDC9yMhIpkyZwokTJ/D19a3yxfKLL75g4cKFes8n59CEEKIO1bZZWOlSUlJYsWIFp06dory8HIDGjRvTo0cPIiIiCA4O1nsuWaEJIUQdqm2zsNINHTqUoUOHUlpaqnObMHNz81rPJSu0BmTevHnk5+cTHx9v6lDqPcmVEC8eWaE1INnZ2Vy9etXUYbwQJFfi/2HIZmGhP1mhCSGEARm6WVjoTwqaEEIYkKGbhYX+pKApzMOHD9m6dWu1hzpCQkKwsLAwcYT1h+RKGIONjQ2ZmZk19lZduHABHx8fuZ+jEcidQhQkJyeH119/nVmzZnHnzh1sbGywsbHhzp07zJo1Czc3N3JyckwdZr0guRLG8rRZuCa1bRYW+pMVmoIEBgZiZWXFypUrad68uc7Yo0eP0Gg0FBQUPPOWTw2F5EoYS2JiIjNmzCAsLOyZzcJjxowxcaTKIwVNQWxtbTlw4ABOTk7Vjp89exZ/f3/y8vLqOLL6R3IljMmQzcJCf3LZvoKoVCouXbpU4y/p3NxcVCpVHUdVP0muhDEZsllY6E8KmoKEh4ej0Wi4ePEifn5+VS4XjouLQ6PRmDjK+kFyJeqCubk5NjY2pg6jwZBDjgoTFxdHQkIC+fn5mJmZUVn55H+vWq1m4sSJvP/++yaOsP6QXAmhLFLQFOrKlSsUFBRQWlpK+/btazy0JiRXQiiFFDSFs7OzIzMzUx5WqQfJlRAvNulDU7inh9HEL5NcCfFik4ImhBBCEaSgKdzy5cu1V/CJZ5NcCfFik3NoQgghFEFWaEIIIRRBCpoQQghFkIImFCE1NZXBgwfj6OiIjY0Nzs7OjBw5kn379pk6tBpNnDgRFxcXg82nUqlQqVTExMRUGausrOS1115DpVIxYcIEg72nEPWJFDTxwktISCAsLAwHBwc+/vhjkpOTmTp1KgAHDx40cXR1q3Xr1mzevLlKC8Lhw4e5du2aPONNKJrcy1G88OLj4wkICCA+Pl77Wt++fRk1ahQVFRUmjKzuDRkyhM2bN5OVlcVvfvMb7eubNm3Cy8uLq1evmjA6IYxLVmjihXfv3r0aH5jYqNFPf8ULCwuZNGkSvXr1wtbWlldffZVx48Zx8+ZNnX0WLlyISqXiwoULDB06lF/96lc4Ozuzfv164ElxcHNzo0OHDgQGBvLdd9/p7O/i4sKECRNYu3Ytrq6uqNVqfHx89FotFhcXEx0dTffu3bGysqJ79+4sWbJE78JsZ2eHt7c3ycnJ2tcePXrEjh07CA0NrXafwsJCJk+ezCuvvIK1tTVubm58/vnnVbapTe5yc3MZPnw4HTp0wNnZmdjYWJ3P8ODBA6ZOnYqzszPW1tY4OjoSFBTEhQsX9PqcQlRHVmjihdezZ082btxIp06dGDJkCI6OjtVud+/ePZo1a8acOXOwtLTk1q1bxMfHM2jQILKzs6s86HP06NGEh4cTGRlJYmIif/jDH7h8+TJZWVlER0dTVlZGVFQU48aNIyMjQ2ffrKwsTp06xezZs2natCkfffQRw4YNIysriy5dulQbX1lZGSEhIeTk5DB16lReffVVsrOzWbx4Mffu3WP+/Pl65SM0NJQZM2awZMkSmjdvTlpaGmVlZQQFBREbG6uz7b///W9++9vf8ujRI6KiorC3tycjI4MpU6ZQUlLCu++++1y5CwsLY+TIkWg0GtLT01m4cCEdOnQgLCwMgJkzZ5Kens7s2bNxcHDg7t27HDt2jPv37+v1GYWojhQ08cJbvnw54eHhzJkzhzlz5vDSSy/h5+fH7373O/r166fdrkuXLjq/0MvLy/Hw8MDZ2Zm9e/fyxhtv6MwbGRnJiBEjAHB1deXvf/87SUlJ/POf/6RNmzYA3Lp1i6ioKK5du0bHjh21+96+fZs9e/bw8ssvA08Ogbq4uLB48WI+/fTTaj/H1q1bOXLkCGlpaXh5eWn3A4iNjWXSpEl6NX4HBQUxdepU0tLSCAkJYdOmTQQEBNC6desq2yYkJHD9+nUOHz6Mg4MDAL6+vty/f5/Y2FjGjh1LkyZNap27iIgIbfHy9fUlMzOTbdu2aV/Lzs5m2LBhhIeHa/f53zmEqC055CheeI6OjmRmZpKWlsaf/vQnXFxcSE1NZejQoSxevFhn29WrV+Pl5UWHDh1o3749zs7OAFy6dKnKvAMGDND+t0qlwsrKCjc3N20xA/j1r38NwI0bN3T2ff3117XFDJ5crDFw4ECys7Nr/BwZGRnY2dnh4eFBWVmZ9k+/fv0oLS195r4/16pVKwIDA0lOTiY/P5/9+/fXeLgxIyODXr16YW9vr/Oe/v7+3L17l5ycHO22tcndoEGDdH5+5ZVX+P7777U/u7q68sUXX7B06VJOnjypfaqzEP8PWaEJRWjcuDFeXl7alU1eXh4hISHExsYyfvx4VCoVq1atYvr06URERODv749KpaKiooL+/fvz6NGjKnP+7xOrzc3Nq30NoKSkROd1a2vrKvNZW1uTl5dX42e4ffs2169fx9LSstrxu3fv1rjv/woNDWX48OGsWLECKysrfH19a3zPy5cv/+J71jZ37dq10/m5adOmOtstWrQItVrN+vXrmTdvHu3atSM0NJTZs2fTsmVLvT+nED8nBU0okq2tLeHh4URFRZGbm0uvXr1ISUmhb9++Oueirly5YpT3LygoqPY1W1vbGvd56aWXsLe3r3JBxlM/P6T5S3x9fbGysuLjjz8mIiKCxo0b1/ieVlZWfPjhh9WOPz0faejctWrViujoaKKjo7l27Ro7duxg7ty5NG3alLlz5z73vKJhk4ImXni3bt2q9jH3Fy9eBH5aLRUXF1c5j7RhwwajxPT111/z/fffaw87FhUVsWfPHgYOHFjjPv7+/uzcuRMLCwvtoczn1ahRI6ZOncq+ffu0561qes9PP/2Ul19++Znn54yZu44dOxIZGcmWLVs4d+6cQeYUDZMUNPHC6927N76+vgwYMAB7e3uKiorYu3cva9asITg4GDs7OwD69+9PXFwcS5cupVevXhw8eJAdO3YYJSZra2uGDh1KVFSU9irH4uJipk2bVuM+w4cPZ8OGDQQFBREREYGLiwuPHz/mu+++Iz09nQ0bNtTqcNyYMWMYM2bMM7fRaDRs376dwYMHo9FocHR0pLi4mIsXL3L48GE2btwIGD53AwYMYPDgwXTr1g0LCwsOHTrEt99+q70IR4jnIQVNvPBmz57Nnj17WLBgAbdv36Zx48Y4ODjwwQcfMHHiRO1206ZN4/79+6xYsYKSkhK8vLzYtm0bPXr0MHhMXl5eeHt7ExMTw82bN+natStbtmypsaUAnpyPS0lJYfny5axdu5arV6/SsmVLOnfuzMCBA2natKnB42zbti179uwhNjaWuLg48vLyaNu2LV26dNG56tDQuevTpw/bt28nLi6OsrIyOnXqxIIFC3jvvfcM9dFEAySPjxHCwFxcXOjdu3eNl+cLIYxDLtsXQgihCFLQhBBCKIIcchRCCKEIskITQgihCFLQhBBCKIIUNCGEEIogBU0IIYQiSEETQgihCFLQhBBCKMJ/AQb5t1PX7MD4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 100, 5000)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(50000, 100000)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulate_sample_mean(salaries, 'salary', 100, 10000)\n",
    "plots.xlim(50000, 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What did you notice about the sample means of the four bootstrapped samples above?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Question 2.4 **\n",
    "<br/>\n",
    "Next, let's think about how the relationships between population SD, sample SD, and SD of sample means change with varying sample size. Which of the following is true? Again, assign the variable `pop_vs_sample` to the integer corresponding to your answer. To gain some intuition, you can run the simulation cells below.\n",
    "\n",
    "1. Sample SD gets smaller with increasing sample size, SD of sample means gets smaller with increasing sample size\n",
    "2. Sample SD gets larger with increasing sample size, SD of sample means stays the same with increasing sample size\n",
    "3. Sample SD becomes more consistent with population SD with increasing sample size, SD of sample means gets smaller with increasing sample size\n",
    "4. Sample SD becomes more consistent with populatoin SD with increasing sample size, SD of smaple means stays the same with increasing sample size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_vs_sample = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb638d2f28>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check('tests/q2_4.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what happens: First, we calculate the population SD so that we can compare the SD of each sample to the SD of the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51697.0349864653"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_sd = np.std(salaries.column(\"salary\"))\n",
    "pop_sd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's then how a small sample behaves. Run the following cells multiple times to see how the SD of the sample changes from sample to sample. Adjust the bins as necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample SD:  40274.75276391259\n",
      "Sample size:  10\n",
      "Population mean: 79516.915\n",
      "Average of sample means:  nan\n",
      "Population SD: 40274.75276391259\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_10 = salaries.sample(10)\n",
    "sample_10.hist(\"salary\")\n",
    "print(\"Sample SD: \", np.std(sample_10.column(\"salary\")))\n",
    "simulate_sample_mean(sample_10, 'salary', 10, 1000)\n",
    "plots.xlim(5,120000)\n",
    "plots.ylim(0, .0001);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample SD:  55786.13587955388\n",
      "Sample size:  200\n",
      "Population mean: 74373.644\n",
      "Average of sample means:  nan\n",
      "Population SD: 55786.13587955388\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_200 = salaries.sample(200)\n",
    "sample_200.hist(\"salary\")\n",
    "print(\"Sample SD: \", np.std(sample_200.column(\"salary\")))\n",
    "simulate_sample_mean(sample_200, 'salary', 200, 1000)\n",
    "plots.xlim(5,100000)\n",
    "plots.ylim(0, .00015);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample SD:  51542.500433021785\n",
      "Sample size:  1000\n",
      "Population mean: 76946.87586\n",
      "Average of sample means:  nan\n",
      "Population SD: 51542.500433021785\n",
      "SD of sample means: nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_1000 = salaries.sample(1000)\n",
    "sample_1000.hist(\"salary\")\n",
    "print(\"Sample SD: \", np.std(sample_1000.column(\"salary\")))\n",
    "simulate_sample_mean(sample_1000, 'salary', 1000, 1000)\n",
    "plots.xlim(5,100000)\n",
    "plots.ylim(0, .00025);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's illustrate this trend. Below, you will see how the average absolute error of SD from the population changes with sample size (N)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S0rT+ue6Ho0MTfFq3VGvbI5dZhRCiTmh0ifXatWs89dRTxMbGYmJiQtOmTbl8+TLh4eH07NmTH374ARsbG60Emj59OgUFBQQGBmJsbExFRQWzZs1i4sSJAGRlZQHg7Oys9jpnZ2cyMqovR2ZnZ+Po6HjbQCInJyeys7NVfZo3b37bMa7v8/T0rDHf9TPQuuLhZsfBozdm0vn1jz34ezpp/Pq6zqtthpxfsuuPIec35OxgWPm9vLzuuF+jAvn222+TkJDAihUrGDVqFMbGxlRWVvLTTz8xc+ZM3n77bRYvXqyVwOvXr2ft2rWsWrUKX19fjh49yty5c3F3d2fcuHFaeY9/4m5fqLY5uzZja+xxrl/1zS8oxbpJU5q7Ot71tSkpKXWeV5sMOb9k1x9Dzm/I2cHw899Ko0usmzZt4r///S9PPvkkxsbGABgbG/Pkk08yb948Nm3apLVAb7zxBlOnTmXUqFG0a9eOMWPGMGXKFJYsWQKAq6srADk5OWqvy8nJwcXFBQAXFxdyc3PV7iUqlUouXbqk1qemY1zfV1/YN7GhnZeHWpuMZhVCCN3TqEBevnwZX1/fGvf5+vpy+fJlrQUqKipSFeHrjI2NqaqqAsDDwwNXV1e2b9+u2l9SUkJsbKzqnmNAQAAFBQXEx8er+sTHx1NYWKjWJzY2lpKSElWf7du306xZMzw81AuSvt06N2vsQZmbVQghdE2jAunh4UF0dHSN+37//XetFpQhQ4awdOlStmzZQlpaGps2bSI8PJxHH30UqL6XOHnyZJYtW0ZUVBSJiYmEhoZibW1NSEgIAD4+PgwcOJAZM2YQHx9PfHw8M2bMYPDgwarT/5CQECwtLQkNDSUxMZGoqCiWLl1KaGio2r3L+iCgsy/Gxjf+V53PuMS5i9l6TCSEEA2fRvcgx48fz2uvvUZhYSGjR4/Gzc2NrKws1q9fzzfffMP8+fO1Fmjx4sXMnz+fmTNncunSJVxdXXn++edVz0ACTJs2jeLiYmbPnk1+fj7dunVj/fr1qmcgAVatWsWcOXMYNWoUAEOHDlW7T2pnZ8eGDRuYNWsW/fv3x97enilTpjB16lStfRZtaWJjRQcfTw4nnlG1xR48gXvz+nMpWAghGhpFfn6+Rg/9vf3224SHh1NeXg5U39MzMzNTPfIhdGvH3gSWr/5Fte3m7MDSN/59x7NdQ79hbsj5Jbv+GHJ+Q84Ohp//VhqdQUL14JmXX36Zffv2kZeXh4ODA927d8fe3l6X+cTfHuzojYmJERUV1fdiM3PyOHs+k9buze7ySiGEEPfjrgWyrKyMCRMmEBoaSq9evVQP7Iu6ZW1lQWf/NuxPuPGM0Z6DiVIghRBCR+46SMfMzIwdO3aoRpEK/bl16rl9R5LrzbR4QgjR0Gg0ijUwMJD9+/frOou4i67t22JicuN/WWZOHucu5tzhFUIIIe6XRgXy3Xff5dtvv2XlypVcuHCByspKqqqq1P4I3bO0MKejb2u1tn1HkmrpLYQQ4p/QqEAGBQVx9uxZ5s6dS4cOHXB2dsbJyUn159Z5UYXuBHTyVtuOO3JST0mEEKJh02gU65w5c+rdw/ONVbcO3igUv6rmZj13IYfMnMu4OTfVbzAhhGhgNCqQYWFhus4hNNTExgr/tu4cTzmnatuXkMxjA3roMZUQQjQ8d73EWlZWhqenJ7/++mtd5BEa6N7JR207/rDchxRCCG3T6DEPExMTLCws6iKP0EDALQUy+ewF8q5c01MaIYRomDQapDN8+HB+/vlnXWcRGnJ0aEIbD/UJAvYlJOspjRBCNEwa3YMcOHAgc+fOZdy4cQwfPhw3N7fb+vTr10/r4UTtAjv5cDotQ7UdfySJQX266TGREEI0LBoVyHHjxgFw8eJFtcWRFQoFSqUShUKh1TUhxd117+TD91F/qrYTU85xrbAYW2tL/YUSQogGRKMCeXNRFPVDc1dHWro5kp6ZC0BlZRUHj52iX2AHPScTQoiGQaMC2bt3b13nEPchoLMv6dG7VdvxR05KgRRCCC3RaJDOdbm5uURHR/P999+Tl5cHQElJiUw1pye3jmZNOHGWktIyPaURQoiGRaMCqVQqef311/H392fs2LFMnTqVtLQ0AJ5++mk++OADnYYUNfNs6Yqzo51qu6y8giMnzugxkRBCNBwaFciPPvqIzz77jDlz5vDHH3+oLbE0ZMgQtmzZorOAonYKhYKAjupzs8YflrlZhRBCGzQqkN988w1z5sxh5syZdOrUSW1f69atOXv2rE7Cibu7dVadg8dPUVFRqac0QgjRcGhUIDMyMnjwwQdr3GdqakpRUZFWQwnN+bRuiZ2tlWq7qLiMY8mp+gskhBANhEYFslmzZpw4caLGfceOHcPDw0OroYTmjIyMbp+bVdaIFEKIf0yjAjlixAgWL17M3r17VW0KhYJTp04RHh7OE088obOA4u6633Ifcn9CsowsFkKIf0ij5yDnzp1LfHw8w4YN44EHHgBg/PjxXLhwgYCAAGbMmKHTkOLO2nt7YmVpRlFx9SMeV64VkXQmXbP/uUIIIWqk0RmkpaUlv/zyC8uXLycwMJCHHnqIrl27snTpUjZu3IiZmZmuc4o7MDExpmt7L7W2fXKZVQgh/hGNTzKMjY0ZM2YMY8aM0WUecZ8COvmwa99x1XZ8QjI9Osi9YSGEuF/3NJOOqL86+bXGzPTGv3dycq9wIUsmkBdCiPslBbKBsDA3o5Nfa7W2hJNpekojhBCGTwpkA3Lr4x4JyVIghRDifkmBbEC6tm+LsfGN/6VZl65wIfOSHhMJIYThkgLZgNhaW+Lv5a7Wti8hWU9phBDCsMlyVw3MrUtgyaw6Qghxf2S5qwame0dvFIob26fTMrh0+Yr+AgkhhIGS5a4aGAc7W7xbtVRrk8usQghx72S5qwaoeyf1uVllVh0hhLh3stxVA3Tr5OWJp85x5VqhntIIIYRhkuWuGiA356Z4tHRRbSuVcPBYih4TCSGE4ZHlrhqogI63jmaV+5BCCHEvNCqQc+fOxdvbm2HDhtG1a1egermroKAgWrduLctd1UO3zapz8gxFxaV6SiOEEIZHo9U8ri93tW7dOmJiYmjdujVNmzZl9uzZPPnkk5iYyMqD9Y17c2ecHGwpLqt+RrWioopDx0/R68F2ek4mhBCGQZa7aqAUCgUdfNyJP5qqatuXkCQFUgghNKTRJdamTZty4MCBGvcdPnyYpk2bajWU0I6OPp5q24eOn6asvFw/YYQQwsBoPJNObSorK1HcPHWLqDc8mjvhYGej2i4pLefoyVT9BRJCCANyxwJZVVVFZWWl6u+3/iksLGTr1q04OjrWSVhxbxQKxW3PRMrcrEIIoZla70EuXLiQxYsXA9W/aAcPHlzrQV544QXtJxNaEdDZh993HlRtHziaQmVlJcbGxnpMJYQQ9V+tBbJ3795A9eXVxYsX89xzz9G8eXO1Pubm5vj4+DBkyBCthsrMzOStt95i69atFBQU4OnpyYcffqiWaeHChXz99dfk5+fTrVs3PvjgA/z8/FTHyM/PZ86cOURHRwPVc8YuXrwYe3t7VZ/jx48ze/ZsDh48iIODA+PHj2fOnDkN6pKxXxt3bK0tuVZYDMC1wmJOnD5Pe29P/QYTQoh67o4F8npBUigUjBs37rYCqQv5+fkMHjyYHj168OOPP+Lo6EhaWhrOzs6qPsuWLSM8PJzw8HC8vLxYvHgxI0eOZN++fdja2gIwceJE0tPTiYyMBODll1/mpZde4ocffgDg6tWrjBw5kqCgIGJiYkhJSWHKlClYWVnxn//8R+efs66YmBjTtX1bdsQdVbXtO5IkBVIIIe5Co8c85s6dq+scKh9//DFubm6sWLFC1ebp6an6u1KpJCIigunTpxMcHAxAREQEXl5eREZGMmHCBJKSkti2bRvR0dEEBAQAsGTJEoYOHUpKSgpeXl6sW7eO4uJiIiIisLS0xN/fn+TkZJYvX87UqVMb1FlkQCcftQIZfySZ8SGDGtRnFEIIbdOoQE6ZMuWO+xUKBZ9++qlWAm3evJkBAwYwYcIEdu7ciZubG+PGjePFF19EoVCQlpZGVlYWDz/8sOo1lpaWBAUFERcXx4QJE4iPj8fGxobAwEBVnx49emBtbU1cXBxeXl7Ex8fTs2dPLC0tVX0GDBjA/PnzSUtLUyvKN0tJMaw5TVNSUrAyqaSivJTSsgoACgsL2bYjFs8Wznd5tf4Z2vd9M8muP4ac35Czg2Hl9/LyuuN+jQrkX3/9ddvZRn5+PteuXcPOzg47O7v7T3iL1NRUPv/8c0JDQ5k+fTpHjx7l1VdfBWDSpElkZWUBqF1yvb6dkZEBQHZ2No6OjmqZFQoFTk5OZGdnq/rcesn4+jGzs7NrLZB3+0Lrk+tnywC9Azqx99BJ1b6cK6U88lD9/iw35zc0kl1/DDm/IWcHw89/K40K5NGjR2ts3717N6+88gorV67UWqCqqiq6dOnCm2++CUCnTp04c+YMq1atYtKkSVp7n8YmoJOPWoGMO3ySsY8/JJdZhRCiFhpNFFCbXr16ERoaqjrD0wZXV1d8fNQn2vb29iY9PV21HyAnJ0etT05ODi4u1Us8ubi4kJubqzbBgVKp5NKlS2p9ajrG9X0NTWf/NpiY3PjfnZmTx/mMnDu8QgghGrd/VCChegBNQkKCNrIA1fcKT506pdZ26tQpHnjgAQA8PDxwdXVl+/btqv0lJSXExsaq7jkGBARQUFBAfHy8qk98fDyFhYVqfWJjYykpKVH12b59O82aNWuQ61taW1ncNnJ1nyyBJYQQtfpHBbKiooLvvvtOq49/hIaGsm/fPj744APOnDnDxo0bWblyJRMnTgSq7yVOnjyZZcuWERUVRWJiIqGhoVhbWxMSEgKAj48PAwcOZMaMGcTHxxMfH8+MGTMYPHiw6vp4SEgIlpaWhIaGkpiYSFRUFEuXLiU0NLTBXnYM7Oyrti2z6gghRO00ugf52GOP3dZWXl7OqVOnuHz5MkuWLNFaoK5du/Ldd9/x9ttv8/7779OyZUvmzZunKpAA06ZNo7i4mNmzZ6smCli/fr3qGUiAVatWMWfOHEaNGgXA0KFDVTMDAdjZ2bFhwwZmzZpF//79sbe3Z8qUKUydOlVrn6W+6dbBC4UCrl95Tk3PIjs3HxdH+zu/UAghGiGNCmRVVdVtZ1U2NjY89thjPPHEE/Tp00eroQYPHnzHqe0UCgVhYWGEhYXV2sfe3v6ug4fatWvHb7/9dt85DY2drTV+bd1JTDmnaos/ksSjDwfe4VVCCNE4aVQgN2/erOscoo507+itXiAPS4EUQoia/ONBOsKw3Lq6R/LZdPKvFugpjRBC1F+1nkGuWbPmng40duzYfxxG6J6zoz2t3d04cy4TqL4fuT8hmYG9u+o5mRBC1C+1FsjQ0FCND6JQKKRAGpCATj6qAgkQdyRJCqQQQtyi1gJ55MiRuswh6lD3Tj6s3bRDtX08OY2ComJsrCzv8CohhGhcai2Q7u7udZlD1KGWbk60cHXkQlYuAJWVVbz98Xf079GJ3t3bY2sthVIIITQaxXpdYmIiu3fvJi8vDwcHB3r37q22SLEwHAGdfdiwZY9qOy09m68it7J64x90a+/FQz060smvNcbGxnpMKYQQ+qNRgayoqCA0NJTIyEi1+U0VCgUhISFERETIL1ID0zegA1Hb9lJZWaXWXlFRRdzhJOIOJ2HXxJo+3dvzUI+OPNCs/i+NJYQQ2qTRYx4LFy5kw4YNzJs3jyNHjpCZmcmRI0eYN28eGzZsYNGiRbrOKbSsuasjr//naTr4elLbzHpXrhbyyx9xzJr/GfPe/5Lfdx7gWmFx3QYVQgg90egM8scff2TWrFnMmjVL1ebu7s6sWbOorKzku+++Y968eToLKXTDr607r019mty8q/wVf5QdcQlkZOfV2Pd0Wgan0zL4+qetdO/oTb/AjnT0bSVXDoQQDZZGBTIzM1O1CsatAgMD+fDDD7UaStQtR4cmjBzcixGDgkg+e4E/9x4h9uAJikvKbutbUVFF7MGTxB48iYOdDX0C2tMvsCMt3Zz0kFwIIXRHowLp5ubG3r17eeihh27bFxcXh5utMIefAAAgAElEQVSbm7ZzCT1QKBT4tG6JT+uWjA8ZRPyRJHbsTeBYcio33XpWybtSQNTWvURt3Utbz2Y8FNiJoG7+WFtZ1H14IYTQMo0K5JNPPsmHH36IkZERTz75JK6urmRlZbF+/Xo+/PBDpk2bpuucoo6Zm5nSp3t7+nRvT05uPjv3HWNH3FEyc2q+BHsqNYNTqRms37KbuZOfwqNFw1t0WgjRuGhUIOfOnUtqaioLFixg4cKFqnalUklISAivvvqqzgIK/XN2tOeJIb0ZObgXJ0+fZ0fcUWIPJlJSWn5b38v513h/5ToWzPmXPE8phDBoGhVIExMTVq1axcyZM9mzZ4/qOcigoCB5DrIRUSgU+LV1x6+tO+NDHrnpEmyaWr+c3Css+3ID80LHYGQk8+ELIQzTPU0U4OfnJwVRAGBhbkbfgA70DehATm4+azf9ya79iar9R0+msnbTDp4O7q/HlEIIcf80+ud9XFwc0dHRqu28vDxeeOEFgoKCeO2116isrNRZQFH/OTvaM/nZx/Bp3VKt/eetscQePKGnVEII8c9oVCDfeustDh8+rNp+7bXX2Lp1K23atOGLL76QxzwEJibGvDLxCRzsbNTa//fdL5y7mK2nVEIIcf80KpDJycl06dIFgPLycqKiopg/fz7ffvstr732GpGRkToNKQyDfRMbZk4chYnJjR+rktJyPvzsJwqKZAYeIYRh0ahAFhYWYmtrC8CBAwcoLCxkyJAhAHTq1In09HTdJRQGxatVCyaEDFJry8zJ45OvfqaqqqqWVwkhRP2jUYFs1qwZx44dA2Dbtm34+fnh7Fw9eXV+fj6WljKcX9wwsHdXBvTqrNZ2OPEM637dqadEQghx7zQqkKNGjeKdd95h3LhxhIeH89RTT6n2HTlyhDZt2ugsoDBME0IG4eXZXK1tffRu9h1J0lMiIYS4NxoVyLCwMKZNm0ZpaSnTp08nNDRUte/YsWMEBwfrLKAwTKamJrwycRR2TazV2sO/3UR65iU9pRJCCM1p9ByksbGx2koeN/v++++1Gkg0HE3tbXnlhSd4++PvVOtOFpeU8eFnkbw7c7zM2SqEqNfuaZqTM2fOsG7dOj7++GPWrVvH2bNndZVLNBC+bR7g+VED1douZl1m+epNaotvCyFEfaPRGWRJSQkzZ87khx9+UJsUwNjYmLFjx/LBBx9gbm6us5DCsA3q043TaRnsiDuqatufkML66N2MGtpbj8mEEKJ2Gp1Bvv7666xbt46wsDAOHTpEeno6hw4dYu7cufzwww+8/vrrus4pDJhCoWDimCG0dldfFm3dr39x8FiKnlIJIcSdaVQgf/rpJ1599VVmzpyJp6cn1tbWeHp6MmvWLObMmSMTBYi7MjM1ZebEUTSxufFIkFIJn3z9MxezcvWYTAghaqZRgSwrK6Nbt2417nvwwQcpL7992SMhbuXU1I5p/xqJkZFC1VZUXD1op7ikVI/JhBDidhoVyH79+hETE1PjvpiYGPr06aPVUKLhau/tybMjB6i1pWfmErH6Fxm0I4SoV2odpJOamqr6+5QpU3jppZcoKioiODgYFxcXsrOz2bhxI7///jsrV66si6yigRj2UHfOpF1UWx4r7nASUVtjCR4UpMdkQghxQ60FskuXLigUNy6FKZVKPv/8c7744gu1NoDhw4dz+fJlHcYUDYlCoWDS08M5n3mJtPQbK32s2fQnHi1d6ewvMzMJIfSv1gIZHh5elzlEI2NuZsqsF0OYt/hLrhVWr/ShVMLHX23kvdkTcHNuqueEQojGrtYC+fTTT9dlDtEIuTja8/KEEbwXvobrtx8Li0r5cNVPvPPK8/oNJ4Ro9O5pJh0htK2jbyvGPt5fre3chRxWfv+rDNoRQuiVRjPpAOTk5BAZGcmpU6coKSlR26dQKPj000+1Hk40Do8P7MHZ8xnEHjypatt9IBFz4yq8vLzU7oULIURd0ahApqSk8Mgjj1BZWUlhYSGOjo7k5eVRWVmJvb09TZo00XVO0YApFAr+/cyjXMjM5dzFHFX7pu0HSDybRWBnXwI7+9DavZkUSyFEndF4qrmuXbuSnJyMUqlk3bp1ZGZm8vHHH2NlZcXq1at1nVM0cBbmZsx8cRTWVupz+mbm5PHz1ljmvf8VU98M55uftnLy9Hm5/CqE0DmNziAPHTrERx99pJqQXKlUYmJiwnPPPUdubi5hYWH88ssvOg0qGj4356a8PH4ECyN+oKb6d+nyVTZv38fm7fuwb2JNQCcfArv44tfmAYyNjes+sBCiQdOoQBYWFuLg4ICRkRFNmjQhN/fG3JldunTh/fff11lA0bh09m/D26+MY8OWPcTuP1prv/yrhfy+8yC/7zxIExtLunXwpkcXX9p7e2JiIsVSCPHPaVQg3d3dyc6ufqDby8uLjRs3MnBg9Rp/W7Zswc7OTncJRaPj3aolr/77SRKOtuNqKcQdOsHhxDOUlVfU2P9qQTHbY4+wPfYI1lbmdG3vRY/OvnT0a4WZqWkdpxdCNBQaFciHHnqI7du3M2LECKZMmcK//vUv9u7di4mJCcnJycycOVPXOUUjZGlhRscOXvR+sB0lpWUcTjxN/OEkDhxLoaS05gnyC4tK2Rl/jJ3xx7AwN6VLuzYEdvajW4e2UiyFEPdEowL55ptvUlpavdrCyJEjsbCwYMOGDRQVFfHvf/+b55+Xh7qFblmYm9Gjix89uvhRVl7O0ZOpxB0+yf6jyRQW1bwSSElpObEHTxJ78CQujnbM+feTPNDMuY6TCyEMlUajWM3NzdUe5Rg6dCgrV65k9erVjB8/XqdD7z/66CPs7e2ZPXu2qk2pVLJgwQJ8fX1xc3Nj+PDhnDhxQu11+fn5TJo0CXd3d9zd3Zk0aRL5+flqfY4fP86wYcNwc3PDz8+PRYsWyehIA2Bmakq3Dl6EPvcYK9+bzrwpYxjYuwt2tla1viY79woLlq8lN+9qHSYVQhiyej2Tzr59+/jqq69o166dWvuyZcsIDw9n0aJFxMTE4OzszMiRI7l27Zqqz8SJE0lISCAyMpLIyEgSEhJ46aWXVPuvXr3KyJEjcXFxISYmhoULF/LJJ5/IhAcGxsTEmE5+rXlxzFD+N/9l3nj5GYb064aDnc1tfXPzrvFe+BrV3K9CCHEn9bZAXrlyhRdffJFPP/0Ue3t7VbtSqSQiIoLp06cTHByMv78/ERERFBQUEBkZCUBSUhLbtm1j6dKlBAQEEBAQwJIlS9iyZQspKSkArFu3juLiYiIiIvD39yc4OJhp06axfPlyOYs0UEZGRrTz9mDC6MFEvPsf3pk5jp5dfdX6pGfm8v6KdZTJIt9CiLuotwXyegHs27evWntaWhpZWVk8/PDDqjZLS0uCgoKIi4sDID4+HhsbGwIDA1V9evTogbW1tVqfnj17YmlpqeozYMAAMjIySEtL0+VHE3VAoVDg3aolL48fQWBnH7V9SWfSWfblRiorK/WUTghhCDSei7Uuff3115w5c6bGhZizsrIAcHZWH2zh7OxMRkYGANnZ2Tg6OqrdG1UoFDg5OakeV8nOzqZ58+a3HeP6Pk9PzxqzXT8DNRSGlvdW2sg/JMiftPMXOH0uS9W2I/YwZSVFjB7SU2f30A35uzfk7GDY+Q05OxhWfi8vrzvur3cFMiUlhbfffpvo6GhM6+Gw/Lt9ofVJSkqKQeW9lTbzv/dqK95a+q3aXK8JyRfwaZtFyLA+WnmPmxnyd2/I2cGw8xtydjD8/Le6p0usVVVVJCYmsmvXLgoLC3USKD4+ntzcXHr06IGjoyOOjo7s3r2bVatW4ejoSNOm1Qvp5uTkqL0uJycHFxcXAFxcXMjNzVW7l6hUKrl06ZJan5qOcX2faFisrSwICx2Do4OtWvu6X3eybddBPaUSQtRnGhfIzz77DG9vb3r37s3jjz+uOo1++umn+d///qe1QMOHD2fPnj3s3LlT9adLly6MGjWKnTt30rZtW1xdXdm+fbvqNSUlJcTGxqruOQYEBFBQUEB8fLyqT3x8PIWFhWp9YmNj1Zbu2r59O82aNcPDw0Nrn0fUH03tbZk3ZSy21pZq7at+iGbfkSQ9pRJC1FcaFcivv/6auXPnMnz4cL788ku1M7OePXsSFRWltUD29vb4+/ur/bGyssLBwQF/f38UCgWTJ09m2bJlREVFkZiYSGhoKNbW1oSEhADg4+PDwIEDmTFjBvHx8cTHxzNjxgwGDx6sOv0PCQnB0tKS0NBQEhMTiYqKYunSpYSGhsqSSg1YSzcn5vx7NGamN+4uKJXw8Vc/c/L0eT0mE0LUNxoVyPDwcKZOncqyZct49NFH1fZ5e3tz6tQpnYSrzbRp05g8eTKzZ8+mf//+ZGZmsn79emxtb1w+W7VqFe3bt2fUqFGMGjWK9u3bs2LFCtV+Ozs7NmzYQEZGBv3792f27NlMmTKFqVOn1ulnEXXPu1VLpv9rJEZGN/4hVFZeweIVP3I+I+cOrxRCNCYaDdJJS0tTe6ziZlZWVly5ckWroW61efNmtW2FQkFYWBhhYWG1vsbe3r7GUbA3a9euHb/99ptWMgrD0q2DF5PGDuV/3/2qaissKmXB8rW888rzODrIIuBCNHYanUE6Ojpy7ty5GvedOnWKZs2aaTWUEHWhf8/OPPWo+nO2MtuOEOI6jQrk4MGDWbx4Mampqao2hUJBbm4uy5cvZ/jw4brKJ4ROjRzci0F9uqq1yWw7QgjQsEC+9tprmJub07NnT4KDg1EoFMydO5eAgACMjY2ZM2eOrnMKoRMKhYIJowfJbDtCiNtofIl1+/btzJgxg4qKClq1akVFRQUvvvgiv//+uyyYLAyakZERU59/HH8vd7X2/QkpfP7jFpmbV4hGSuOZdGxtbZkzZ46cLYoGyczUlFkvhvDWsm85d+HGSNY/dh/GoYkNo4f3vcOrhRANUb2drFyIumZtZUHY5DE4NVUfwRr52y627pTZdoRobDQ6g3zsscdq3WdkZESTJk3o3Lkzzz33nEzTJgxaU3tb5oWO4c0l36qNZP38x2jsmlgT0MnnDq+unVKp5Mq1QrJz88m6lE9Obj7XCorp4OtJ1/YNZ+5KIRoSjQqkUqnk9OnTZGZm4uHhgYuLC9nZ2aSlpeHm5oazszNbt25l+fLlbN68GV9f37sfVIh6qsXfs+288/H3lJVXANdn29nIf6eMxa+te42vKyouJTu3uvgdOXaCXYfPkJ17hexLeWTnXlEd62a//rmPIf268dzIgZiYGOv0cwkh7o1GBXLKlCmEhYWxfft2OnfurGo/fPgw48eP59VXX6Vz58488cQTvPPOO3z33Xc6CyxEXfBu1ZIZL4zk/ZWRVFVVD9IpL6/k/ZXrmPjUUIqKS6qLX27e30UwX+2Ms7CwEGtra43eK3rHAdIzLjH9hSdumydWCKE/Gt2DfPfdd5k7d65acQTo3Lkzr776KvPnz6dFixb85z//Yc+ePToJKkRd69q+eradmxUWlbLsy418tjaan7fGEnvwJKfTMv7xxALHktOYt/gL0i5k/6PjCCG0R6MCefr0aZycnGrc5+TkxJkzZwBo1aoVRUVF2ksnhJ7179mZMY/108qxrCzN8GjpQveO3gzv3/22wUDZuVd446OvZWURIeoJjS6xuru78/XXXzNw4MDb9n311Ve4u1ffk8nNzVWt1yhEQzFiUBB5VwrY8teBO/YzMTHCuakdzo72UFlGe18vXJwccHG0w8XJARsrC7WVYoIHBbHk8/WcOHVjFZGS0nI++OwnnhzelyeG9JKVZYTQI40K5Jw5c5g0aRJBQUE8/vjjODs7k5OTQ1RUFCdOnGDVqlUA7Nixg27duuk0sBB1TaFQMD7kEWysLdiXkIyluTkuTva4ONrh6uiAs6Mdrk4OONjZYGRUfVFGk5XV7WyteW3q03wZ+Tvbdh1S2/fj5r84dzGbyc8+ioW5mc4+mxCidhoVyJCQEBwdHVmwYAEfffQR5eXlmJqa0qVLFzZs2MBDDz0EwPz58zE2lpF4ouExMjLiyeH9eHK4di63XmdiYsyLY4bi0cKFryK3UllZpdq399BJMnIuM/vFkOqzUiFEndJ4Jp3+/fvTv39/qqqqyM3NxdHRUfWv5essLCy0HlCIxmBQn260cHViyefr1Qb8pKVn898PvmLGC0/U+niJEEI37nkmHSMjI5ydnW8rjkKIf6adtwfvzZmAewtntfYr14p455Pv2bZLZvMRoi5pfAZZVlbG1q1bOXXqFCUlJWr7FAqFzNEqhBa4ONrzzivPE/5NFPFHklXtlZVVfLY2mrQL2Tw/6hGZVECIOqBRgczIyGDIkCGcO3cOhUKhWt3g5hF2UiCF0A4LczNemTiKyF93EvnbLrV9v+88yIXM6kkFmthY6SmhEI2DRtdJ33jjDZycnDh27BhKpZI//viDw4cPM3PmTFq3bs3hw4d1nVOIRkWhUDB6eF9emfgE5mbq/449nnKOee9/KZMKCKFjGhXI2NhYpk6dSrNmzapfZGSEh4cH//3vfwkODubVV1/VaUghGqvAzr68/crzODuqr7ma8/ekAnGHT+opmRANn0YF8vLly7i5uWFkZIS1tTX5+fmqfX379mX37t06CyhEY+fZ0pX5s8bftqBzSWk5H61aT+SvO+vlos5KpZKy8nJ9xxDivml0D7J58+bk5uYC4OnpSUxMjOrZxwMHDmBubq6zgEKI6kkF/jtlLF//tJXfb1mbct2vOzl3MZuXnh6OtVXdPWpVWFTCpbyrXM6/Sm7eVS7lXSU3/xqX//775fxrlJVX4OJoRztvT9r7eNLOyx0HO9s6y1gXlEqlzHjUQGlUIPv06cPu3bt59NFHmTBhArNmzeLo0aOYmpryxx9/MGHCBF3nFKLRMzEx5oWnhuDe3IUvI39Xm1Qg7nAScYeTMDM1wcrSHCtLc6wtLbCytMDa0hwrKwus//675d/7rK0s/u5b3W5tZYGpSfWvhNKyclXRqy6A17iUd4XcvGvk5l0hN/8axSVlGuXOzr1CduwRtsceAaClm2N1sfT2xK+tu8GuYHI06SyRv+4kNT2LHl38mDB6kMx61MBoVCBfe+018vLyAHjhhReoqKhgw4YN5ObmMm3aNBnBKkQdeqRPV1q4ObLk8/VcLVBfRaSsvIKy8gryrxbe17FNTIwoLSnB2ER3v+jTM3NJz8wlescBFIrqS8jtvD3p4OOJb5sH6n2RSc+8xOoNf3Do+GlV2597E8jOzefVfz9Z7/MLzSny8/PvevPiypUrWFhYyKVUA6PJfKD1mSHnr4vsObn5LF65jnMXcrR63HtZy/JuFIrqxaY1ZWxsRFuP5rTz9qCdtwferVpgZmp6T++pq+8+/2oh6379iz92H6r1M/l7uf+jImnIP/Ng+PlvddczyIqKClq3bs3q1asZOnTo3boLIeqI89+TCvy4+S/2HEjkakGR2mVXXTM1NcbRvglODk1o6tAER3tbnBya4NTUjqb2tjjaN8HExJikM+kcT07leHIap9Iu3rFgVlZWkXQmnaQz6ayP3o2ZqQk+rVvSztuD9t6etHZ3q/P5nkvLytkcE8fPW2MpKb3zoKPElHMsjPiBuZOfkjPJBuCuBdLExAQXFxeZhFyIesjC3IxxTwxk3BMD/x41WkFRcQmFxaUUFpWo/l5UXFK9XVJKUXEphUXFqvaiv/sWFpdQUVFdYI2MFKoiV10Ab/z9egFsYmOl0eCUjr6t6OjbCqge2JN05jzHklI5lpJGWvqdn+UsK6/gaFIqR5NSgR3YNbEmqKsfvbq1o61nc50OjqmqqmJn/DHW/rKDy/nXauzj26YlJaXlpKZnqdpOnDrfYIpk/tUCft2+j/TMHPr36ET3Tj76jlSnNLoH+eSTT/LNN98waNAgXecRQtwnhUKBuZkp5mam9zVSVKlUUl5RwfHEk3Tq0E4n8y1bW1nQtb0XXdtXX4a7WlBEYkoax5PTOJacysWsy3d8/ZWrhfz2535++3M/rk72BHXzp3f39rR0q3lB9/t1NOksqzfEqBW+m7k5O/DMiIfp3tGbwuIS5n+6hjPnMlX7Db1IlpSWsWnbXn6JiVOdNR84eoqJTw3hkT5d9Zyu7mi8YHJkZCT9+/dn2LBhuLq63vYvt+eee04nAYUQdUOhUGBmaoqNlUWdLUbQxMaKHl386NHFD4DL+dc4lpzKsaRUjqekceny1Vpfm3Upnw1b9rBhyx48WrrQq1s7enXz/0d5zmfk8N3GGLUBODeztbZk1NDePNK7q2o+XBsrS/47dSzvha/ldFqGqu+JU+dZsHwtcyc/haWFYYzfqKysZHvsEdb9urPGgV6f/xiNg50ND3b01kO6uqfRIB0HB4c7H0Sh4PLlO//LT9Q9Q79hbsj5Jfs/p1QqybqUV312mZTK4ROnKSq++6Mlrk1teOyR3gR28dV4vtr8qwWs+3VnrQNwTEyMGNY/gBGPBNX6rGlhUQnzw9eoFUkAn9YtCQvVrEjq67tXKpUcPHaK73+OIT0z9459zUxNeOPlZ/Bq1eK2ffXlZ0dbNCqQ586du+uB3N1lrbr6xtB/WA05v2TXvrLycg4dP83u/cc5ePwU5eWVNfa7PgrX2NiITn6t6fVgOx7s4FXjpU5NBuD06ubP2Mcf0mjR6n9aJPXx3Z9Ou8jqjTEkptT8e97W2pKComK1fzg0sbHknZnP4+bcVK1vff3ZuV8aFUhhmAz9h9WQ80t23SosKmF/QjI79x/jWFKq2i/vmh5TMTcz4cEO3vTu3o6Ovq0xMlKwM/4Yazb9Sd6Vghrfw6/tAzw7YgBtPZvfc7baiuTcyU9hZVl7kazL7z47N5+1UX+y+0BijfvNTE0Y1r87jw/sSezBRD5bG622383ZgbdfGYed7Y3v2hB+du7FPRXIY8eOsWfPHi5fvsyECRNwdXXlzJkzODs7Y2vbsKaPaggM/YfVkPNL9rqTf7WA2IMn2L3/OCmpF+/6HKettSVNbKy4kFXzpcRmLg48E/wwD3b0vu9RsoVFJby3fA2nUtWLpHerFoSFjqm1SNbFd3+tsJgNW3az5a/9qlHLN1MooG9AB556tB+ODk1U7Ws3/cmGLXvU+rb1bMYbLz+LuZlpneWvSxoVyNLSUiZNmsSmTZtU8w7GxMTQuXNnnn32Wdq2bctbb71VB3HFvTD0H1ZDzi/Z9SMz5zI/bY7h9PncWgtgbWytLQkZ1oeBvbpoZUHq+ymSuvzuy8rL2fLXATZs2U1hUWmNfTr5tebp4P54tnS9bZ9SqWT56l/4K+6oWnu3Dm2ZOXEUxsbGBv2zUxONhqq98847/Pnnn6xYsYKUlBS1lQMeeeQR/vjjD50FFEIITbk5N2VQr058+NokFs39F48NDMTR4c5Xt0xNjXn8kR4se3MyQ/o9qJXiCNWPtMwLHYvXLZdok89e4L3wNRQV11yktE2pVLJr/3FeeXclqzfE1FgcPVq68N+pY5k3ZUyNxRGqB2O+NHYYHXw91doPHD3FF+t+r5cryvxTGj3m8dNPP/Haa68xevRoKivVb4x7eHhoNIhHCCHqikKhwLOlG54t3Xgm+GFOnDrH7gOJxB06ybXCG/PX3ssAnPthbWVBWOgYFixfS0rqRVV7SupF3gtfQ1joGJ2uwHIsOZXvNsaoPaN5M0cHW54a3o8+Ae01erTHxMSYV14YxVvLvlWb5GHbrkM4N7WjXStnrWWvDzQqkJcvX8bbu+bnXqqqqigr02xWfyGEqGsKhQJ/Lw/8vTyYEDKIhJNnuJCVSwcfTzxbuun8/a2tLJg3ZSzvha+5rUguWL5W60WyoqKSM+cz2LBlDwePnaqxj5WlGSMeCWJo/+73PNetlaU5YZOf4r8ffEVu3o0ZhtZE/cmIh7s2qEusGhVIDw8P9u3bR79+/W7bd+DAAdq2bav1YEIIoW0mJsZqM/nUFStLc+ZNGcuC5WtJPntB1a6NIpl/tYDksxdIPptOytkLnDmXSVl5RY19jY2NGNSnKyMH91IbfXqvHOxsCQsdw5tLvlG7ZLt28y7a+fuophY0dBoVyDFjxvDRRx/h7u7O448/DlT/q+yvv/5i+fLlzJ07V6chhRDC0FlZmhMWOoaFET+QdCZd1Z6SepH3lq9hXujYux6joqKStAtZJJ+9QMrZCySnXiAn94pG79+jiy9jH3/otmcX79cDzZyZ9WII88PXqEbDVlYp+WjVT7w1/bla72UaEo1GsVZWVvLiiy+yYcMGzM3NKS0txdLSkpKSEkaNGsVnn31WF1nFPTL0EWWGnF+y6099z19cUsqC5epFEqCNRzPGDAmgY4d2qrb8q4WknE0nObW6IJ5Oy6j17LA2Pq1b8uzIh/Fu1VIr+W+150Aiy77cCNx4BtXBzoZ3Zz6PU1M7nbxnXbmn5yD37NlDTEwMOTk5NG3alAEDBtC7d29d5hP/QH3/RXE3hpxfsuuPIeSvrUg6NrEgeEhf1Rli1qX8+zq+g50N3q1a0DegA906eOl01ROAX2Li+Hb9H2rPoLZ0c+T/XhmHjZWlTt9blzS6xHpdUFAQQUFBusoihBCNgqWFOWGhT91WJM9l5PLFj7/f07FMTIzwbOmKt2dLvFq1wLtVCxwdmui8KN5seP8Aci9fZd3mP1Vt6Zm5fPjZT8wLHYOp6T2VmnpDo9R9+vRh7NixhISE4OLioutMQgjR4F0vkgsjfuDk6fS7v+Bv188Oq4thS1o94HrPI1G1TaFQ8NwTA0g6fZbT5y+p2hNTzhGx+hf+Mz64Tgu2tmhUIN3c3HjjjTd488036devH2PHjmX48OFYWOju+R0hhGjoLC3MmTu59iJpbPz32WGrFnh5Vp8dOjW1q5fFxsjIiGcf68Pa6H1qZ8W7DyTiYG/LcyMH6DHd/dFoJp1169Zx4sQJ/u///o9Lly4xceJEvL29mTJlCpocRGUAABE3SURBVH/99ZdWA3300Uf079+fBx54gDZt2vDUU0+RmKg+ma5SqWTBggX4+vri5ubG8OHDOXHihFqf/Px8Jk2ahLu7O+7u7kyaNIn8fPXr+cePH2fYsGG4ubnh5+fHokWLGuRsEEKI+qv6THIMg/p0pbmLAwGdvHlmxMP834zn+OqDmbw3ewLjQwbR68F2ODva18vieJ2pqQmzXxpNC1dHtfZf/ojjtz/36SnV/dN4VVRnZ2dCQ0P5888/2bt3LxMnTuSvv/5ixIgRtG/fXmuBdu3axQsvvMCWLVuIiorCxMSEESNGkJeXp+qzbNkywsPDWbRoETExMTg7OzNy5EiuXbvx0OrEiRNJSEggMjKSyMhIEhISeOmll1T7r169ysiRI3FxcSEmJoaFCxfyySef8Omnn2rtswghhCYszM144akhzJ4YzMwXQ3h8YA982zyg90un98PW2pKw0Kewb6L+nOXXP20l7vBJPaW6P/e1bLiPjw9z5szh9ddfp1mzZly8ePHuL9LQ+vXrefbZZ/H396ddu3asWLGCS5cusXfvXqD67DEiIoLp06cTHByMv78/ERERFBQUEBkZCUBSUhLbtm1j6dKlBAQEEBAQwJIlS9iyZQspKSlA9VlxcXExERER+Pv7ExwczLRp01i+fLmcRQohxD/g7GjP3MlPYWF+o8ArlfDp11G3jdytz+55aNGOHTv44Ycf+OWXXygoKKBbt27MmDFDF9kAKCgooKqqCnv76rkS09LSyMrK4uGHH1b1sbS0JCgoiLi4OCZMmEB8fDw2NjYEBgaq+vTo0QNra2vi4uLw8vIiPj6enj17Yml5YwjygAEDmD9/PmlpaXh6eursMwkhREPX6gE3ZrzwBIv+9yNVVdUnHWXlFby/Yh3PjnwYpVJJaVkFZeUVlJdXUFpWTll5OWXl1W1lZeVq/y0tL/+7343+syaF0Mmvtc4+g0YFMjExkR9//JHIyEguXLiAu7s7L730EmPGjKFNmzY6Cwcwd+5cOnToQEBAAABZWVlA9SXfmzk7O5ORUb2sTHZ2No6OjmrX6hUKBU5OTmRnZ6v6NG/e/LZjXN9XW4G8fgZqKAwt760MOb9k1x9Dzm/I2UE9v7UpDO/bibWbd6vaCgsL+WDFj1p5r9NnzmBlUnn3jrW42/OyGhXIXr160aRJE0aMGMGYMWPo2bPnfQe6F/PmzWPv3r1ER0djbKydJWj+qfr+APLNDOGB6Tsx5PySXX8MOb8hZ4ea83t5eWFp3YQfN2t3QCeAq2sznX5fGhXIr776iiFDhmBuXvMq2LoQFhbG+vXr2bRpk9rZnKtr9fx+OTk5PPDAA6r2nJwc1TOaLi4u5ObmqhZ3hup7l5cuXVLrk5OTo/ae17flWU8hhNCeJ4b0IufyFbbHHtHqccvKy7V6vFtpVCCDg4Nr3bdr1y7WrFlDeHi41kK9+uqrbNiwgU2bNt22zJaHhweurq5s376drl27AlBSUkJsbCxvv/02AAEBARQUFBAfH6+6DxkfH09hYaFqOyAggLfeeouSkhLV85zbt2+nWbNmeHh4aO2zCCFEY6dQKJj0/+3de1BUZR8H8O8KiMgqu8pyk4ujMJCEYSAxdhlQwEgkTQoZxkwgGohRd2a5BTNOjROgBtgMMpVNEcglVzCZIWw0tIsS7zQYRbrZS4IyJkgtCopye//gdfPAymJvcBbe72eGcTnnLPN9nuNzfnv27J4nKhRODgpo/n0ZJrNmwczMFOazzXT/zjYzxezZZphtaqp7bD7bFGZmppht9t/HgnUjz5lMf+uvt7S0oKysDBUVFbh8+TLmzp37jxVIlUqFiooKlJSUQCaT6a45WlpaQiqVQiKRICEhAbm5uXBzc4Orqyv27dsHS0tLREREABj5lG1QUBCUSiXy8/MBAEqlEmvXrtWdjkdERCAnJweJiYlQqVT49ddfkZ+fj5SUFKP+nhER0XQ0a9YsrAv0w7pAP7GjTNiEC2R3dzeqqqpQVlaGf/1r5Aufjz76KJRKJTZt2vSPBTp48CCAsWetqampSE9PBwDs2LEDt2/fRnJyMrRaLXx8fFBZWYl58+YJ/k5KSoouW2hoKPbs2aNbb2VlhaqqKqhUKgQGBkImk+H1119HUlLSP9YWIiKavsadzWNoaAgnTpxAWVkZamtr0dfXB3t7e4SFheHgwYOorq7Gk08+OZV56SHMxAv+0wWzi2c655/O2YHpn3+0B55BZmRkQK1Wo7OzE3PmzEFYWBiioqIQEBCAGzducA5IIiKa0R5YIA8cOACJRILg4GAUFhZiwYK/ZqHmNToiIprpHniruS1btkAqleKLL76Ar68vkpOT8f33309lNiIiItE8sEC+++67+OWXX/DBBx9gxYoV+OijjxAcHAw/Pz/s37+fZ5FERDSjjfshnfv9/vvvqKioQHl5OS5cGLkj+8qVKxEbG4vnn3+ec0MSEdGMMuECeb/GxkaUlpaisrISf/zxB+bPn4/W1tbJyEdERCSKv1Ug7+nv70dtbS3Ky8tx6NChfzIXERGRqP6nAklERDRT/a0Jk4mIiGY6FkgiIiI9WCCJiIj0YIGcxnJzcxEYGAgnJycsXboUkZGR+PnnnwXbJCQkQCaTCX6CgoJESiyUlZU1Jtv905sNDw8jKysLHh4esLOzw7p163D+/HkRE//Fy8trTHaZTIaXXnoJgOG2TbVvv/0WmzdvxiOPPAKZTDbmQ3UT6WutVov4+Hg4OzvD2dkZ8fHx0Gq1ombv7+/Hrl27sGrVKjg4OMDd3R1xcXG4fPmy4G+sW7duzP6IiYmZ9OyG8gMTG6N37txBcnIylixZAgcHB2zevBnt7e2iZ9c3BmQyGVQq1UO1z1ixQE5j33zzDWJjY3H8+HEcO3YMpqam2LBhA/7880/BdgEBAdBoNLqfw4cPi5R4LDc3N0G2M2fO6Nbt378fBQUFyMnJwZdffgmFQoGNGzfi5s2bIiYeUVdXJ8h9+vRpSCQSbNiwQbfNeG2bar29vVi2bBmys7NhYWExZv1E+jouLg5NTU1Qq9VQq9VoamrCa6+9Jmr2W7du4YcffoBKpcLp06dRWlqK9vZ2REREYGBgQLBtdHS0YH/k5eVNenZD+e8xNEbT09NRXV2NDz/8EDU1Nbh58yYiIyMxODgoavb7M2s0GpSXlwOAYBwAxn0MGs/kzjZJk6qyslLw+3vvvQdnZ2fU19cjNDRUt9zc3By2trZTHW9CTE1N9WYbHh5GYWEhdu7cqZv6rLCwEG5ublCr1di2bdtURxWwtrYW/F5cXIx58+Zh48aNumUPapsYQkJCEBISAgBITEwUrJtIX2s0Gpw4cQK1tbXw8xuZzy8vLw+hoaGTPoPDeNmtrKxw9OhRwbK8vDz4+/tDo9HA09NTt3zu3Lmi7I/x8t8z3hjt7u5GcXExCgoKEBgYCGBkrHt5eeHUqVNYs2bN5ASH4eyjM9fU1MDV1RVPPfWUYLkxH4PGwzPIGaSnpwdDQ0OQyWSC5WfPnoWrqyt8fHywfft2dHZ2ipRwrEuXLsHDwwPLly9HTEwMLl26BABobW3FtWvXsHr1at22FhYWWLVqFb777juR0uo3PDyM4uJiREZGCl5lP6htxmYifd3Q0ACpVIonnnhCt42/vz8sLS2Nbn/cO+sdPQ6OHDmCJUuWwN/fH5mZmUbxTsQ9443Rc+fOob+/X7B/HB0d4e7ublR939PTg8rKSmzdunXMOmM+Bo2HZ5AzSFpaGry8vHSv8AEgKCgI69evh4uLC9ra2rB7926Eh4fj1KlTMDc3FzEt4OvriwMHDsDNzQ3Xr1/H3r17ERISgvr6ely7dg0AoFAoBM9RKBS4evWqGHEfqK6uDq2trXj55Zd1y8Zr2/0z4xiDifR1R0cHFi5cKLgHs0QigbW1NTo6OqYurAF3795FZmYmnn32WSxatEi3/MUXX4STkxPs7Oxw4cIFvPnmm2hubkZVVZWIaUcYGqMdHR0wMTHBwoULBc9TKBRG1fdqtRp3795FVFSUYLkxH4MMYYGcId544w3U19ejtrYWJiYmuuWbNm3SPfb09IS3tze8vLxw/PhxhIeHixFVJzg4WPC7r68vvL29UVpaipUrV4qU6uEVFRXh8ccfh5eXl27ZeG1LSkqa6oj/FwYGBhAfH4/u7m6UlZUJ1r3yyiu6x56enli8eDHWrFmDc+fOwdvbe4qTChnzGH0YRUVFeO6558ZcfpjO7eNbrDNAeno6jhw5gmPHjmHx4sXjbmtvbw8HBwe0tLRMTbiHIJVK4eHhgZaWFt31itFvxXR2dsLGxkaMeHp1dnaipqZG79tK97u/bcZmIn1tY2ODrq4uDA//deOt4eFhXL9+3Sj2x8DAAGJjY9Hc3IzPPvvM4Fn6ihUrYGJiYpT7Y/QYtbGxweDgILq6ugTbGdNYaGpqQmNjo8FxABj3MWg0FshpLjU1VVccJ/I1gq6uLly9etUoL5j39fXh4sWLsLW1hYuLC2xtbVFXVydYf/bsWcF1MLGVlpbC3Nxc8CpZn/vbZmwm0td+fn7o6elBQ0ODbpuGhgb09vaKvj/6+/uxbds2NDc3o7q6ekJ93NzcjMHBQaPcH6PHqLe3N8zMzAT7p729HRqNRvS+v6eoqAguLi4ICAgwuK0xH4NG41us05hKpUJFRQVKSkogk8l015IsLS0hlUrR09OD7OxshIeHw9bWFm1tbXjrrbegUCgQFhYmcnrorhU5OjrqrtPdunULUVFRkEgkSEhIQG5uLtzc3ODq6op9+/bB0tISERERYkcHMHIG9cknn+CFF16AVCoVrBuvbWLo6enRvWIfGhrClStX0NTUBLlcDicnJ4N97e7ujqCgICiVSuTn5wMAlEol1q5dO6mfYDWU3d7eHlu3bkVjYyPKysogkUh042D+/PmwsLDAb7/9hk8//RQhISFYsGABNBoNMjMzsXz5cvj7+09qdkP55XK5wTFqZWWFLVu2YNeuXVAoFJDL5cjIyICnp+eECtJkZXdycgIw8lWbw4cPY/v27WPmCTb2Y5AhvFn5NDb6U3r3pKamIj09Hbdv30Z0dDSamprQ3d0NW1tbPP3008jIyICjo+MUpx0rJiYGZ86cQVdXF6ytreHr64uMjAx4eHgAGClA2dnZ+Pjjj6HVauHj44N9+/Zh2bJlIicf8dVXXyE8PBwnT56Ej4+PYJ2htk21r7/+GuvXrx+zPCoqCoWFhRPqa61Wi5SUFHz++ecAgNDQUOzZs+eB/w+nIntaWhoee+wxvc8rKChAdHQ0rly5gvj4eJw/fx69vb1YtGgRQkJCkJaWBrlcPqnZgfHz5+bmTmiM3rlzB5mZmVCr1ejr68MzzzyDd955Z9LHsaH/NwBQUlKCHTt24KeffoK9vb1gO2M/BhnCAklERKQHr0ESERHpwQJJRESkBwskERGRHiyQREREerBAEhER6cECSUREpAcLJNH/uUOHDkEmk8HZ2XnMBMgDAwOQyWTIysoSKR2ReFggiQgAcOPGDd1dcoiIBZKI/mv16tV4//33jWoKJSIxsUASEYCRe/sCwN69e0VOQmQcWCCJCABgZ2eHV199FUVFRWhraxM7DpHoWCCJSGfnzp2YM2cOcnJyxI5CJDoWSCLSkcvlSEpKQnl5OS5evCh2HCJRsUASkUBiYiLkcjnefvttsaMQiYoFkogEpFIplEoljh49ih9//FHsOESiYYEkojHi4uLg4OCA3bt3ix2FSDQskEQ0hrm5OVJSUnDy5EmxoxCJhgWSiPSKjo7G0qVLxY5BJBqJVqsdFjsEERGRseEZJBERkR4skERERHqwQBIREenBAklERKQHCyQREZEeLJBERER6sEASERHpwQJJRESkx38ArLLb+S1LtpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Don't change this cell, just run it!\n",
    "sample_n_errors = make_array()\n",
    "for i in np.arange(10, 200, 10):\n",
    "    sample_n_errors = np.append(sample_n_errors, np.average([abs(np.std(salaries.sample(i).column(\"salary\"))-pop_sd)\n",
    "                                                             for d in np.arange(100)]))\n",
    "Table().with_column(\"Average absolute error in SD\", sample_n_errors, \"N\", np.arange(10, 200, 10)).plot(\"N\", \"Average absolute error in SD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should notice that the distribution of means gets spiker, and that the distribution of the sample increasingly looks like the distribution of the population as we get to larger sample sizes. \n",
    "\n",
    "Is there a relationship between the sample size and absolute error in standard deviation? Identify this relationship – if you're having trouble, take a look at this [section](https://www.inferentialthinking.com/chapters/14/5/variability-of-the-sample-mean.html) in our textbook about the variability of sample means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You're finished with lab 1! In order to successfully submit your assignment, follow these steps...\n",
    "- **IMPORTANT** Before you do anything, **Save and Checkpoint** from the `File` menu. Please do this first before running the cell below,\n",
    "- **run all the tests and verify that they all pass** (the next cell has a shortcut for that), \n",
    "- **Review the notebook one last time** If you make any changes, please **Save and Checkpoint** again.\n",
    "- **Hit the Submit button** Your submission will be saved and grade will be posted when it's finished running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  400\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  625\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample size:  100\n",
      "Population mean: 75463.91814023031\n",
      "Average of sample means:  nan\n",
      "Population SD: 51697.0349864653\n",
      "SD of sample means: nan\n",
      "Sample SD:  58653.75152428679\n",
      "Sample size:  10\n",
      "Population mean: 85992.008\n",
      "Average of sample means:  nan\n",
      "Population SD: 58653.75152428679\n",
      "SD of sample means: nan\n",
      "Sample SD:  47885.47453604265\n",
      "Sample size:  200\n",
      "Population mean: 79265.51365000001\n",
      "Average of sample means:  nan\n",
      "Population SD: 47885.47453604265\n",
      "SD of sample means: nan\n",
      "Sample SD:  51307.78924819284\n",
      "Sample size:  1000\n",
      "Population mean: 77451.17811\n",
      "Average of sample means:  nan\n",
      "Population SD: 51307.78924819284\n",
      "SD of sample means: nan\n",
      "['tests/q1_2.py', 'tests/q1_4.py', 'tests/q1_5.py', 'tests/q2_3.py', 'tests/q2_4.py']\n",
      "Question 1:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb62ebbd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 2:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb62f7b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 3:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>All tests passed!</p>\n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb62e55208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 4:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>0 of 1 tests passed</p>\n",
       "        \n",
       "        \n",
       "        <p> <strong>Tests failed: </strong>\n",
       "            <ul>\n",
       "            \n",
       "                <li> \n",
       "    <p><strong style='color: red;'>tests/q2_3.py</strong></p>\n",
       "\n",
       "    <p><strong>Test code:</strong><pre><div class=\"highlight\" style=\"background: #f8f8f8\"><pre style=\"line-height: 125%\"><span></span><span style=\"color: #000080; font-weight: bold\">&gt;&gt;&gt; </span>bootstrap_sampled_SD <span style=\"color: #666666\">==</span> <span style=\"color: #666666\">3</span>\n",
       "<span style=\"color: #888888\">True</span>\n",
       "</pre></div>\n",
       "</pre></p>\n",
       "\n",
       "    <p><strong>Test result:</strong><pre>Trying:\n",
       "    bootstrap_sampled_SD == 3\n",
       "Expecting:\n",
       "    True\n",
       "**********************************************************************\n",
       "Line 2, in tests/q2_3.py 0\n",
       "Failed example:\n",
       "    bootstrap_sampled_SD == 3\n",
       "Exception raised:\n",
       "    Traceback (most recent call last):\n",
       "      File \"/srv/conda/envs/notebook/lib/python3.7/doctest.py\", line 1329, in __run\n",
       "        compileflags, 1), test.globs)\n",
       "      File \"<doctest tests/q2_3.py 0[0]>\", line 1, in <module>\n",
       "        bootstrap_sampled_SD == 3\n",
       "    NameError: name 'bootstrap_sampled_SD' is not defined\n",
       "</pre></p>\n",
       "     </li>\n",
       "            \n",
       "            </ul>\n",
       "        \n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb63b36630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 5:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    \n",
       "    \n",
       "        <p>0 of 1 tests passed</p>\n",
       "        \n",
       "        \n",
       "        <p> <strong>Tests failed: </strong>\n",
       "            <ul>\n",
       "            \n",
       "                <li> \n",
       "    <p><strong style='color: red;'>tests/q2_4.py</strong></p>\n",
       "\n",
       "    <p><strong>Test code:</strong><pre><div class=\"highlight\" style=\"background: #f8f8f8\"><pre style=\"line-height: 125%\"><span></span><span style=\"color: #000080; font-weight: bold\">&gt;&gt;&gt; </span>pop_vs_sample <span style=\"color: #666666\">==</span> <span style=\"color: #666666\">3</span>\n",
       "<span style=\"color: #888888\">True</span>\n",
       "</pre></div>\n",
       "</pre></p>\n",
       "\n",
       "    <p><strong>Test result:</strong><pre>Trying:\n",
       "    pop_vs_sample == 3\n",
       "Expecting:\n",
       "    True\n",
       "**********************************************************************\n",
       "Line 2, in tests/q2_4.py 0\n",
       "Failed example:\n",
       "    pop_vs_sample == 3\n",
       "Expected:\n",
       "    True\n",
       "Got:\n",
       "    False\n",
       "</pre></p>\n",
       "     </li>\n",
       "            \n",
       "            </ul>\n",
       "        \n",
       "    \n",
       "    "
      ],
      "text/plain": [
       "<gofer.ok.OKTestsResult at 0x7fbb63b36358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For your convenience, you can run this cell to run all the tests at once!\n",
    "import glob\n",
    "from gofer.ok import grade_notebook\n",
    "if not globals().get('__GOFER_GRADER__', False):\n",
    "    display(grade_notebook('lab01.ipynb', sorted(glob.glob('tests/q*.py'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "lab": "lab01",
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