{
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   "source": [
    "# Practicum 2 - March 28, 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Due Date: Friday, March 28th no later than 10:50 a.m.\n",
    "- Submission Instructions: Upload your solution, entitled **YourFirstName-YourLastName-Practicum2.ipynb** to the \n",
    "BrightSpace Practicum 2 Dropbox.\n",
    "- Note: For all questions, determine the answer using python constructs (as opposed to eyeballing the csv file)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "St. Patrick's Day, a day of revelry and celebration, took place earlier this month.\n",
    "For this practicum, you are going to explore a New York City file that contains\n",
    "information about noise complaints that came from a club, bar or restaurant.\n",
    "Download **noise_complaints.csv** and place it in the same directory as your Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place the csv file in the same directory as this notebook\n",
    "noise = Table().read_table(\"noise_complaints.csv\")\n",
    "noise.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this practicum, the only two columns of interest are the borough where\n",
    "# the bar is located and the number of complaints that were received from that bar.\n",
    "noise = noise.select(\"Borough\", \"num_calls\").relabel(\"num_calls\", \"Complaints\")\n",
    "noise.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1 - 10 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the total number of calls that were made to complain about noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2 - 20 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display a table that shows the probability (as a percentage) that a noise complaint call comes from each\n",
    "of the five individual boroughs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3 - 10 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the table that you created for Question 2, calculate and display the probability\n",
    "that a noise complaint calls from either Manhattan or Brooklyn.\n",
    "Your output format should look something like this\n",
    "(although the exact probability might be different!):\n",
    "*The probability that a call is made from Manhattan or Brooklyn is 56.78%*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4 - 20 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore the information above.  Assume that the true probability distribution of 1000 noise\n",
    "complaint calls coming from Bronx/Brooklyn/Manhattan/Queens/Staten Island is [.10, .30, .40, .15, .05].\n",
    "Furthermore, assume that the probability distribution of 1000 noise complaint calls coming from five\n",
    "unknown places is [.11, .3, .38, .13, .08].  Use python to calculate and print the total variation\n",
    "distance between these two distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_distribution = make_array(.1, .3, .4, .15, .05)\n",
    "sample_distribution = make_array(.11, .3, .38, .13, .08)\n",
    "# Place answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5 - 30 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the style of Chapter 11, obtain 5,000 total variation distances by sampling the\n",
    "true distribution from Question 4 1,000 times to produce each individual total variation distance.\n",
    "Plot the distribution of the total variation distances in a histogram that uses the default bins.\n",
    "Place a red dot on the histogram at (tvd-you-calculated-for-question-4, 0) using plots.scatter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6 - 10 points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate and display the p-value that the distribution of unknown city calls from Question 4\n",
    "came from the Bronx/Brooklyn/Manhattan/Queens/Staten Island distribution of Question 4.  The format\n",
    "of your output should be similar to this: *The p-value is 8.43%*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Place answer here."
   ]
  }
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