exercises/exercises_13_descr_stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "729659cf-d0ff-4543-a354-d11c51e60f38",
   "metadata": {},
   "source": [
    "# Descriptive statistics exercises\n",
    "\n",
    "This notebook contains all solutions of the exercises from [Section 1.3 Descriptive Statistics](https://minireference.com/static/excerpts/noBSstats/descriptive_statistics.pdf) in the **No Bullshit Guide to Statistics**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6389227-654c-47dd-b60e-d5e9e4a8fad9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Notebooks setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "068cba77-95ea-4024-aced-9895802a4d43",
   "metadata": {
    "id": "VFzPCGo6I1NJ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93fa5c9c-e955-4ff4-a9eb-67035c96d510",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas setup\n",
    "pd.set_option(\"display.precision\", 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ada6f56c-bc0e-421f-b3d9-698b3d6a794a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figures setup\n",
    "sns.set_theme(\n",
    "    context=\"paper\",\n",
    "    style=\"whitegrid\",\n",
    "    palette=\"colorblind\",\n",
    "    rc={'figure.figsize': (7,4)},\n",
    ")\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd818d6d-51df-47b2-9671-af329f33231e",
   "metadata": {},
   "source": [
    "### Load the `sdudents` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efedd9cc-ee8c-4ef0-b882-87024f015785",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "if os.path.exists(\"../datasets/students.csv\"):\n",
    "    data_file = open(\"../datasets/students.csv\", \"r\")\n",
    "else:\n",
    "    import io\n",
    "    data_file = io.StringIO(\"\"\"\n",
    "student_ID,background,curriculum,effort,score\n",
    "1,arts,debate,10.96,75\n",
    "2,science,lecture,8.69,75\n",
    "3,arts,debate,8.6,67\n",
    "4,arts,lecture,7.92,70.3\n",
    "5,science,debate,9.9,76.1\n",
    "6,business,debate,10.8,79.8\n",
    "7,science,lecture,7.81,72.7\n",
    "8,business,lecture,9.13,75.4\n",
    "9,business,lecture,5.21,57\n",
    "10,science,lecture,7.71,69\n",
    "11,business,debate,9.82,70.4\n",
    "12,arts,debate,11.53,96.2\n",
    "13,science,debate,7.1,62.9\n",
    "14,science,lecture,6.39,57.6\n",
    "15,arts,debate,12,84.3\n",
    "\"\"\")\n",
    "\n",
    "students = pd.read_csv(data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6b9d11-26f4-410a-a04c-77dab408c0f7",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let's look at the `effort` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f78b2e6-e5b4-4b79-91b0-643210c5a16d",
   "metadata": {},
   "outputs": [],
   "source": [
    "efforts = students[\"effort\"]\n",
    "# efforts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5bd612e-6536-45b3-b777-b4f2ccacc1f3",
   "metadata": {},
   "source": [
    "### E1.1\n",
    "Compute the Mean, Min, Max, and Range of the effort variable in the students dataset.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5779df42-cd6f-4b64-91e1-56a7d910642a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.904666666666666"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efforts = students[\"effort\"]\n",
    "\n",
    "# Mean(efforts)\n",
    "efforts.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d348c6e-9406-4db2-aaca-ec59340475ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Min(efforts)\n",
    "efforts.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34ed13be-94c2-4c15-8bc0-a8ab97bbb577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max(efforts)\n",
    "efforts.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77f2725d-56b4-4ac1-a162-147aab804465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.79"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range(efforts)\n",
    "efforts.max() - efforts.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55d71fe8-8ac1-4345-b652-f4d4d247a57d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "52f43300-489c-4058-a5b0-4ca4b56f51d6",
   "metadata": {},
   "source": [
    "### E1.14\n",
    "Find Q1, Med, and Q3 of the effort variable in the students dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "286cefb6-740c-46cd-9419-9317adb58c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.76, 8.69, 10.350000000000001)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efforts.quantile(q=0.25), efforts.median(), efforts.quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ba1a68-5d8c-4099-b9ba-15b875b4268c",
   "metadata": {},
   "source": [
    "### E1.15\n",
    "Make a one-way frequency table for the effort variable. Use $(5,7]$, $(7,9]$, $(9,11]$, $(11,13]$ as the bin intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e749417-8159-4cc8-af59-0f026dfe44f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.999, 7.0]    2\n",
       "(7.0, 9.0]      6\n",
       "(9.0, 11.0]     5\n",
       "(11.0, 13.0]    2\n",
       "Name: effort, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [5, 7, 9, 11, 13]\n",
    "efforts.value_counts(bins=bins).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32d2f52f-07d4-4ac8-9888-72530a42eb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ALT. to get [5,7), [7,9), [9,11), [11,13) instead, use\n",
    "# bins2 = pd.IntervalIndex.from_breaks(bins, closed=\"left\")\n",
    "# efforts.value_counts(bins=bins2).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f2fe09-08af-41e9-9626-6c30bd917d01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c4b14dd-99f2-42ef-9369-88b643dfbecc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434c8ad1-bcb8-4bec-8164-933b5820e345",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "95b7848f-b3b9-4b39-a7b1-4d9f83b941c4",
   "metadata": {},
   "source": [
    "### E1.16\n",
    "Draw a scatter plot for the following dataset of (x,y) pairs:\n",
    "\\{ (2,2), (3,3), (4,3), (5,5), (6,4) \\}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f1aefe4-1ca0-4d50-ad8d-31d56133d704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 608
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame([(2,2), (3,3), (4,3), (5,5),\n",
    "                   (6,4), (5,4), (7,6), (8,5)],\n",
    "                  columns=[\"x\", \"y\"])\n",
    "sns.scatterplot(x=\"x\", y=\"y\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58a48bcd-bbe0-49e4-81cf-056e013eee63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e64a64ec-056f-4dc4-a086-21f4f02e7973",
   "metadata": {},
   "source": [
    "### E1.17\n",
    "\n",
    "TODO: add simple exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "743c9146-95d1-442a-b8b9-97208cfa2720",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f61deb1-03b3-413f-a051-d188b42b6062",
   "metadata": {},
   "source": [
    "### E1.18\n",
    "Make a bar chart displaying the frequencies of the curriculum variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6946b057-9fd9-4351-a71a-1302f7d9e818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='curriculum', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 598
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=students, x=\"curriculum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62dc98f8-fa32-4fa3-bb74-b90fb7e9f432",
   "metadata": {},
   "source": [
    "### E1.19\n",
    "Compute frequencies and relative frequencies for the curriculum variable. Display the results in a one-way table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bd73bd2-4713-4671-87e9-83df39287a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debate     8\n",
       "lecture    7\n",
       "Name: curriculum, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[\"curriculum\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d295e8d-3fc8-4ff1-973f-479f3377b75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debate     0.53\n",
       "lecture    0.47\n",
       "Name: curriculum, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "students[\"curriculum\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed23238-0fc0-4bc9-a136-851377264d41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df087103-2b9d-45dc-90c5-64558a616a16",
   "metadata": {},
   "source": [
    "### E1.20\n",
    "What is the mode for curriculum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "769679ec-21a0-45d3-9a79-f750cb4626d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode is debate with frequency 8\n"
     ]
    }
   ],
   "source": [
    "mode = students[\"curriculum\"].describe()['top']\n",
    "mode_freq = students[\"curriculum\"].describe()['freq']\n",
    "\n",
    "print(\"The mode is\", mode, \"with frequency\", mode_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a079d5da-4b46-4bc6-9536-b1adda5a9739",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1de8e16-810a-4910-a1ff-f279d80a39db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a97d7af3-fae4-4771-9331-75427dd7ddaa",
   "metadata": {},
   "source": [
    "### E1.21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "538a979f-2787-4eff-81fc-32ef9f7bcbd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
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    "### E1.22"
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