{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20a03edd",
   "metadata": {},
   "source": [
    "# Rock Fries Your Brains\n",
    "\n",
    "First we load the needed packages for this exercise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0ef0862",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35dcfbd",
   "metadata": {},
   "source": [
    "# The data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb8ac4c",
   "metadata": {},
   "source": [
    "The data for this experiment came from a student named David Merrell, who collected them as part of a high school science project. Merrell raised three groups of mice.\n",
    "\n",
    "- Group 1 was raised in the absence of music\n",
    "- Group 2 heard 10-12 hours of Mozart every day\n",
    "- Group 3 heard 10-12 hours of rock music (performed by the group Anthrax) every day\n",
    "\n",
    "They were originally taught to navigate a maze for food, and then were placed back in the maze each week and the time to run the maze was collected. (Actually there were three trials each week, and the data file gives those three times). Included are also the mean and the median running time per week. Finally, the weights of each mouse when they were received from the breeding lab and at each of the four weeks of testing (wt0 to wt4) are included.\n",
    "\n",
    "In the data, missing values are coded as 999 (but this is handled by the command below)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31213c02",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289144a6",
   "metadata": {},
   "source": [
    "Load data from URL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b5e47318",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>group</th>\n",
       "      <th>wk1r1</th>\n",
       "      <th>wk1r2</th>\n",
       "      <th>wk1r3</th>\n",
       "      <th>wk2r1</th>\n",
       "      <th>wk2r2</th>\n",
       "      <th>wk2r3</th>\n",
       "      <th>wk3r1</th>\n",
       "      <th>wk3r2</th>\n",
       "      <th>...</th>\n",
       "      <th>week4</th>\n",
       "      <th>wt0</th>\n",
       "      <th>wt1</th>\n",
       "      <th>wt2</th>\n",
       "      <th>wt3</th>\n",
       "      <th>wt4</th>\n",
       "      <th>median1</th>\n",
       "      <th>median2</th>\n",
       "      <th>median3</th>\n",
       "      <th>median4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1068</td>\n",
       "      <td>534</td>\n",
       "      <td>352</td>\n",
       "      <td>982</td>\n",
       "      <td>170</td>\n",
       "      <td>492</td>\n",
       "      <td>492.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>...</td>\n",
       "      <td>444.666667</td>\n",
       "      <td>16.1</td>\n",
       "      <td>16.8</td>\n",
       "      <td>24.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>29.3</td>\n",
       "      <td>534</td>\n",
       "      <td>492</td>\n",
       "      <td>492.0</td>\n",
       "      <td>592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1017</td>\n",
       "      <td>568</td>\n",
       "      <td>529</td>\n",
       "      <td>715</td>\n",
       "      <td>608</td>\n",
       "      <td>89</td>\n",
       "      <td>383.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>219.666667</td>\n",
       "      <td>18.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>24.7</td>\n",
       "      <td>29.5</td>\n",
       "      <td>30.3</td>\n",
       "      <td>568</td>\n",
       "      <td>608</td>\n",
       "      <td>166.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1156</td>\n",
       "      <td>720</td>\n",
       "      <td>60</td>\n",
       "      <td>620</td>\n",
       "      <td>572</td>\n",
       "      <td>239</td>\n",
       "      <td>795.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>...</td>\n",
       "      <td>347.333333</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>29.3</td>\n",
       "      <td>720</td>\n",
       "      <td>572</td>\n",
       "      <td>136.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>805</td>\n",
       "      <td>205</td>\n",
       "      <td>497</td>\n",
       "      <td>209</td>\n",
       "      <td>396</td>\n",
       "      <td>600</td>\n",
       "      <td>106.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>327.333333</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>497</td>\n",
       "      <td>396</td>\n",
       "      <td>419.0</td>\n",
       "      <td>369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>456</td>\n",
       "      <td>245</td>\n",
       "      <td>1160</td>\n",
       "      <td>401</td>\n",
       "      <td>533</td>\n",
       "      <td>828</td>\n",
       "      <td>589.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>...</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>27.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>456</td>\n",
       "      <td>533</td>\n",
       "      <td>422.0</td>\n",
       "      <td>442.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject  group  wk1r1  wk1r2  wk1r3  wk2r1  wk2r2  wk2r3  wk3r1  wk3r2  \\\n",
       "0        1      1   1068    534    352    982    170    492  492.0  926.0   \n",
       "1        2      1   1017    568    529    715    608     89  383.0   57.0   \n",
       "2        3      1   1156    720     60    620    572    239  795.0   83.0   \n",
       "3        4      1    805    205    497    209    396    600  106.0  419.0   \n",
       "4        5      1    456    245   1160    401    533    828  589.0  422.0   \n",
       "\n",
       "   ...       week4   wt0   wt1   wt2   wt3   wt4  median1  median2  median3  \\\n",
       "0  ...  444.666667  16.1  16.8  24.1  27.4  29.3      534      492    492.0   \n",
       "1  ...  219.666667  18.5  14.5  24.7  29.5  30.3      568      608    166.0   \n",
       "2  ...  347.333333  17.6  16.0  25.0   3.1  29.3      720      572    136.0   \n",
       "3  ...  327.333333  17.7  17.3  24.7  27.0  26.8      497      396    419.0   \n",
       "4  ...  431.000000  17.0  16.5  24.5  27.2  27.8      456      533    422.0   \n",
       "\n",
       "   median4  \n",
       "0    592.0  \n",
       "1    251.0  \n",
       "2    230.0  \n",
       "3    369.0  \n",
       "4    442.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musik = pd.read_csv(\"https://asta.math.aau.dk/datasets?file=musik.txt\", sep=\"\\t\").replace(999, np.nan)\n",
    "musik.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8520f3",
   "metadata": {},
   "source": [
    "## Convert group variable to a categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807cc3c2",
   "metadata": {},
   "source": [
    "We now convert the group variable to a categorical variable with proper names (instead of 1, 2, 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a230df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>926.0</td>\n",
       "      <td>...</td>\n",
       "      <td>444.666667</td>\n",
       "      <td>16.1</td>\n",
       "      <td>16.8</td>\n",
       "      <td>24.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>29.3</td>\n",
       "      <td>534</td>\n",
       "      <td>492</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Control</td>\n",
       "      <td>1017</td>\n",
       "      <td>568</td>\n",
       "      <td>529</td>\n",
       "      <td>715</td>\n",
       "      <td>608</td>\n",
       "      <td>89</td>\n",
       "      <td>383.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>219.666667</td>\n",
       "      <td>18.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>24.7</td>\n",
       "      <td>29.5</td>\n",
       "      <td>30.3</td>\n",
       "      <td>568</td>\n",
       "      <td>608</td>\n",
       "      <td>166.0</td>\n",
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       "    <tr>\n",
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       "      <td>60</td>\n",
       "      <td>620</td>\n",
       "      <td>572</td>\n",
       "      <td>239</td>\n",
       "      <td>795.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>...</td>\n",
       "      <td>347.333333</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>29.3</td>\n",
       "      <td>720</td>\n",
       "      <td>572</td>\n",
       "      <td>136.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Control</td>\n",
       "      <td>805</td>\n",
       "      <td>205</td>\n",
       "      <td>497</td>\n",
       "      <td>209</td>\n",
       "      <td>396</td>\n",
       "      <td>600</td>\n",
       "      <td>106.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>...</td>\n",
       "      <td>327.333333</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>497</td>\n",
       "      <td>396</td>\n",
       "      <td>419.0</td>\n",
       "      <td>369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Control</td>\n",
       "      <td>456</td>\n",
       "      <td>245</td>\n",
       "      <td>1160</td>\n",
       "      <td>401</td>\n",
       "      <td>533</td>\n",
       "      <td>828</td>\n",
       "      <td>589.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>...</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>27.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>456</td>\n",
       "      <td>533</td>\n",
       "      <td>422.0</td>\n",
       "      <td>442.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject    group  wk1r1  wk1r2  wk1r3  wk2r1  wk2r2  wk2r3  wk3r1  wk3r2  \\\n",
       "0        1  Control   1068    534    352    982    170    492  492.0  926.0   \n",
       "1        2  Control   1017    568    529    715    608     89  383.0   57.0   \n",
       "2        3  Control   1156    720     60    620    572    239  795.0   83.0   \n",
       "3        4  Control    805    205    497    209    396    600  106.0  419.0   \n",
       "4        5  Control    456    245   1160    401    533    828  589.0  422.0   \n",
       "\n",
       "   ...       week4   wt0   wt1   wt2   wt3   wt4  median1  median2  median3  \\\n",
       "0  ...  444.666667  16.1  16.8  24.1  27.4  29.3      534      492    492.0   \n",
       "1  ...  219.666667  18.5  14.5  24.7  29.5  30.3      568      608    166.0   \n",
       "2  ...  347.333333  17.6  16.0  25.0   3.1  29.3      720      572    136.0   \n",
       "3  ...  327.333333  17.7  17.3  24.7  27.0  26.8      497      396    419.0   \n",
       "4  ...  431.000000  17.0  16.5  24.5  27.2  27.8      456      533    422.0   \n",
       "\n",
       "   median4  \n",
       "0    592.0  \n",
       "1    251.0  \n",
       "2    230.0  \n",
       "3    369.0  \n",
       "4    442.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musik[\"group\"] = pd.Categorical(musik[\"group\"].map({1: \"Control\", 2: \"Mozart\", 3: \"Rock\"}))\n",
    "musik.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf5faf1",
   "metadata": {},
   "source": [
    "# Exploratory analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2500d068",
   "metadata": {},
   "source": [
    "## Numerical summaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc0e976",
   "metadata": {},
   "source": [
    "Let us see a summary of the `week1` means for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c67710bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4830/850470079.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  musik.groupby(\"group\")[\"week1\"].describe()\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
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       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>group</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>24.0</td>\n",
       "      <td>597.222222</td>\n",
       "      <td>77.315569</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>560.750000</td>\n",
       "      <td>622.166667</td>\n",
       "      <td>650.833333</td>\n",
       "      <td>704.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mozart</th>\n",
       "      <td>24.0</td>\n",
       "      <td>618.013889</td>\n",
       "      <td>96.187140</td>\n",
       "      <td>470.666667</td>\n",
       "      <td>555.833333</td>\n",
       "      <td>612.833333</td>\n",
       "      <td>662.583333</td>\n",
       "      <td>930.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock</th>\n",
       "      <td>24.0</td>\n",
       "      <td>569.638889</td>\n",
       "      <td>110.058567</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>521.083333</td>\n",
       "      <td>576.666667</td>\n",
       "      <td>619.333333</td>\n",
       "      <td>838.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std         min         25%         50%  \\\n",
       "group                                                                        \n",
       "Control   24.0  597.222222   77.315569  383.000000  560.750000  622.166667   \n",
       "Mozart    24.0  618.013889   96.187140  470.666667  555.833333  612.833333   \n",
       "Rock      24.0  569.638889  110.058567  289.333333  521.083333  576.666667   \n",
       "\n",
       "                75%         max  \n",
       "group                            \n",
       "Control  650.833333  704.666667  \n",
       "Mozart   662.583333  930.333333  \n",
       "Rock     619.333333  838.666667  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musik.groupby(\"group\")[\"week1\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95685e52",
   "metadata": {},
   "source": [
    "And the corresponding summary for `week4` means for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ccaa3450",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete this line and add the correct code yourself"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1eb897",
   "metadata": {},
   "source": [
    "## Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394a46cc",
   "metadata": {},
   "source": [
    "Let us try to illustrate all four weeks' means using boxplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91042f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x=\"group\", y=\"week1\", data=musik)\n",
    "plt.ylabel(\"Week1 mean\")\n",
    "plt.show()\n",
    "sns.boxplot(x=\"group\", y=\"week2\", data=musik)\n",
    "plt.ylabel(\"Week2 mean\")\n",
    "plt.show()\n",
    "sns.boxplot(x=\"group\", y=\"week3\", data=musik)\n",
    "plt.ylabel(\"Week3 mean\")\n",
    "plt.show()\n",
    "sns.boxplot(x=\"group\", y=\"week4\", data=musik)\n",
    "plt.ylabel(\"Week4 mean\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51f5cc5",
   "metadata": {},
   "source": [
    "If we want to arrange these in a joint plot we can use plt.subplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7a187fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
    "sns.boxplot(x=\"group\", y=\"week1\", data=musik, ax=axes[0, 0])\n",
    "axes[0, 0].set_ylabel(\"Week1 mean\")\n",
    "sns.boxplot(x=\"group\", y=\"week2\", data=musik, ax=axes[0, 1])\n",
    "axes[0, 1].set_ylabel(\"Week2 mean\")\n",
    "sns.boxplot(x=\"group\", y=\"week3\", data=musik, ax=axes[1, 0])\n",
    "axes[1, 0].set_ylabel(\"Week3 mean\")\n",
    "sns.boxplot(x=\"group\", y=\"week4\", data=musik, ax=axes[1, 1])\n",
    "axes[1, 1].set_ylabel(\"Week4 mean\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78dfe43f",
   "metadata": {},
   "source": [
    "## Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e87327",
   "metadata": {},
   "source": [
    "We can also compare the `week1` performance with `week4` performance for each subject by using one or more scatter plots using `gf_point` function. A basic scatter plot doesn't tells us much (other than some mice have become much slower in week 4):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b990946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"week1\", y=\"week4\", data=musik)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827bcdf6",
   "metadata": {},
   "source": [
    "Try to add a code chunk similar to the one above where you color the points depending on which group they belong to (hint: hue=... can be used for this). Can you predict what this plot will look like?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ff5a75",
   "metadata": {},
   "source": [
    "Does it seem like there is any difference between the groups?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56fbdba",
   "metadata": {},
   "source": [
    "As a further source of inspiration you can have a look at the [data source](https://www.uvm.edu/~statdhtx/StatPages/More_Stuff/Anthrax.html), where you also can find some suggestions to the data analysis."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}