This is one of the case studies from Chapter 1 of the textbook (pp. 12-16). You may load it from the URL linked above or the resampledata package.
library(resampledata)
library(tidyverse)
glimpse(MobileAds)Rows: 655
Columns: 40
$ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
$ m.impr_post <int> 255, 18, 583, 11498, 1554, 1886, 4859, 1015, 21, 534, 1…
$ m.impr_pre <int> 155, 4900, 6858, 3439, 1549, 1159, 891, 347, 72, 473, 1…
$ m.click_post <int> 3, 1, 2, 151, 28, 62, 57, 19, 0, 23, 2, 2, 1, 1, 33, 3,…
$ m.click_pre <int> 3, 41, 46, 36, 34, 35, 7, 11, 7, 31, 17, 10, 5, 13, 12,…
$ m.cost_post <dbl> 148.53, 4.50, 8.17, 500.93, 90.37, 1166.75, 228.15, 43.…
$ m.cost_pre <dbl> 94.38, 395.32, 162.05, 105.69, 86.97, 699.00, 17.91, 23…
$ m.conv_post <int> 0, 0, 0, 13, 1, 40, 0, 2, 0, 3, 0, 0, 0, 0, 3, 0, 1, 3,…
$ m.conv_pre <int> 4, 2, 2, 6, 6, 28, 4, 6, 5, 10, 8, 4, 3, 4, 5, 3, 6, 5,…
$ m.value_post <dbl> 0.00, 0.00, 0.00, 405.40, 0.00, 1097.60, 0.00, 9.50, 0.…
$ m.value_pre <dbl> 193.00, 231.57, 46.30, 395.90, 141.77, 1240.41, 212.55,…
$ m.cpm_pre <dbl> 0.608903226, 0.080677551, 0.023629338, 0.030732771, 0.0…
$ m.cpm_post <dbl> 0.582470588, 0.250000000, 0.014013722, 0.043566707, 0.0…
$ m.cpc_pre <dbl> 31.4600000, 9.6419512, 3.5228261, 2.9358333, 2.5579412,…
$ m.cpc_post <dbl> 49.510000, 4.500000, 4.085000, 3.317417, 3.227500, 18.8…
$ m.cpa_pre <dbl> 23.595000, 197.660000, 81.025000, 17.615000, 14.495000,…
$ m.cpa_post <dbl> 0.00000, 0.00000, 0.00000, 38.53308, 90.37000, 29.16875…
$ m.cpr_pre <dbl> 0.48901554, 1.70712959, 3.50000000, 0.26696135, 0.61345…
$ m.cpr_post <dbl> 0.000000, 0.000000, 0.000000, 1.235644, 0.000000, 1.063…
$ mult.change <dbl> 0.00, -0.21, -0.68, 0.38, 0.50, -0.30, 1.00, 0.12, -0.3…
$ d.impr_post <int> 1466, 64535, 119831, 207, 5912, 8094, 978, 144, 148, 65…
$ d.impr_pre <int> 1430, 54535, 86900, 1097, 5281, 6021, 1010, 311, 152, 6…
$ d.click_post <int> 22, 348, 1001, 1, 52, 185, 6, 0, 2, 38, 18, 2, 13, 0, 0…
$ d.click_pre <int> 15, 297, 685, 4, 37, 95, 8, 2, 1, 32, 41, 2, 10, 0, 0, …
$ d.cost_post <dbl> 703.39, 5142.91, 5042.99, 2.54, 144.15, 3195.21, 9.25, …
$ d.cost_pre <dbl> 481.63, 4084.68, 3000.78, 7.08, 118.23, 1404.71, 15.64,…
$ d.conv_post <int> 9, 51, 117, 0, 3, 112, 1, 0, 1, 14, 8, 4, 0, 0, 0, 26, …
$ d.conv_pre <int> 2, 60, 109, 0, 7, 63, 0, 0, 1, 10, 20, 2, 0, 0, 0, 30, …
$ d.value_post <dbl> 570.00, 3589.35, 4163.26, 0.00, 74.30, 3936.03, 0.00, 0…
$ d.value_pre <dbl> 0.00, 3904.85, 3419.64, 0.00, 243.72, 3308.56, 0.00, 0.…
$ d.cpm_pre <dbl> 0.336804196, 0.074900156, 0.034531415, 0.006453965, 0.0…
$ d.cpm_post <dbl> 0.479802183, 0.079691795, 0.042084185, 0.012270531, 0.0…
$ d.cpc_pre <dbl> 32.1086667, 13.7531313, 4.3807007, 1.7700000, 3.1954054…
$ d.cpc_post <dbl> 31.9722727, 14.7784770, 5.0379520, 2.5400000, 2.7721154…
$ d.cpa_pre <dbl> 240.815000, 68.078000, 27.530092, 0.000000, 16.890000, …
$ d.cpa_post <dbl> 78.154444, 100.841373, 43.102479, 0.000000, 48.050000, …
$ d.cpr_pre <dbl> 0.0000000, 1.0460530, 0.8775134, 0.0000000, 0.4851059, …
$ d.cpr_post <dbl> 1.2340175, 1.4328249, 1.2113080, 0.0000000, 1.9401077, …
$ error.cpr_pre <dbl> 0.48901554, 0.66107661, 2.62248658, 0.26696135, 0.12835…
$ error.cpr_post <dbl> 1.23401754, 1.43282489, 1.21130797, 1.23564381, 1.94010…