# Errors we made and found

## Info

There are a few errors we made, unfortunately. Some are typos, swapped figure lables, some are imprecise language. It may be that we found an important error in code and corrected it, so the code does not exactly reproduce tables and graphs in the book.

Fortunately, we found some. As we, and our kind readers, carry on finding more errors, we are adding them here. You shall review them before reading / teaching.

### Status

Latest update: 12 July 2021

### Feedback

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If you are teaching the book or learning from it, PDF could be useful

## List of errors

### Part I

ID Date added Error Type Chapter Page Problematic Corrected

### Part II

ID Date added Error Type Chapter Page Problematic Corrected
10-01 2021-03-06 Typo Ch10 B1 p.293 Table 10.6 N=217 N=207
10-02 2021-03-08 Missing Ch10 B1 p.285 Graph 10.2 Note, missing info Male: blue, female: green

### Part III

ID Date added Error Type Chapter Page Problematic Corrected
14-01 2021-01-06 Imprecise sentence Ch14 B1 p.401 “The number of apartments or rooms is left as it is, and treated as continuous..” “The number of guests to accommodate or rooms is left as it is, and treated as continuous.”
14-02 2021-02-07 Typo Ch14 p.415 “two variables, $$x_i x_j$$ and $$x_i^2 x_j$$ and $$x_i^2 x_j$$” “two variables, $$x_i x_j$$ and $$x_i^2 x_j$$ and $$x_i x_j^2$$”
14-03 2021-02-13 Imprecise sentence Ch14 B1-B4 The currency is USD for price Actually, local currency (GBP) is used. Recently clarified
15-01 2021-01-19 Typo in number Ch15 p.423-24 In text, and Figure 15.3, cp=0.001 is wrong It’s cp=0.01
15-02 2021-01-19 Typo in text Ch15 p.427 “improved the R-squared in the test sample by less than” improved the R-squared in the train sample by less than
15-03 2021-07-13 Code vs text Ch15 p.431 “Therefore, it should be performed on the holdout set.” “However, it may be performed on the training set.”
15-04 2021-07-13 Wrong comment Ch15 p.433 “In Figure 15.7, we can look at variable importance for a regression tree on the holdout set. Note that the role of the holdout set is played by the single test set of 144 observations in this oversimplified case study.” “In Figure 15.7, we can look at variable importance for a regression tree . Note that used a the single training set in this oversimplified case study.”
15-05 2021-07-13 Code vs text Ch15 p.434 Figure 15.7 (holdout set, N=144). (training set, N=333).
15-06 2021-10-06 Code vs text comment Ch15 p.434 Figure 15.7 The variable importance plot has small values for features that are not part of the tree. This is not an error, just part of how some variable importance algorithms work (e.g. rpart in R) The reduction in the loss function attributed to each variable at each split is tabulated and the sum is returned. Also, since there may be candidate variables that are important but are not used in a split, the top competing variables are also tabulated at each split. Default in R. Not in Python.
16-01 2021-01-19 Wrong reference Ch16 p.443 “We have illustrated the basics of growing a regression tree using the airbnb dataset in a single London borough.” “We have illustrated the basics of growing a regression tree using the used-cars dataset.”
16-02 2021-07-13 Code vs text Ch16 p.444 “using the holdout sample that we set aside (Chapter 14, Section 14.7).” “using the training as well as the holdout sample that we set aside (Chapter 14, Section 14.7).”
16-03 2021-01-19 Imprecise Ch16 p. 445 “The partial dependence plot shows the values of the x variables within each copy of the data against the average predicted y from that data.” “The partial dependence plot shows the values of the x variables against the average predicted y on the holdout set.”
16-04 2021-07-13 Code vs text Ch16 p.447 Figure 16.1 footnote: “Variable importance based on predictions for the holdout set.” … “(holdout set, N=14 946)” “Variable importance based on predictions for the training set.” … “(work set, N=34 880)””
16-05 2021-07-13 Code vs text Ch16 p.448 Figure 16.2 footnote: “Variable importance based on predictions for the holdout set.” … “(holdout set, N=14 946)” “Variable importance based on predictions for the training set.” … “(work set, N=34 880)”
16-06 2021-01-20 Typo in graph numbers Ch16 p.448 Figure 16.2a and 16.2b wrong 16.2a and 16.2b titles should be swapped: 16.2a is “Factor variables grouped”; 16.2b is “Top 10 important variables”.
16-07 2021-02-09 Imprecise language Ch16 p.446-8, Box 16.3. PDP: it shows “average y,”, about the “$$y-x$$ relationship” conditional on other x variables. The PDP shows average predicted y ( $$\hat{y}$$), about the “$$\hat{y} - x$$ relationship” conditional on other variables.
16-08 2021-02-13 Imprecise sentence Ch16 A1-A3 The currency is USD for price Actually, local currency (GBP) is used. Recently clarified
17-01 2021-01-21 Typo numbers Ch17 p.479 “Yields 139 euros higher profit … increase of 139 000 euros in profits” “Yields 135 euros higher profit … increase of 135 000 euros in profits “
18-01 2021-07-12 Code vs table Ch18 p.509 “RMSE result for the VAR is RMSE=4.4” “RMSE result for the VAR is RMSE=4.5”
18-02 2021-07-12 Code vs table Ch18 p.510 M7 (var) RMSE line presents results without seasonality ( reads: 13.30, 5.85, 3.52, 4.28, 7.8) M7 (var) RMSE line should read: 5.24, 2.51, 5.18, 4.75, 4.5

### Part IV

ID Date added Error Type Chapter Page Problematic Corrected
19-01 2021-02-16 Typo reference Ch19 p.562 “… with the help of a t-test (Chapter 6, Section 5.U1).”, “…and the false negative (see Chapter 6, Section 5.U1)” “… with the help of a t-test (Chapter 6, Section 3).”, “…and the false negative (see Chapter 6, Section 4)”
21-01 2021-03-01 Typo number Ch21 p.600 In Table 21.1, the number of observations in column 1 N=8440 N is 8439 not 8440
21-02 2021-03-01 Typo number Ch21 p.600 Formulae 21.17 and 21.21 are not correct, in the second term in the denominator. In the second term in the denominator, instead of x=0 there should be x=1
21-03 2021-05-11 Typo mumber Ch21 p.607 In Table 21.2, the number of matched observations (5751 and 5528) slightly off col 1: 5716, col 2: 5481
21-04 2021-05-11 Typo mumber Ch21 p.607 In Table 21.2, the number of observations in the second column (8827) is slightly off N is 8439 not 8227
24-01 2020-12-09 Text not match code Ch24 B2 p.696 “When there was more than one candidate game within the same season for the same team, we selected the first one in the season.” “When there was more than one candidate game within the same season for the same team, we selected one in the season randomly.”
24-02 2021-06-07 Imprecise sentence Ch24 B2 page 698 “Here the intercept, $$\beta_0$$, shows the average change in points in the reference time period, from 7–12 games before to 1–6 games before, for pseudo-interventions. $$\beta_1$$ shows the average change in points from 1–6 games before to 1–6 games after, in addition to $$\beta_0$$. $$\beta_2$$ shows the average change in points from 1–6 games after to 7–12 games after, again, in addition to $$\beta_0$$. Thus, the change from 1–6 games before the pseudo-intervention to 1–6 games after it is $$\beta_1 + \beta_0$$.” "”Here, the intercept, $$\beta_0$$ shows the average change in points in the reference time period: from event time window $$[-12,-7]$$ to event time window $$[-6,-1]$$, for the control group. $$\beta_1$$ shows the average change in points from event time window $$[-6,-1]$$ to event time window $$[1,6]$$, compared to the change in the reference time period (captured by $$\beta_0$$), for the control group. $$\beta_2$$ shows the average change in points from event time $$[1,6]$$ to $$[7,12]$$, again compared to the change in the reference time period, for the control group.”
24-03 2021-06-07 Imprecise sentence Ch24 B2 page 698 ”$$\beta_3$$ shows the difference between the treatment and control group in terms of average point change from 7–12 games before to 1–6 before. If we selected the control group well, this should be close to zero.” ”$$\beta_3$$ shows the treatment-control difference in the change in the reference time period (from $$[-12,-7]$$ 7-12 to $$[-6,-1]$$). If we selected the control group well, $$\beta_3$$ should be close to zero, because we want the control group to have the same pre-treatment changes in the outcome variable.”