1 Chapter 1

p. 4, line 14 from bottom: happening they way → happening the way
p. 5, lines 3-4 of par. 2: could impossibly have contributed something useful → could not possibly have contributed something useful (thanks to Michael Fiddler for pointing this out)
p. 10, example (1a) should be labeled vop (as it is spelled out in the parentheses; thanks to Michael Fiddler for pointing this out)
p. 12, line 1: things whereas → things prefer CONSTRUCTION: vpo whereas (thanks to Adam Przepiórkowski for pointing this out)
p. 14, last line: the word not into → the word not into
p. 29, line 6: support our H1 → support our H₁ whereas (thanks to Adam Przepiórkowski for pointing this out)
p. 29, par 1 after 4b.: ’the result is of a kind […] → ‘the result is of a kind […] threshold’ (thanks to Adam Przepiórkowski for pointing this out)
p. 33, line 1: in Table 11, →’ In Table 11, (thanks to Adam Przepiórkowski for pointing this out)
p. 33, line 6: Why is that →’ Why is that? (thanks to Adam Przepiórkowski for pointing this out)
p. 36, line 3: even i the →’ even if the (thanks to Adam Przepiórkowski for pointing this out)
p. 36, line 7: deviation).The →’ deviation). The (thanks to Adam Przepiórkowski for pointing this out)
p. 36, line 6 from bottom of first paragraph: because the one assumes → because then one assumes
p. 39, line 4 from bottom: then cumulative the → then the cumulative (thanks to Adam Przepiórkowski for pointing this out)
p. 42, line 2: ’“how likely it → ’how likely it (thanks to Adam Przepiórkowski for pointing this out)
p. 44, section heading of 1.6: Data collection and storage → The design of a factorial experiment (thanks to Maria Fionda for pointing this out)

2 Chapter 2

p. 54: the first code block should be this (thanks to Michael Fiddler for pointing this out):

> 2 -
+

p. 54: the third code block should be this (thanks to Michael Fiddler for pointing this out):

> library(effects +
+

p. 57, the first code box should be this:

log(x=5, base=2) # compute the log of 5
log(base=2, x=5) # (to the base of 2)

p. 64, the last block of code should be this (thanks to Michael Fiddler for pointing this out):

(x <- scan(file="_inputfiles/02_vector2.txt", what=character(),
   sep="\n"))
[1] "This is the first line"  "This is the second line"

p. 67: line 3 from bottom: Thus, which translates → Thus, which translates (thanks to Michael Fiddler for pointing this out):
p. 71: mid of par. 2: Crucially, the values that table returns → Crucially, the values that table returns (thanks to Michael Fiddler for pointing this out)
p. 73: mid of par. 2: then, like sort it can take a logical vector called decreasing as an additional argument → then, like sort, it can take a logical vector called decreasing as an additional argument (thanks to Michael Fiddler for pointing this out)
p. 78, caption of Table 20: Concrete token set for CONSTRUCTION:OBJTYPE 3 → A simple example data frame
p. 80, lines 6-7 before 2.5.2: human-readable strings closed and open and → human-readable strings closed and open and (thanks to Michael Fiddler for pointing this out)
p. 82, line 3: we can usually just use the function read.delim with only the file argument → we can usually just use the function read.delim with only the file argument (and ideally also stringsAsFactors=TRUE) (thanks to Adam Przepiórkowski for pointing this out)
p. 84: the recommendation(s) for further study might be better placed immediately before the beginning of Section 2.5.3
p. 85, line 2: with subsetting - [ or subset - it’s often %rarr; with subsetting - "[" or subset - it’s often
p. 85, last line: We will return to split in → We will return to split in (thanks to Michael Fiddler for pointing this out)

3 Chapter 3

p. 102, line 1 of 3.1.2.3: can typically done → can typically be done (thanks to Adam Przepiórkowski for pointing this out)
p. 112, lines 1-2 of code box should be this (thanks to Michael Fiddler for pointing this out):

set.seed(sum(utf8ToInt("Räucherforelle"))) # set a replicable random number seed
two.normals.side.by.side <- c(rnorm(200, 0, 2), rnorm(200, 8, 2)) # generate random data

p. 113-114, the two code boxes: change the two “shorter ways” to the following: (thanks to Adam Przepiórkowski for pointing this out)

MEAN.c <- as.numeric(scale(MEAN, scale=FALSE))
MEAN.s <- as.numeric(scale(MEAN))

p. 117, change code box 2 to the following: (thanks to Adam Przepiórkowski for pointing this out)

c("mean"=mean(RT), "sd"=sd(RT), "n"=length(RT))
##      mean        sd         n
## 631.85996  60.16321  77.00000

p. 118, bullet point 2: Fields → Field (thanks to Adam Przepiórkowski for pointing this out)

On p. 119:

what is the smallest number of heads you can get in 500 tosses that is not significantly different from the 300-out-of-500 result? It’s 277, i.e. 0.554 or 55.4% (the result of sum(dbinom(x=0:277, size=number.tosses, prob=perc.heads)) is <0.025, the result of sum(dbinom(x=0:278, size=number.tosses, prob=perc.heads)) is >0.025);
what is the largest number of heads you can get in 500 tosses that is not significantly different from the 300-out-of-500 result? It’s 322, i.e. 0.644 or 64.4% (the result of sum(dbinom(x=322:number.tosses, size=number.tosses, prob=perc.heads)) is <0.025, the result of sum(x=dbinom(0:321, size=number.tosses, prob=perc.heads)) is >0.025);

Thus, computing the 95%-CI for this example in this way returns [277, 322] or, in %, [0.554, 0.644], and you can see how similar they are to the result of binom.test, which was [0.556, 0.643]. Thus, the 95%-CI includes the values that do not differ significantly from the result you found and the interpretation of this approach would be to say “the 95%-CI boundaries are the numbers of heads that wouldn’t be significantly different from the 0.6 result you got, i.e. between 277 and 322”.

→

what is the smallest number of heads you can get in 500 tosses that is not significantly different from the 300-out-of-500 result? It’s 278, i.e. 0.556 or 55.6% (the result of sum(dbinom(x=0:278, size=number.tosses, prob=perc.heads)) is >0.025, the result of sum(dbinom(x=0:277, size=number.tosses, prob=perc.heads)) is >0.025);
what is the largest number of heads you can get in 500 tosses that is not significantly different from the 300-out-of-500 result? It’s 321, i.e. 0.642 or 64.2% (the result of sum(dbinom(x=321:number.tosses, size=number.tosses, prob=perc.heads)) is >0.025, the result of sum(x=dbinom(322:number.tosses, size=number.tosses, prob=perc.heads)) is >0.025).

Thus, computing the 95%-CI for this example in this way returns [278, 321] or, in %, [0.556, 0.642], and you can see how similar they are to the result of binom.test, which was [0.556, 0.643]. Thus, the 95%-CI includes the values that do not differ significantly from the result you found and the interpretation of this approach would be to say “these 95%-CI boundaries are the numbers of heads that wouldn’t be significantly different from the 0.6 result you got”.

p. 125, last par.: Section 3.1.2) often get analyzed with the tools designed for categorical variables; → Section 3.1.2 often get analyzed with the tools designed for categorical variables); (thanks to Adam Przepiórkowski for pointing this out)
p. 136, last line of the third last paragraph: around 640 somewhere: → around 640 somewhere.
p. 139, line 9 from bttom: hopefully immediately discarded → hopefully have immediately discarded (thanks to Adam Przepiórkowski for pointing this out)
p. 154, upper code box: change the line beginning with sort to as.character(sort(unique(x[,i]))), (thanks to Adam Przepiórkowski for pointing this out)
p. 156, line 2 above code box 1: pairwise addition → pairwise multiplication (thanks to Adam Przepiórkowski for pointing this out)

4 Chapter 4

p. 165, line 11 from bottom: In both cases → In that case (thanks to Adam Przepiórkowski for pointing this out)
p. 176, line 1 of last paragraph: Kolmogorov-Smirnomv test → Kolmogorov-Smirnov test (thanks for William Comer for pointing this out)
p. 177, line two above Figure 56: the plot above → the plot below (thanks for Michael Fiddler for pointing this out)
p. 179, line 3 below code box 2: (14765) → (147/65) (thanks to Adam Przepiórkowski for pointing this out)
p. 179, line 3 below code box 2: (i.e., the same as c(nrow(...), nol(...)) → (i.e., the same as c(nrow(...), nol(...))) (thanks to Adam Przepiórkowski for pointing this out)
p. 187:, delete paragraph below Figure 60
p. 207, code block 2: (t.value ← (mean(x$HEDGES)-12.35) → (t.value <- (mean(x$HEDGES)-12.35) (thanks to Adam Przepiórkowski for pointing this out)
p. 208, line 3: with (i) the → with the (thanks to Adam Przepiórkowski for pointing this out)
p. 211, note 9: step 4b → 3b (thanks to Adam Przepiórkowski for pointing this out)
p. 212, line 2 above code block 2: applies (i) to → applies to (thanks to Adam Przepiórkowski for pointing this out)
p. 212, line 1 above code block 2: argument: (I show only one result.) → argument (I show only one result): (thanks to Adam Przepiórkowski for pointing this out)
p. 213, bullet point 2: one categorical response CRITERION → one categorical predictor CRITERION (thanks to Adam Przepiórkowski for pointing this out)
p. 215, procedure point 4: compute the test statistic T → compute the test statistic V (thanks to Adam Przepiórkowski for pointing this out)
p. 217, line 2: collect it somewhere; → collect it somewhere; (thanks for Michael Fiddler for pointing this out)
p. 222, delete the last (parenthesized) sentence of 4.3.2.3
p. 229, 2 lines below Figure 76: of the two variable RT → of the two variables RT (thanks for Michael Fiddler for pointing this out)
p. 230, H₁: There is a negative correlation between MOUSEEVENT and KEYPRESS, r/ρ/τ<0. → There is a positive correlation between MOUSEEVENT and KEYPRESS, r/ρ/τ>0. (thanks to Adam Przepiórkowski for pointing this out)
p. 231, bullet point 2: so 00 mean → so 00 means (thanks for Michael Fiddler for pointing this out)

5 Chapter 5

p. 236, last line: Figure 79: mpg ~ disp → Figure 79: mpg ~ disp (thanks to Adam Przepiórkowski for pointing this out)
p. 237, line 8 from bottom: contribution: (p=0.3314). → contribution (p=0.3314).
p. 242, line 3 from bottom: We see that same thing in the our current plot → We see that same thing in our current plot (thanks to Adam Przepiórkowski for pointing this out)
p. 243: jitter(log2(x)$FREQ) → jitter(log2(x$FREQ)) (thanks to Mohammad Alenezi for pointing this out)
p. 243, line 2 from bottom: easily to un-do → easy to undo (thanks to Adam Przepiórkowski for pointing this out)
p. 245, above equation: the intercept): → the intercept): (thanks to Adam Przepiórkowski for pointing this out)
p. 251, line 1 of last par.: or hypothesis → our hypothesis (thanks to Adam Przepiórkowski for pointing this out)
p. 251: line 2 above last code block: 2.12] and [2.12, → -4.585] and [+4.585,
p. 251, line 2: confidence intervals as well → confidence intervals as well) (thanks for Michael Fiddler for pointing this out)
p. 251, line 1 of last par.: we formulated or hypothesis → we formulated our hypothesis (thanks for Michael Fiddler for pointing this out)
p. 256, the first code block should be this:

sum(coef(m.01) * mm["ant",]) # same as predict(m.01)["ant"] and same as
667.03260 * # coef(m.01)[1]   , the intercept
1 +         # mm["ant",1]     , the intercept in the model matrix
-16.82015 * # coef(m.01)[2]   , the slope for FREQ
2.807355    # mm["ant",2]     , FREQ for "ant"

p. 265, line 3: in term of → in terms of (thanks to Adam Przepiórkowski for pointing this out)
p. 290, line 4: recall Table Table 21 → recall Table 21 (thanks to Adam Przepiórkowski for pointing this out)
p. 291, line 2 from bottom: what ever you’d → whatever you’d (thanks for Michael Fiddler for pointing this out)
p. 297, line 6 after code box: which level a certain exhibits → which level a certain data point exhibits (thanks to Adam Przepiórkowski for pointing this out)
p. 297, line 3 after bullet points: that ant has → that ant has (thanks for Michael Fiddler for pointing this out)
p. 303, line 2 above recommendation: the both the → both the (thanks for Michael Fiddler for pointing this out)
p. 303, line 6 from bottom: so that see how → so that we can see how (thanks to Adam Przepiórkowski for pointing this out)
p. 328, line 2 of code box: add underscores before helpers (thanks to Adam Przepiórkowski for pointing this out)
p. 332, line 2 of code box: add underscores before helpers (thanks to Adam Przepiórkowski for pointing this out)
p. 333, mid: Let’s compute Cis → Let’s compute CIs (thanks to Adam Przepiórkowski for pointing this out)
p. 339, line 9 from bottom: which I will use it to → which I will use to (thanks to Adam Przepiórkowski for pointing this out)
p. 342, last line: involved in. → involved in). (thanks to Adam Przepiórkowski for pointing this out)
p. 350, line 1 of code block 1: grid=TRUE → grid=TRUE) (thanks to Adam Przepiórkowski for pointing this out)
p. 350, line 3 of code block 1: co fint → confint (thanks for Michael Fiddler for pointing this out)
p. 352: lines 1-2: which his why → which is why (thanks for Michael Fiddler for pointing this out)
p. 354, line before code box 1: that is changed → that it is changed (thanks to Adam Przepiórkowski for pointing this out)
p. 354, line 2 of code box: add underscores before helpers (thanks to Adam Przepiórkowski for pointing this out)
p. 371, the last bullet point: the equation for models with ordinal responses should be $m=n-\frac{1}{n^2} \times \sum_{i=1}^{k} n_i^3$ (thanks to Adam Przepiórkowski for pointing this out)
p. 381, lines 1-2 above Figure 127: the long run or prepositional datives → the long run of prepositional datives (thanks for Michael Fiddler for pointing this out)
p. 381, line 1 below Figure 127: to show many → to show how many (thanks for Michael Fiddler for pointing this out)
p. 385, mid: called overfitting A → called overfitting. A (thanks for Adam Przepiórkowski for pointing this out)

6 Chapter 6

p. 393, epigraph: testing], A → testing], (thanks for Adam Przepiórkowski for pointing this out)
p. 394, line 3 from bottom: there’s only file → there’s only one file (thanks to Adam Przepiórkowski for pointing this out)
p. 400, last line: how of data points produced → how the data points produced (thanks to Adam Przepiórkowski for pointing this out)
p. 409, line 3 from bottom: and when an MEM with → and an MEM with (thanks to Adam Przepiórkowski for pointing this out)
p. 410, 5 lines above recommendations: (2019:161 formulate → (2019:161) formulate
p. 412, line 1 or par. 2: Back the MEMS: → Back to MEMs: (thanks to Adam Przepiórkowski for pointing this out)
p. 417, line 2: there are quote noticeable → there are quite noticeable (thanks to Adam Przepiórkowski for pointing this out)
p. 419, line 8-9: ‘pockets’ of the data → ‘pockets’ of the data) (thanks to Adam Przepiórkowski for pointing this out)
p. 429, line 2 from bottom: ; for predictors (which → for predictors (which (thanks to Adam Przepiórkowski for pointing this out)
p. 430, line 4 from bottom: but now there are → but now they are (thanks to Adam Przepiórkowski for pointing this out)
p. 432, line 2 below code block 2: 7×1015 → 7×10¹⁵ (thanks to Adam Przepiórkowski for pointing this out)
p. 434, bottom code block: the random number seed should be set like this: set.seed(sum(utf8ToInt("Brokkoliauflauf")))
p. 435, last line of code block 2: grid=TRUE → grid=TRUE) (thanks to Adam Przepiórkowski for pointing this out)
p. 438, 2nd code block: the random number seed should be set like this: set.seed(sum(utf8ToInt("Schokonüsse")))
p. 439, line 2 of code box: add underscores before helpers (thanks to Adam Przepiórkowski for pointing this out)
p. 445, recommendations for further study: the random number seed should be set like this: set.seed(sum(utf8ToInt("simres")))
p. 446, bottom code block: the random number seed should be set like this: set.seed(sum(utf8ToInt("PolkHigh")))
p. 448, top code block: the random number seed should be set like this: set.seed(sum(utf8ToInt("Gorp.Fnark.Schmegle.")))
p. 448, line 1 of code block 2: spkt <- effect(m.final))) → spkt <- effect(“SPEAKERTYPE”, m.final))) (thanks to Adam Przepiórkowski for pointing this out)
p. 450, line 2 below Figure: when CONTRAST is new → when GIVENNESS.fac is new (thanks to Adam Przepiórkowski for pointing this out)

Note: the set.seed(sum(... errors were also fixed in the now updated code files.

7 Chapter 7

p. 453: from tree-base methods → from tree-based methods
p. 460: effects plots of shown → effects plots shown
p. 462, after code block 2: this is how can → this is how you can (thanks for Michael Fiddler for pointing this out)
p. 462, line 2 from bottom: pruned classification tree. → pruned classification tree). (thanks to Adam Przepiórkowski for pointing this out)
p. 466: and that the accuracy was much lower for sc-mc(0.46875) than for mc-sc (0.17818). → and that the classification/prediction error was much higher for sc-mc (0.46875) than for mc-sc (0.17818).
p. 76, line 1: provide quite importance results → provide quite important results (thanks to Adam Przepiórkowski for pointing this out)
p. 480: de-indent the third bullet point so that it is aligned with the previous two.

8 References

p. 489: add the following entry to the reference section: Good, Philip I., & James W. Hardin. 2012. Common errors in statistics (and how to a them). 4th ed. Hoboken, NJ: John Wiley and Sons.

Errata for SFLWR3

Stefan Th. Gries

2024-11-19 16:32:00