diff --git a/errata.Rmd b/errata.Rmd
index b92f347..4863ca1 100644
--- a/errata.Rmd
+++ b/errata.Rmd
@@ -17,9 +17,11 @@ If you notice an error that is not mentioned in the errata below, submit an issu
- p. 167, the code line `y_rep[s,1] <- y[1]` should be `y_rep[s,1] <- unemp$y[1]` (thanks to Ravi Shroff)
- p. 181, ex 11.5(b) "Figure 10.2." should be "Figure 11.2." (thanks to Ravi Shroff)
- p. 187, "a procedure that is equivalent to centering and rescaling is to leave" $\rightarrow$ "we can get the same inferences for the coefficients other than the intercept by leaving" (thanks to Ravi Shroff)
+- p. 209, "$\frac{p_0}{D-p_0}\frac{\sigma}{\sqrt{n}}$, where $p_0$ is the expected number of relevant predictors" $\rightarrow$ "$\frac{p_0}{p-p_0}\frac{\sigma}{\sqrt{n}}$, where $p$ is the number of predictors, $p_0$ is the expected number of relevant predictors" (thanks to Roberto Viviani)
- p. 231, $\log(-.595)$ $\rightarrow$ $\log(0.595)$ (thanks to Daniel Timar)
- p. 267 `fit_nb[[k]] <- stan_glm(y ~ x, family=neg_binomial_2(link="log"), data=fake, refresh=0)` $\rightarrow$ `fit_nb[[k]] <- stan_glm(y ~ x, family=neg_binomial_2(link="log"), data=fake_nb[[k]], refresh=0)` (thanks to Brian Bucher)
- p. 268, `offset=log(exposure)` $\rightarrow$ `offset=log(exposure2)` (thanks to A. Solomon Kurz)
+- p. 269, `y_rep_1 <- posterior_predict(fit_1)` $\rightarrow$ `yrep_1 <- posterior_predict(fit_1)` (thanks to Darci Kovacs)
- p. 285, The numbers for the earnings model on p.285 should be the same as on p. 284 (thanks to David Galley)
- p. 295, $n=(2.8* 0.49/0.1)^2=196$ $\rightarrow$ $n=(2.8* 0.5/0.1)^2=196$, where $0.5$ is used as a conservative estimate of the standard deviation (thanks to Solomon A. Kurz)
- p. 299, $0.5/1.15 = 0.43$ $\rightarrow$ $0.5/1.25 = 0.4$ (thanks to Solomon A. Kurz)
@@ -39,7 +41,7 @@ the true $\beta$ to be 2.8 standard errors from zero, so that there is
an 80\% probability that $\hat{\beta}$ is at least 2 standard errors
from zero. If $\beta=0.018\%$, then its standard error would have to be
no greater than $0.018/2.8$, so that the survey would need a sample
-size of $1629*(2.8*0.015/0.018)^2=900$. (thanks to comment by Solomon A. Kurz)
+size of $1629*(2.8*0.015/0.018)^2=9000$. (thanks to comment by Solomon A. Kurz, and typo fix by Patrick Wen)
- p. 304 "only 44% of their children were girls" $\rightarrow$ "only 48% of their children were girls" (thanks to comment by Solomon A. Kurz)
- p. 375, three regression equations (unnumbered, 19.2 and 19.3) are missing $\alpha + $ from the beginning of RHS (right after $=$). (thanks to Junhui Yang)
- p. 407, $\sum_{k=1}^K(X_{ik}-X_{jk})^2$ $\rightarrow$ $(\sum_{k=1}^K(X_{ik}-X_{jk})^2)^{1/2}$ (thanks to Stefan Gehrig)
@@ -78,6 +80,7 @@ size of $1629*(2.8*0.015/0.018)^2=900$. (thanks to comment by Solomon A. Kurz)
- p. 208, Figure 12.11 all top row subplots show the prior distribution for the regularized horseshoe prior. The correct subplot are produced by the R code in [Student example](https://avehtari.github.io/ROS-Examples/Student/student.html) and shown below (thanks to Zhengchen Cai for reporting the issue)
{ width=800 }
+- p. 213, Ex 12.11, "$\beta$ is 0.3" $\rightarrow$ $\beta$ is $-0.3$ (thanks t o Justin Gross)
- p. 217, First line in section 13.1 "The logistic function, logit(x)" $\rightarrow$ "The logit function, logit(x)" (thanks to VicentModesto)
- p. 219, "The inverse logistic function is curved" $\rightarrow$ "The inverse logit function is curved" (thanks to VicentModesto)
- p. 241-242, in paragraphs starting "The steps go..." and "Figure 14.2...": $(4.0/4.1)x_1$ $\rightarrow$ $(4.4/4.1)x_1$ (thanks to Doug Davidson)
diff --git a/errata.html b/errata.html
index 0404fc7..e1d6e41 100644
--- a/errata.html
+++ b/errata.html
@@ -343,7 +343,7 @@
Regression and Other Stories - Errata
Andrew Gelman, Jennifer Hill, Aki Vehtari
-Page updated: 2022-01-13
+Page updated: 2022-02-24
@@ -359,16 +359,18 @@ 1st and 2nd printing
p. 167, the code line y_rep[s,1] <- y[1] should be y_rep[s,1] <- unemp$y[1] (thanks to Ravi Shroff)
p. 181, ex 11.5(b) “Figure 10.2.” should be “Figure 11.2.” (thanks to Ravi Shroff)
p. 187, “a procedure that is equivalent to centering and rescaling is to leave” \(\rightarrow\) “we can get the same inferences for the coefficients other than the intercept by leaving” (thanks to Ravi Shroff)
+p. 209, “\(\frac{p_0}{D-p_0}\frac{\sigma}{\sqrt{n}}\), where \(p_0\) is the expected number of relevant predictors” \(\rightarrow\) “\(\frac{p_0}{p-p_0}\frac{\sigma}{\sqrt{n}}\), where \(p\) is the number of predictors, \(p_0\) is the expected number of relevant predictors” (thanks to Roberto Viviani)
p. 231, \(\log(-.595)\) \(\rightarrow\) \(\log(0.595)\) (thanks to Daniel Timar)
p. 267 fit_nb[[k]] <- stan_glm(y ~ x, family=neg_binomial_2(link="log"), data=fake, refresh=0) \(\rightarrow\) fit_nb[[k]] <- stan_glm(y ~ x, family=neg_binomial_2(link="log"), data=fake_nb[[k]], refresh=0) (thanks to Brian Bucher)
p. 268, offset=log(exposure) \(\rightarrow\) offset=log(exposure2) (thanks to A. Solomon Kurz)
+p. 269, y_rep_1 <- posterior_predict(fit_1) \(\rightarrow\) yrep_1 <- posterior_predict(fit_1) (thanks to Darci Kovacs)
p. 285, The numbers for the earnings model on p.285 should be the same as on p. 284 (thanks to David Galley)
p. 295, \(n=(2.8* 0.49/0.1)^2=196\) \(\rightarrow\) \(n=(2.8* 0.5/0.1)^2=196\), where \(0.5\) is used as a conservative estimate of the standard deviation (thanks to Solomon A. Kurz)
p. 299, \(0.5/1.15 = 0.43\) \(\rightarrow\) \(0.5/1.25 = 0.4\) (thanks to Solomon A. Kurz)
p. 300, The paragraph starting “We illustrate with the example of the survey earnings and height discussed in Chapter 4.” and the next two paragraphs have been edited to:
We illustrate with the survey of earnings and height discussed in Chapter 12. The coefficient for the sex-earnings interaction in model (12.2) is plausible (a positive interaction, implying that an extra inch of height is worth 2% more for men than for women), with a standard error of 1%.
Extracting another significant figure from the fitted regression yields an estimated interaction of 0.018 with standard error 0.015. How large a sample size would have been needed for the coefficient on the interaction to be “statistically significant” at the conventional 95% level? A simple calculation uses the fact that standard errors are proportional to \(1/\sqrt{n}\). For a point estimate of 0.018 to be two standard errors from zero, it would need a standard error of 0.009, which would require the sample size to be increased by a factor of \((0.015/0.009)^2\). The original survey had a sample of 1629; this implies a required sample size of \(1629*(0.015/0.009)^2=4500\).
- To perform a power calculation for this hypothetical larger survey of 4500 people, we could suppose that the true \(\beta\) for the interaction is equal to 0.018 and that the standard error is as we have just calculated. With a standard error of 0.009 the estimate from the regression would then be conventionally “statistically significant” only if \(\hat{\beta}>0.018\) (or, in other direction, if \(\hat{\beta}< -0.018\), but that latter possibility is highly unlikely given our assumptions). If the true coefficient \(\beta\) is 0.018, then we would expect \(\hat{\beta}\) to exceed 0.018, and thus achieve statistical significance, with a probability of \(\frac{1}{2}\)—that is, 50% power. To get 80% power, we need the true \(\beta\) to be 2.8 standard errors from zero, so that there is an 80% probability that \(\hat{\beta}\) is at least 2 standard errors from zero. If \(\beta=0.018\%\), then its standard error would have to be no greater than \(0.018/2.8\), so that the survey would need a sample size of \(1629*(2.8*0.015/0.018)^2=900\). (thanks to comment by Solomon A. Kurz)
+ To perform a power calculation for this hypothetical larger survey of 4500 people, we could suppose that the true \(\beta\) for the interaction is equal to 0.018 and that the standard error is as we have just calculated. With a standard error of 0.009 the estimate from the regression would then be conventionally “statistically significant” only if \(\hat{\beta}>0.018\) (or, in other direction, if \(\hat{\beta}< -0.018\), but that latter possibility is highly unlikely given our assumptions). If the true coefficient \(\beta\) is 0.018, then we would expect \(\hat{\beta}\) to exceed 0.018, and thus achieve statistical significance, with a probability of \(\frac{1}{2}\)—that is, 50% power. To get 80% power, we need the true \(\beta\) to be 2.8 standard errors from zero, so that there is an 80% probability that \(\hat{\beta}\) is at least 2 standard errors from zero. If \(\beta=0.018\%\), then its standard error would have to be no greater than \(0.018/2.8\), so that the survey would need a sample size of \(1629*(2.8*0.015/0.018)^2=9000\). (thanks to comment by Solomon A. Kurz, and typo fix by Patrick Wen)
p. 304 “only 44% of their children were girls” \(\rightarrow\) “only 48% of their children were girls” (thanks to comment by Solomon A. Kurz)
p. 375, three regression equations (unnumbered, 19.2 and 19.3) are missing $+ $ from the beginning of RHS (right after \(=\)). (thanks to Junhui Yang)
p. 407, \(\sum_{k=1}^K(X_{ik}-X_{jk})^2\) \(\rightarrow\) \((\sum_{k=1}^K(X_{ik}-X_{jk})^2)^{1/2}\) (thanks to Stefan Gehrig)
@@ -407,6 +409,7 @@ 1st printing (original printing)
p. 190 “an expected positive difference of about 5% in the outcome variable” \(\rightarrow\) “an expected positive difference of about 6% in the outcome variable”
p. 208, /sqrt(0.3*26) \(\rightarrow\) *sqrt(0.3/26) (this is correct in the code in the web page, typo fix in the book thanks to Eugenia Migliavacca)
p. 208, Figure 12.11 all top row subplots show the prior distribution for the regularized horseshoe prior. The correct subplot are produced by the R code in Student example and shown below (thanks to Zhengchen Cai for reporting the issue) 
+p. 213, Ex 12.11, “\(\beta\) is 0.3” \(\rightarrow\) \(\beta\) is \(-0.3\) (thanks t o Justin Gross)
p. 217, First line in section 13.1 “The logistic function, logit(x)” \(\rightarrow\) “The logit function, logit(x)” (thanks to VicentModesto)
p. 219, “The inverse logistic function is curved” \(\rightarrow\) “The inverse logit function is curved” (thanks to VicentModesto)
p. 241-242, in paragraphs starting “The steps go…” and “Figure 14.2…”: \((4.0/4.1)x_1\) \(\rightarrow\) \((4.4/4.1)x_1\) (thanks to Doug Davidson)