Errata for A Gentle Introduction to Stata, 4th Edition

The errata for A Gentle Introduction to Stata, Fourth Edition are provided below. Click here for an explanation of how to read an erratum. Click here to learn how to determine the printing number of a book.

(1) Chapter 7, p. 178, fifth line of second paragraph
... power twomeans 35 37, sd1(10) sd2(10) power(0.80) ... ... power twomeans 35 37, sd1(10) sd2(15) power(0.80) ...
(1) Chapter 9, p. 260, first line of second paragraph
... with 23 participants ... ... with 53 participants ...
(all) Chapter 10, p. 269–270
The variable env_con (environmental concern) was accidentally reverse coded in ops2004.dta. Thus the variable has a higher score for people who have less environmental concern. This makes the coefficients on page 270 have a sign that is opposite of what they should have. The remaining portion of the chapter is correct in terms of procedures and Stata code. The interpretation is correct if the coding error is ignored and we assume that higher values of env_con correspond to more environmental concern.
(1,2) Chapter 10, p. 283, first full paragraph, second sentence
... (we will use 10,000 here) ... ... (we will use 1,000 here) ...
(1,2) Chapter 10, p. 321, sentence above the Stata output
H0 is r2f(.20), Ha is r2r(0), ... H0 is r2r(0), Ha is r2f(.20), ...
(1,2) Chapter 14, p. 435, Stata command middle of page
. use http://www.stata-press.com/agis4/nourishing_bmi . use http://www.stata-press.com/data/agis4/flourishing_bmi
(all) Chapter 14, p. 440, question 1
Using gss2002_chapter10.dta, fit the model using all the variables except for polviews.
  1. Do this using the regress command.
  2. Do this using the sem command and SEM Builder.
  3. List the commands as you would enter them in a do-file.
 You are interested in the socioeconomic status (sei) of adult children. You have the following predictors: male, mother's education (maeduc), father's education (paeduc), hours worked last week (hrs1), and education (educ). Use gss2002_chapter10.dta, and recode the variable, sex, as male (1 male; 0 female).
  1. Fit this model using regress, and estimate standardized coefficients.
  2. Do the same using the sem command and SEM Builder.
  3. List the commands as you would enter them in a do-file.