Wooldridge Source: These data were collected by Professors Ronald Fisher and Carl Liedholm during a term in which they both taught principles of microeconomics at Michigan State University. Professors Fisher and Liedholm kindly gave me permission to use a random subset of their data, and their research assistant at the time, Jeffrey Guilfoyle, who completed his Ph.D. in economics at MSU, provided helpful hints. Data loads lazily.
data('attend')
A data.frame with 680 observations on 11 variables:
attend: classes attended out of 32
termGPA: GPA for term
priGPA: cumulative GPA prior to term
ACT: ACT score
final: final exam score
atndrte: percent classes attended
hwrte: percent homework turned in
frosh: =1 if freshman
soph: =1 if sophomore
missed: number of classes missed
stndfnl: (final - mean)/sd
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
The attendance figures were obtained by requiring students to slide their ID cards through a magnetic card reader, under the supervision of a teaching assistant. You might have the students use final, rather than the standardized variable, so that they can see the statistical significance of each variable remains exactly the same. The standardized variable is used only so that the coefficients measure effects in terms of standard deviations from the average score.
Used in Text: pages 111, 152, 199-200, 222
#> 'data.frame': 680 obs. of 11 variables: #> $ attend : int 27 22 30 31 32 29 30 26 24 29 ... #> $ termGPA: num 3.19 2.73 3 2.04 3.68 ... #> $ priGPA : num 2.64 3.52 2.46 2.61 3.32 ... #> $ ACT : int 23 25 24 20 23 26 21 22 24 21 ... #> $ final : int 28 26 30 27 34 25 10 34 26 26 ... #> $ atndrte: num 84.4 68.8 93.8 96.9 100 ... #> $ hwrte : num 100 87.5 87.5 100 100 100 75 100 100 100 ... #> $ frosh : int 0 0 0 0 0 0 1 0 1 0 ... #> $ soph : int 1 0 0 1 1 1 0 1 0 1 ... #> $ missed : int 5 10 2 1 0 3 2 6 8 3 ... #> $ stndfnl: num 0.4727 0.0525 0.8929 0.2626 1.7332 ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"