Wooldridge Source: Dr. Zhehui Luo, a recent MSU Ph.D. in economics and Visiting Research Associate in the Department of Epidemiology at MSU, kindly provided these data. She obtained them from state files linking birth and infant death certificates, and from the National Center for Health Statistics natality and mortality data. Data loads lazily.
data('bwght2')A data.frame with 1832 observations on 23 variables:
mage: mother's age, years
meduc: mother's educ, years
monpre: month prenatal care began
npvis: total number of prenatal visits
fage: father's age, years
feduc: father's educ, years
bwght: birth weight, grams
omaps: one minute apgar score
fmaps: five minute apgar score
cigs: avg cigarettes per day
drink: avg drinks per week
lbw: =1 if bwght <= 2000
vlbw: =1 if bwght <= 1500
male: =1 if baby male
mwhte: =1 if mother white
mblck: =1 if mother black
moth: =1 if mother is other
fwhte: =1 if father white
fblck: =1 if father black
foth: =1 if father is other
lbwght: log(bwght)
magesq: mage^2
npvissq: npvis^2
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
There are many possibilities with this data set. In addition to number of prenatal visits, smoking and alcohol consumption (during pregnancy) are included as explanatory variables. These can be added to equations of the kind found in Exercise C6.10. In addition, the one- and five-minute APGAR scores are included. These are measures of the well being of infants just after birth. An interesting feature of the score is that it is bounded between zero and 10, making a linear model less than ideal. Still, a linear model would be informative, and you might ask students about predicted values less than zero or greater than 10.
Used in Text: pages 184, 223
str(bwght2)
#> 'data.frame': 1832 obs. of 23 variables:
#> $ mage : int 26 29 33 28 23 28 27 41 32 16 ...
#> $ meduc : int 12 12 12 17 13 12 16 17 12 11 ...
#> $ monpre : int 2 2 1 5 2 1 3 6 3 2 ...
#> $ npvis : int 12 12 12 8 6 12 11 8 11 10 ...
#> $ fage : int 34 32 36 32 24 30 28 56 36 21 ...
#> $ feduc : int 16 12 16 17 16 16 16 NA 16 10 ...
#> $ bwght : int 3060 3730 2530 3289 3590 3420 3355 3459 3590 4410 ...
#> $ omaps : int 9 8 8 8 6 9 9 8 9 6 ...
#> $ fmaps : int 9 9 9 9 8 9 9 9 9 9 ...
#> $ cigs : int 0 NA 0 0 0 0 0 0 0 0 ...
#> $ drink : int 0 NA 0 0 0 0 0 0 0 0 ...
#> $ lbw : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ vlbw : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ male : int 1 0 0 1 1 0 1 0 0 1 ...
#> $ mwhte : int 0 1 1 1 1 1 1 0 1 1 ...
#> $ mblck : int 0 0 0 0 0 0 0 1 0 0 ...
#> $ moth : int 1 0 0 0 0 0 0 0 0 0 ...
#> $ fwhte : int 0 1 1 1 1 1 1 0 1 1 ...
#> $ fblck : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ foth : int 1 0 0 0 0 0 0 1 0 0 ...
#> $ lbwght : num 8.03 8.22 7.84 8.1 8.19 ...
#> $ magesq : int 676 841 1089 784 529 784 729 1681 1024 256 ...
#> $ npvissq: int 144 144 144 64 36 144 121 64 121 100 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"