Wooldridge Source: See CEOSAL1.RAW Data loads lazily.
data('ceosal2')A data.frame with 177 observations on 15 variables:
salary: 1990 compensation, $1000s
age: in years
college: =1 if attended college
grad: =1 if attended graduate school
comten: years with company
ceoten: years as ceo with company
sales: 1990 firm sales, millions
profits: 1990 profits, millions
mktval: market value, end 1990, mills.
lsalary: log(salary)
lsales: log(sales)
lmktval: log(mktval)
comtensq: comten^2
ceotensq: ceoten^2
profmarg: profits as percent of sales
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
Compared with CEOSAL1.RAW, in this CEO data set more information about the CEO, rather than about the company, is included.
Used in Text: pages 64, 111, 163, 214, 335, 699
str(ceosal2)
#> 'data.frame': 177 obs. of 15 variables:
#> $ salary : int 1161 600 379 651 497 1067 945 1261 503 1094 ...
#> $ age : int 49 43 51 55 44 64 59 63 47 64 ...
#> $ college : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ grad : int 1 1 1 0 1 1 0 1 1 1 ...
#> $ comten : int 9 10 9 22 8 7 35 32 4 39 ...
#> $ ceoten : int 2 10 3 22 6 7 10 8 4 5 ...
#> $ sales : num 6200 283 169 1100 351 19000 536 4800 610 2900 ...
#> $ profits : int 966 48 40 -54 28 614 24 191 7 230 ...
#> $ mktval : num 23200 1100 1100 1000 387 3900 623 2100 454 3900 ...
#> $ lsalary : num 7.06 6.4 5.94 6.48 6.21 ...
#> $ lsales : num 8.73 5.65 5.13 7 5.86 ...
#> $ lmktval : num 10.05 7 7 6.91 5.96 ...
#> $ comtensq: int 81 100 81 484 64 49 1225 1024 16 1521 ...
#> $ ceotensq: int 4 100 9 484 36 49 100 64 16 25 ...
#> $ profmarg: num 15.58 16.96 23.67 -4.91 7.98 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"