A subsample of a large data set (from the early 1980s) from a study 
investigating potential sex effects (biases?) in determination of 
professional salary differentials. The individuals come from several
large corporations. Data columns are: 

1) Number of individual
2) Management Level: 1=Lower, 2=Upper, 
3) Sex: 1=Male, 2=Female
4) Education level: 1=High School, 2=1st Degree, 3=Graduate Degree
5) Years in job
6) Annual Salary 

  1 1 1 1  6 12336
  2 1 1 1  8 13670
  3 1 1 1 10 14468
  4 1 1 1 13 15990
  5 1 1 1 16 18000
  6 1 1 1 20 20900
  7 1 1 2  1 11283
  8 1 1 2  3 12313
  9 1 1 2  4 12884
 10 1 1 2  5 13245
 11 1 1 2  6 13829
 12 1 1 2 10 15542
 13 1 1 2 12 16882
 14 1 1 2 14 18100
 15 1 1 2 16 19800
 16 1 1 2 17 20904
 17 1 1 3  1 11608
 18 1 1 3  2 12195
 19 1 1 3  4 13231
 20 1 1 3  5 13677
 21 1 2 1  2 10535
 22 1 2 1  4 11417
 23 1 2 1 11 14861
 24 1 2 2  2 11772
 25 1 2 2  8 15305
 26 1 2 3  1 11767
 27 2 1 1  1 13876
 28 2 1 1  3 14975
 29 2 1 1  7 16498
 30 2 1 1  8 17900
 31 2 1 2  2 22456
 32 2 1 2  6 22885
 33 2 1 2 13 29300
 34 2 1 2 16 29500
 35 2 1 3  3 18998
 36 2 1 3  6 23521
 37 2 1 3  8 21900
 38 2 1 3 10 25242
 39 2 1 3 12 23981
 40 2 1 3 15 27450
 41 2 2 1  5 16465
 42 2 2 2  3 22371
 43 2 2 3  1 19801
 44 2 2 3  4 22269