Assume for a moment that you’re estimating a
model with the natural log of Major League Baseball players’
contract value as the dependent variable and several player
characteristics as independent variables. Three-year averages
for slugging percentages (slg_3_avg) and at-bats (ab_3_avg),
age, and tenure (the number of years a player has been with
his current team) are the independent variables. You can
arbitrarily divide the sample by the average number of at-bats.
Players in Group A have below-average at-bats, and players
in Group B have above-average at-bats. The F-statistic in
Figure 11-7, which shows the process of performing a GQ
test in STATA, suggests that the difference in the RSS for the
two groups is marginally significant in a one-tailed test
(p-value = 0.0730).