15.29 Case # 104 has the largest value of Cook’s D(.137) but
not a very large Studentized residual (t 5 –1.88). When
we delete this case the squared multiple correlation is
increased slightly. More importantly, the standard error
of regression and the standard error of one of the pre-
dictors (PVLoss) also decrease slightly. This case is not
sufficiently extreme to have a major impact on the data.
15.31 Logistic regression on Harass.dat:
The only predictor that contributes significantly is the Of-
fensiveness of the behavior, which has a Wald of 26.43.
The exponentiation of the regression coefficient yields
0.9547. This would suggest that as the offensiveness of
the behavior increases, the likelihood of reporting de-
creases. That’s an odd result. But remember that we have
all variables in the model. If we simply predict reporting
by using Offensiveness, exp(B) 5 1.65, which means that
a 1 point increase in Offensiveness multiplies the odds of
reporting by 1.65. Obviously we have some work to do to
make sense of these data. I leave that to you.15.33 It may well be that the frequency of the behavior is tied in
with its offensiveness, which is related to the likelihood of
reporting. In fact, the correlation between those two vari-
ables is .20, which is significant at p,.000. (I think my
explanation would be more convincing if Frequency were
a significant predictor when used on its own.)
15.35 BlamPer and BlamBeh are correlated at a moderate
level (r 5 .52), and once we condition on BlamPer by
including it in the equation, there is little left for Blam-
Beh to explain.
15.37 This problem required you to make up an example.
15.39 It is impossible to change one of the variables without
changing the interaction in which that variable plays a
role. I can’t think of a sensible interpretation of “hold-
ing all other variables constant” in this situation.
Chapter 16
16.1 Source df SS MS F
Group 2 57.733 28.867 9.312*
Error 12 37.200 3.100
Total 14 94.933
* p,.05 [F.02(2, 12) 5 3.89]16.3 (a) Source df SS MS F
Group 2 79.0095 39.5048 14.92*
Error 18 47.6571 2.6476
Total 20 126.6666
** p,.05 [F.02(2, 18) 5 3.55]x^216.5 Source df SS MS F
Gender 1 65.333 65.333 7.730*
SES 2 338.667 169.333 20.034*
G 3 S 2 18.667 9.333 1.104
Error 42 355.000 8.452
Total 47 777.667
* p,.05 [F.05(1, 35) 5 3.27]
16.7 Source df SS MS F
Gender 1 60.015 60.015 7.21*
SES 2 346.389 173.195 20.80*
G 3 S 2 21.095 10.547 1.27
Error 35 291.467 8.328
Total 40
* p,.05 [F.05(1, 35) 5 4.12; F.05(2, 35) 5 3.27]
16.916.11 If we are actually dealing with unweighted means, SSA
and SSBwill be 0 because means of means are 7 for all
rows and columns.
16.1316.15 (a) Design matrix using only the first entry in each
group for illustration purposes:(b)
Source df SS MS F
Covariate 1 1250.6779 1250.6779 55.81*
A(Group) 2 652.9228 326.4614 14.57*
Error 11 246.5221 22.4111
Total 14 2615.733
* p,.05 [F.05(1, 11) 5 4.84; F.05(2, 11) 5 3.98]X= E1 0 58 75
... ... ... ...
0 1 60 70
... ... ... ...
21 217580Ub 2 =-0.167; ab 11 =0.833; ab 12 =-0.167mN =13.4167; a 1 =1.167; b 1 =-3.167752 Answers
SS(total)SS(error)SESSexS×S