A lurking variable. The effect of a lurking variable can be surprising when individuals are divided into groups. In recent years, the mean SAT score of all high school seniors has increased. But the mean SAT score has decreased for students at each level of high school grades (A, B, C, and so on). Explain how grade in?ation in high school (the lurking variable) can account for this pattern. A relationship that holds for each group within a population need not hold for would be misleading. Here are data on BMR and fat gain for the same 16 subjects whose NEA we examined earlier:
|
BMR increase (cal)
|
117
|
352
|
244 -42
|
-3
|
134
|
136
|
-32
|
|
Fat gain (kg)
|
4.2
|
3.0
|
3.7 2.7
|
3.2
|
3.6
|
2.4
|
1.3
|
|
|
|
|
|
|
|
|
|
|
BMR increase (cal)
|
-99
|
9
|
-15 -70
|
165
|
172
|
100
|
35
|
|
Fat gain (kg)
|
3.8
|
1.7
|
1.6 2.2
|
1.0
|
0.4
|
2.3
|
1.1
|
The correlation between NEA and fat gain is r = -0.7786. The slope of the regression line for predicting fat gain from NEA is b1 = -0.00344 kilogram per calorie. What are the correlation and slope for BMR and fat gain? Explain why these values show that BMR has much less effect on fat gain than does NEA.