![]() ![]() regress weight age1 age2 height age1ht age2ht This test will have 2 df because it compares three regression coefficients. generate age1 = 0Īge1ht and age2ht as predictors in the regression equation That is coded 1 if middle aged (age=2), 0 otherwise. To do this analysis, we first make a dummy variable calledĪge1 that is coded 1 if young (age=1), 0 otherwise, and age2 Is the regression for the middle aged, and B 3 is the Where B 1 is the regression for the young, B 2 We can compare the regression coefficients among these three age groups to test the null hypothesis Significance tests to be able to make claims about the differences among these regression coefficients. However, we would need to perform specific Seem to suggest that height does not predict weight as stronglyįor the young (-.37) as for the middle aged and seniors. Of weight for seniors (3.18) than for the middle aged (2.09). The parameter estimates (coefficients) for the young, middle age, and senior citizens are shownīelow, and the results do seem to suggest that height is a stronger predictor We analyze their data separately using the regress command below after first sorting by age. The variable age indicates the age groupĪnd is coded 1 for young people, 2 for middle aged, and 3 for senior citizens. Young people, 10 fictional middle age people, and 10 fictional senior citizens, along with their Below, we have a data file with 10 fictional Would differ across 3 age groups (young, middle age, senior citizen). Sometimes your research may predict that the size of a regression coefficient may vary across groups.įor example, you might believe that the regression coefficient of height predicting ![]()
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