datawithstata

Module 4: Regression with Dummy Variables STATA playlist Open the full YouTube playlist Dummy variables are essential whenever categorical predictors appear in a model, including sex or gender, hospital type, race or ethnicity, treatment groups, or place of residence. This module explains how to include those predictors correctly and why one category must be omitted and treated as the reference group. Students learn the dummy variable trap, the logic of the baseline category, and how to interpret coefficients as differences relative to that reference group. Key points Use one fewer dummy than the total number of categories. The omitted category becomes the reference group. Interpretation is always relative to that baseline. STATA examples tab group, gen(d_) regress y x1 d_2 d_3 regress y i.group x1

Module 4: Regression with Dummy Variables STATA playlist Open the full YouTube playlist Dummy variables are essential whenever categorical predictors appear in a model, including sex or gender, hospital type, race or ethnicity, treatment groups, or place of residence. This module explains how to include those predictors correctly and why one category must be omitted and treated as the reference group. Students learn the dummy variable trap, the logic of the baseline category, and how to interpret coefficients as differences relative to that reference group. Key points Use one fewer dummy than the total number of categories. The omitted category becomes the reference group. Interpretation is always relative to that baseline. STATA examples tab group, gen(d_) regress y x1 d_2 d_3 regress y i.group x1