Introduction to Stata 8 - (GRIPS

12. Regression analysis
Performing regression analysis with Stata is easy. Defining regression models that give sense
is more complex. Especially consider:
• If you look for causes, make sure your model is meaningful. Don't include independent
variables that represent steps in the causal pathway; it may create more confounding
than it prevents. Automatic selection procedures are available in Stata (see [R] sw), but
they may seduce the user to non-thinking. I will not describe them.
• If your hypothesis is non-causal and you only look for predictors, logical requirements
are more relaxed. But make sure you really are looking at predictors, not consequences
of the outcome.
• Take care with closely associated independent variables, e.g. education and social class.
Including both may obscure more than illuminate.
12.1. Linear regression
regress
A standard linear regression with bmi as the dependent variable:
regress bmi sex age
[R] regress
xi:
[R] xi
The xi: prefix handles categorical variables in regression models. From a five-level
categorical variable xi: generates four indicator variables; in the regression model they are
referred to by the i. prefix to the original variable name:
xi: regress bmi sex i.agegrp
You may also use xi: to include interaction terms:
xi: regress bmi age i.sex i.treat i.treat*i.sex
By default the first (lowest) category will be omitted, i.e. be the reference group. You may,
before the analysis, select agegrp 3 to be the reference by defining a 'characteristic':
char agegrp[omit] 3
predict
[R] predict
After a regression analysis you may generate predicted values from the regression
coefficients, and this may be used for studying residuals:
regress bmi sex age
predict pbmi
generate rbmi = bmi-pbmi
scatter rbmi pbmi
or use rvfplot, see below
Regression diagnostics
[R] Regression diagnostics
The chapter is very instructive. Get a residual plot with a horizontal reference line by:
rvfplot , yline(0)
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