You would like to find the effect of gender and marital status on earnings. As a

Question

You would like to find the effect of gender and marital status on earnings. As a result, you consider running the following regression: ahei= 0 + 1×DFemmei + 2×DMarri + 3×DSinglei + ui Where ahe is average hourly earnings, DFemme is a binary variable which takes on the value of "1" if the individual is a female and is "0" otherwise, DMarr is a binary variable which takes on the value of "1" if the individual is married and is "0" otherwise, DSingle takes on the value of "1" if the individual is not married and is "0" otherwise. The regression program which you are using returns a message that the equation cannot be estimated. Why do you think that is? What should you do?

Explanation / Answer

All IV should not be categorical, you need continuous variables which moderately correlate with DV.

Actually, linear regression with binary predictors is mathematically equivalent to ANOVA. You can even get back the domain means of the predictors just by adding the coefficient for the intercept to the coefficient for the predictor. If you remove the intercept then most statistical software will adjust the regression formula to be non-singular for you. ANOVA is adapted to the specific case where you have binary predictors so you will have more features to play with, but you would want the quick and dirty approach just to see the results of the F-test and some t-tests, throw it into a linear regression.

For example, linear regression is used when the dependent variable is continuous, logistic regression when the dependent is categorical with 2 categories, and multinomi(n)al regression when the dependent is categorical with more than 2 categories. The predictors can be anything (nominal or ordinal categorical, or continuous, or a mix).

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