In the Case of Regression with Interactions, the Coefficient of a Binary

Multiple Choice

In the case of regression with interactions, the coefficient of a binary variable should be interpreted as follows:

A) there are really problems in interpreting these, since the ln(0) is not defined.
B) for the case of interacted regressors, the binary variable coefficient represents the various intercepts for the case when the binary variable equals one.
C) first set all explanatory variables to one, with the exception of the binary variables. Then allow for each of the binary variables to take on the value of one sequentially. The resulting predicted value indicates the effect of the binary variable.
D) first compute the expected values of Y for each possible case described by the set of binary variables. Next compare these expected values. Each coefficient can then be expressed either as an expected value or as the difference between two or more expected values.