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.
Question 2
(Requires Calculus) In the multiple regression model you estimate the effect on Yi of a unit change in one of the Xi while holding all other regressors constant. This
A) makes little sense, because in the real world all other variables change.
B) corresponds to the economic principle of mutatis mutandis.
C) leaves the formula for the coefficient in the single explanatory variable case unaffected.
D) corresponds to taking a partial derivative in mathematics.
Variable expressivity
Types of Interactions
Clinically Significant Drug-Drug Interactions in Dentistry
The phenotype of an organism is the product of interactions
Cross-regulatory interactions among gap genes
Separation of variable number tandem repeat (VNTR) alleles after PCR amplification