The binary variable interaction regression

(A) can only be applied when there are two binary variables, but not three or more.

(B) is the same as testing for differences in means.

(C) cannot be used with logarithmic regression functions because log (0) is not defined.

(D) allows the effect of changing one of the binary independent variables to depend on the value of the other binary variable.

Question

Bridget Gardner

Mathematics

28 October, 16:46

The binary variable interaction regression

(A) can only be applied when there are two binary variables, but not three or more.

(B) is the same as testing for differences in means.

(C) cannot be used with logarithmic regression functions because log (0) is not defined.

(D) allows the effect of changing one of the binary independent variables to depend on the value of the other binary variable.

+5

Answers (1)

Know the Answer?

Madden Fritz · Answer 1

The correct option is D

Step-by-step explanation:

The correct option is D

The regression of binary variable interaction enables one of binary independent variables to be modified based on value of other binary variable.

In regression, whenever the impact of an independent variable on the dependent variable reforms, based on a value (s) of one or more than one independent variables, an interaction impact exists.