The primary distinction between a linear probability model and linear regression model is:
A) you can't interpret the coefficients in a linear probability model.
B) a linear probability model never suffers from heteroscedasticity.
C) the standard errors of a linear probability model will be larger.
D) the outcome is dichotomous in a linear probability model.

Question

Quizplus · Accepted Answer

The primary distinction between a linear probability model and a linear regression model is that the outcome variable in a linear probability model is dichotomous, while in a linear regression model, it is continuous. Therefore, the correct answer is D. The other options are incorrect. While it is true that interpreting coefficients in a linear probability model may not always be straightforward, it is not impossible (A). Similarly, while a linear probability model may be less robust to heteroscedasticity than a linear regression model, it is not true to say that it never suffers from it (B). Additionally, the standard errors of a linear probability model may not always be larger than those of a linear regression model (C).

The Primary Distinction Between a Linear Probability Model and Linear