Multiple Choice
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.
Correct Answer:
Verified
Correct Answer:
Verified
Q1: All of the following variables are likely
Q3: A latent variable is one that:<br>A) can
Q4: Which of the following correctly summarizes one
Q5: In the standard regression formula of Y<sub>i</sub>
Q6: Which of the following limitations of the
Q7: In estimating the linear probability model for
Q8: How does the interpretation of the coefficient
Q9: After estimating a probit model for the
Q10: A shortcoming of potentially using the probit/logit
Q11: The probit model assumes what sort of