In the probit model $\operatorname { Pr } \left( Y = 1 \mid X _ { 1 } , X _ { 2 } , \ldots , X _ { k } \right) = \Phi \left( \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { x } X _ { 2 } + \ldots + \beta _ { k } X _ { k } \right)$
A) the $\beta \text { 's }$ do not have a simple interpretation.
B) the slopes tell you the effect of a unit increase in X on the probability of Y .
C) $\beta _ { 0 }$ cannot be negative since probabilities have to lie between 0 and 1 .
D) $\beta _ { 0 }$ is the probability of observing Y when all X 's are 0 .

Question

In the probit model $\operatorname { Pr } \left( Y = 1 \mid X _ { 1 } , X _ { 2 } , \ldots , X _ { k } \right) = \Phi \left( \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { x } X _ { 2 } + \ldots + \beta _ { k } X _ { k } \right)$
A) the $\beta \text { 's }$ do not have a simple interpretation.
B) the slopes tell you the effect of a unit increase in  X  on the probability of  Y .
C)  $\beta _ { 0 }$  cannot be negative since probabilities have to lie between 0 and 1 .
D) $\beta _ { 0 }$ is the probability of observing  Y  when all  X  's are 0 .

Quizplus · Accepted Answer

The $\beta$'s in a probit model do not have a simple interpretation because they represent changes in the z-score of the standard normal distribution, not direct changes in probability.

In the Probit Model A) The β ’s \beta \text { 's }β ’s

In the Probit Model A) The $\beta \text { 's }$