Exam 9: Prediction for a Dichotomous Variable

If you are modeling shopping decisions at the grocery store, and construct a control variable that is coded as 0 for not a store loyalty program member, 1 for an individual store loyalty program member, and 2 for a family store loyalty program member, it would be appropriate to model this as limited dependent variable because:

Which of the following statistics or conditions has an interpretation in the linear probability model that is unique/distinct to the standard linear regression model?

If one was attempting to build a model that predicted a person's height-which has to be positive, why might it still be the case that you wouldn't consider this as a limited dependent variable?

Aside from the sample being a random sample from the target population, and the latent variable model determining function is correct, what critical assumption guarantees the consistency of the maximum likelihood probit and logit estimates for the coefficients?

Estimating a probit or logit model via maximum likelihood involves all of the following except for what?

A potential shortcoming of the probit/logit models is that to get consistent estimates of the parameters:

A dependent variable whose range of possible values has consequential constraints is known as a(n):

A consequence of the linear probability model always having heteroscedastic errors will be which of the following?

In the event that you are modeling weekly advertising spend (as a dependent variable), and you notice that several weeks have no spending-it is likely your analysis will have to handle what?

For a linear probability model all of the following are true except for which condition?