8. In the linear probability model, the interpretation of the slope coefficient is A. the change in odds associated with a unit change in X, holding other regressors constant. B. not all that meaningful since the dependent variable is either 0 or 1. C. the change in probability that Y=1 associated with a unit change in X, holding others regressors constant. D. the response in the dependent variable to a percentage change in the regressor. E. None of the above 9. The linear probability model is A. the application of the multiple regression model with a continuous left-hand side variable and a binary variable as at least one of the regressors. B. an example of probit estimation. C. another word for logit estimation. D. the application of the linear multiple regression model to a binary dependent variable. E. None of the above 10. In the binary dependent variable model, a predicted value of 0.6 means that A. the most likely value the dependent variable will take on is 60 percent. B. given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one. C. the model makes little sense, since the dependent variable can only be 0 or 1. D. given the values for the explanatory variables, there is a 40 percent probability that the dependent variable will equal one. E. None of the above 11. The probit model A. is the same as the logit model. B. always gives the same fit for the predicted values as the linear probability model for values betw 0.1 and 0.9. C. forces the predicted values to lie between 0 and 1. D. should not be used since it is too complicated. E. None of the above Scanned by CamScanner re multiple regression nodel, the adjusted R²: A. commot be negative. B. will never be greater than the regression R2. C. equals the square of the correlation coefficient r. D. cannot decrease when an additional explanatory variable is added. E. None of the above 13. A term used to describe the case when the independent variables in a multiple regression model are correlat