I. 'Ir'uefFalse/Uncertain - Briefly.r explain. No credit without an explanation (5 marks each). 1. 2. 3. GMSE is bigger with more Xs than the true model than with less Xs. H the exit criterion sls in automatic search is elevated, then the model ends up with less Xs in it. Hf} = 0.01 in a logit model, then for each unit increase in X, the probability that Y=1 is on average higher by 1%, ceten's puritans. . A model with 98% correct classication rate is a useful model. . With the cumulative logitI the probability to be in the second highest category Kl or higher is PIC1 = FKl 1- . lithe p-va.lue='|]. of testing Poisson vs. Negative Binomial model with the x? distribution, one should use the Poisson model. . With all observations in the same cluster, ESQ = 1. . With 500 multiple imputation samples generating 500 predictions of [I or 1 for the binary dependent variable for each observation, to combine the 500 predictions into one for each observation, one has to take the mean of all 500 predictions and use a cuto' of 50%