Linear regression analysis

3.

Consider a multiple linear regression model with standard assumptions and notations. If we overfit the model, i.e., fit more variables than the ones in the true model, the resulting least squares estimators of the regression parameters (A) are not linear functions of Y. (B) are biased for the corresponding true parameters. (C) have larger variances than the variances of the least squares estimators obtained by fitting the true model. (D) All of the above.CDnsider a binary response variable 1"}, which takes values 1 with probability a]: and {l 1with probability 1 erg. Suppose we use the model Y; = r} + lxli + 1 to model Y as a function of an explanatory variable X ; let E(E{) = . Which of the following is FALSE about this model? (A) Variance of 1: depends of X}. [B] The distribution of 63' is Bernoulli. [C] n + ,BIXH is constrained to lie between {I and 1. [D] The ith mean response is constrained to be s'dl in]