1. True/False. Please read each statement and put T(True)/F(False) in the begin- ning. Notice: the standard least squares estimator is applied whenever needed in the statements. Define the standard multiple linear regression model as follows: Yi = Bo+ BIXa+ .. . + BpXip +Ei, where &; ~Lid. N(0, 02 ). (a) A coefficient of determination zero indicates that X and Y are not related. (b) A high coefficient of determination indicates that the estimated re- gression line is a good fit. (c) If two multiple linear regression models have the same mean squared error (MSE), we prefer the model with less variables. (d) For any F-test associated with the multiple linear regression model, we can find an equivalent t-test. (e) In a standard multiple linear regression model, the variance of the prediction becomes larger as X, deviates from the sample mean Xj (f) In a standard multiple linear regression model, define the residuals to be e; = Yi - Ya, we have EleiXij =0 for all j = 1, ...,p - 1. (g) In a standard multiple linear regression model, the prediction for a new observation with predictors X (new) = (X1, X2, . .., Xp) isY= n )t, Yi, where Xj = n-EL_, Xji,j =1, ...,p is the sample mean