Is the regression coefficient vector B E R identifiable in a linear regression subjects model with the following design matrix? 1 1 1 2 X = 1 0 2 1 O 2 O 2 O We would need to know the response vector to determine if B is identifiable. O There is not enough information given to know if B is identifiable. O Yes O NoWhat does the linear regression subjects model assume about the data? O The response and predictors are related. O Let y; be the measured response for the ith subject and let x; = (1, T;2, . . ., Dip)' have the values of the predictors for the ith subject for i E {1, . .., n}. The linear regression model assumes that y is a realization of the random variable Yi = Baits, ie{1, ...,n}, where B E RP is an unknown vector of regression coefficients and 81, . . ., En are independent and identically distributed with mean zero and unknown standard deviation O E (0, DO ) . O The response is normal and the predictors are normal and independent. The response and predictors are normal.A linear regression model with a 40 row by 5 column design matrix, which has linearly independent columns, was t to a dataset and its residual sum of squares was 2123191 What is the BIC value for this tted model? 45.?2636 There is not enough information given to answer this question. 188.2889 123.3422 Suppose that our R workspace has a design matrix X, which has 20 rows and 3 columns, which are linearly independent. Also suppose that our R workspace has a 20 entry measured response vector y, which is assumed to be a realization of the random vector Y = X/ 4 (61, . . ..620)' where B = (81, 82, 83)' is an unknown regression coefficient vector and 61, ...; 620 are iid with mean zero and unknown variance of. Which of the following R code computes and prints an unbiased estimate of o?? beta. hat=qr . coef(qr(X), y=y) residuals=y-XX*%beta. hat rss=sum(residuals*2) sigma . hat . sq=rss/19 print (sigma . hat . 5q) beta. hat=qr . coef(qr(X), y=y) residuals=y-XX**beta. hat rss=sum(residuals*2) sigma . hat . sq=rs5/17 print (sigma . hat . 5q) reps=1e4 SE . 5q=numeric (reps) qrx=qr (x=x) for (r in 1:reps) y=XX*%beta + rnorm(n=20, mean=0, sd=sigma) beta. hat=qr . coef(qrX, y=y) sE . sq[r ]=sum( (y-xx**beta. hat)^2)/19 print (sE. 5q) O reps=1e4 SE . 5q=numeric (reps) qrx=qr (x=x) for (r in 1:reps) y=XX**beta + rnorm(n=20, mean=0, sd=sigma) beta. hat=qr . coef(qrX, y=y) SE. sq[r]=sum((y-xx**beta. hat)*2)/17 O print (sE. 5q)