The accompanying data file contains weekly data on crew size (Crew) and productivity (Output) over the past 27 weeks. A linear regression model and a quadratic regression model are considered for predicting productivity on the basis of crew size. b-1. Use the k-fold method, with k=3, to calculate the average RMSE of the two models. Note: Do not round intermediate calculations and round your final answers to 2 decimal places. b-2. Is the preferred model the same as found by the holdout method? Yes, because the average RMSE equals the RMSE with the holdout method. No, because the average RMSE does not equal the RMSE with the holdout method. Yes, because the earlier preferred model also has the lowest average RMSE. No, because the earlier preferred model does not have the lowest average RMSE. \begin{tabular}{|l|l|l|} \cline { 1 - 3 } & \multicolumn{1}{|c|}{ A } & \multicolumn{1}{c|}{ B } \\ \cline { 2 - 3 } 1 & Crew & Output \\ \hline 2 & 2 & 10 \\ \hline 3 & 3 & 12 \\ \hline 4 & 4 & 12 \\ \hline 5 & 5 & 17 \\ \hline 6 & 6 & 18 \\ \hline 7 & 7 & 18 \\ \hline 8 & 8 & 12 \\ \hline 9 & 9 & 14 \\ \hline 10 & 10 & 12 \\ \hline 11 & 2 & 12 \\ \hline 12 & 3 & 9 \\ \hline 13 & 4 & 16 \\ \hline 14 & 5 & 16 \\ \hline 15 & 6 & 17 \\ \hline 16 & 7 & 17 \\ \hline 17 & 8 & 14 \\ \hline 18 & 9 & 12 \\ \hline 19 & 10 & 11 \\ \hline 20 & 2 & 9 \\ \hline 21 & 3 & 14 \\ \hline 22 & 4 & 14 \\ \hline 23 & 5 & 16 \\ \hline 24 & 6 & 15 \\ \hline 25 & 7 & 17 \\ \hline 26 & 8 & 15 \\ \hline 27 & 9 & 10 \\ \hline 28 & 10 & 12 \\ \hline & & \\ \hline \end{tabular}