Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the adjusted cost for living rates. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: 2%; transportation: 1%; and health care: 7% ? Use a level of significance of 0.05 Click the icon to view the Cost of Living Adjustments data table. Determine the best multiple linear regression model to predict the salary as a function of the other rates. Select the best answer below and fill in the corresponding answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Salary =+1 housing +1 utilities +1 transportation + healthcare B. Salary =++ |groceries + utilities + itransportation + healthcare C. Salary =+1 |groceries + housing +1 healthcare D. Salary =+1 groceries + housing +1 transportation +1 healthcare E. Salary =+1 |groceries +1 housing +1 utilities + healthcare F. Salary =+1 housing +1 utilities + transportation G. Salary =+1 groceries +1 housing +1 utilities +1 transportation H. Salary =+1 groceries +1 housing + utilities + transportation +1 healthcare \begin{tabular}{|r|r|r|r|r|r|r|} \hline \multicolumn{1}{|c|}{ A } & \multicolumn{1}{c|}{ B } & \multicolumn{1}{c|}{ C } & \multicolumn{1}{c|}{ D } & \multicolumn{1}{c|}{ E } & \multicolumn{1}{c|}{ F } \\ \hline Comparative Salary (\$) & Groceries & Housing & Utilities & Transportation & Health Care \\ \hline 60482 & 0.15 & 0.25 & 0.1 & 0.06 & 0.05 \\ \hline 57530 & 0.08 & 0.13 & 0.01 & 0.01 & 0.06 \\ \hline 85904 & 0.16 & 1.41 & 0.45 & 0.11 & 0.33 \\ \hline 60904 & 0.18 & 0.22 & 0.11 & 0.07 & 0.1 \\ \hline 58012 & 0.11 & 0.05 & 0.02 & 0.02 & 0.06 \\ \hline 70000 & 0.23 & 0.73 & 0.01 & 0.18 & 0.02 \\ \hline 54578 & 0.01 & 0.01 & 0.05 & 0.06 & 0.01 \\ \hline 58072 & 0.12 & 0.04 & 0.03 & 0.01 & 0.05 \\ \hline 87892 & 0.2 & 2.11 & 0.06 & 0.13 & 0.03 \\ \hline 65843 & 0.07 & 0.64 & 0.08 & 0.01 & 0.11 \\ \hline 57590 & 0.02 & 0.15 & 0 & 0.06 & 0.01 \\ \hline 55120 & 0.03 & 0.04 & 0.11 & 0.06 & 0.01 \\ \hline 83795 & 0.16 & 1.69 & 0.1 & 0.29 & 0.13 \\ \hline 55602 & 0.01 & 0.02 & 0.13 & 0.05 & 0.08 \\ \hline 65060 & 0.16 & 0.43 & 0.1 & 0.1 & 0.08 \\ \hline 57530 & 0.1 & 0.24 & 0.16 & 0.05 & 0.01 \\ \hline 136024 & 0.35 & 4.77 & 0.29 & 0.3 & 0.19 \\ \hline 72048 & 0.26 & 0.72 & 0.17 & 0.1 & 0.03 \\ \hline 57651 & 0.07 & 0.21 & 0.08 & 0.02 & 0.01 \\ \hline 59578 & 0.09 & 0.21 & 0.03 & 0.14 & 0.01 \\ \hline 77349 & 0.26 & 1.08 & 0.15 & 0.19 & 0.15 \\ \hline 86446 & 0.18 & 1.87 & 0.18 & 0.27 & 0.15 \\ \hline 105241 & 0.39 & 3.02 & 0.03 & 0.26 & 0.22 \\ \hline 83253 & 0.31 & 1.33 & 0.02 & 0.21 & 0.24 \\ \hline 56084 & 0.14 & 0.09 & 0.13 & 0.01 & 0.04 \\ \hline \end{tabular} Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the adjusted cost for living rates. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: 2%; transportation: 1%; and health care: 7% ? Use a level of significance of 0.05 Click the icon to view the Cost of Living Adjustments data table. Determine the best multiple linear regression model to predict the salary as a function of the other rates. Select the best answer below and fill in the corresponding answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Salary =+1 housing +1 utilities +1 transportation + healthcare B. Salary =++ |groceries + utilities + itransportation + healthcare C. Salary =+1 |groceries + housing +1 healthcare D. Salary =+1 groceries + housing +1 transportation +1 healthcare E. Salary =+1 |groceries +1 housing +1 utilities + healthcare F. Salary =+1 housing +1 utilities + transportation G. Salary =+1 groceries +1 housing +1 utilities +1 transportation H. Salary =+1 groceries +1 housing + utilities + transportation +1 healthcare \begin{tabular}{|r|r|r|r|r|r|r|} \hline \multicolumn{1}{|c|}{ A } & \multicolumn{1}{c|}{ B } & \multicolumn{1}{c|}{ C } & \multicolumn{1}{c|}{ D } & \multicolumn{1}{c|}{ E } & \multicolumn{1}{c|}{ F } \\ \hline Comparative Salary (\$) & Groceries & Housing & Utilities & Transportation & Health Care \\ \hline 60482 & 0.15 & 0.25 & 0.1 & 0.06 & 0.05 \\ \hline 57530 & 0.08 & 0.13 & 0.01 & 0.01 & 0.06 \\ \hline 85904 & 0.16 & 1.41 & 0.45 & 0.11 & 0.33 \\ \hline 60904 & 0.18 & 0.22 & 0.11 & 0.07 & 0.1 \\ \hline 58012 & 0.11 & 0.05 & 0.02 & 0.02 & 0.06 \\ \hline 70000 & 0.23 & 0.73 & 0.01 & 0.18 & 0.02 \\ \hline 54578 & 0.01 & 0.01 & 0.05 & 0.06 & 0.01 \\ \hline 58072 & 0.12 & 0.04 & 0.03 & 0.01 & 0.05 \\ \hline 87892 & 0.2 & 2.11 & 0.06 & 0.13 & 0.03 \\ \hline 65843 & 0.07 & 0.64 & 0.08 & 0.01 & 0.11 \\ \hline 57590 & 0.02 & 0.15 & 0 & 0.06 & 0.01 \\ \hline 55120 & 0.03 & 0.04 & 0.11 & 0.06 & 0.01 \\ \hline 83795 & 0.16 & 1.69 & 0.1 & 0.29 & 0.13 \\ \hline 55602 & 0.01 & 0.02 & 0.13 & 0.05 & 0.08 \\ \hline 65060 & 0.16 & 0.43 & 0.1 & 0.1 & 0.08 \\ \hline 57530 & 0.1 & 0.24 & 0.16 & 0.05 & 0.01 \\ \hline 136024 & 0.35 & 4.77 & 0.29 & 0.3 & 0.19 \\ \hline 72048 & 0.26 & 0.72 & 0.17 & 0.1 & 0.03 \\ \hline 57651 & 0.07 & 0.21 & 0.08 & 0.02 & 0.01 \\ \hline 59578 & 0.09 & 0.21 & 0.03 & 0.14 & 0.01 \\ \hline 77349 & 0.26 & 1.08 & 0.15 & 0.19 & 0.15 \\ \hline 86446 & 0.18 & 1.87 & 0.18 & 0.27 & 0.15 \\ \hline 105241 & 0.39 & 3.02 & 0.03 & 0.26 & 0.22 \\ \hline 83253 & 0.31 & 1.33 & 0.02 & 0.21 & 0.24 \\ \hline 56084 & 0.14 & 0.09 & 0.13 & 0.01 & 0.04 \\ \hline \end{tabular}