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: 3%; housing: 9%; utilities: 1%; transportation: 2%; and health care: 8%? 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.) O A. Salary = + () groceries + () housing + () healthcare O B. Salary = + () groceries + ()housing + () transportation + ()healthcare O C. Salary = + ()housing + () utilities + () transportation + () healthcare O D. Salary = + () groceries + ()housing + ( )utilities + ( ) healthcare O E. Salary = + ()groceries + ()housing + ()utilities + () transportation + (healthcare OF. Salary = + () groceries + ( )utilities + ( ) transportation + ()healthcare O G. Salary = + () groceries + () housing + ( ) utilities + () transportation O H. Salary = + ()housing + ( ) utilities + () transportationComparative Salary ($) Groceries Housing Utilities Transportation Health Care 60482 0.15 0.25 0.1 0.06 0.05 57530 -0.08 0.13 0.01 0.01 0.06 85904 0.16 1.41 0.45 0.11 0.33 60904 0.18 0.22 0.11 -0.07 0.1 58012 0.11 0.05 0.02 -0.02 0.06 70000 0.23 0.73 0.01 0.18 0.02 54578 0.01 -0.01 -0.05 -0.06 -0.01 58072 0.12 -0.04 -0.03 0.01 0.05 87892 0.2 2.18 -0.06 0.13 -0.03 65843 0.07 0.64 -0.08 0.01 0.11 57590 -0.02 0.15 0 0.06 -0.01 55120 0.03 0.04 -0.11 0.06 0.01 83795 0.16 1.62 0.1 0.29 0.13 55602 0.01 0.02 0.13 0.05 -0.08 65060 0.16 0.43 -0.1 0.1 0.08 57530 0.1 0.24 -0.19 -0.05 0.01 136024 0.38 4.75 0.23 0.3 0.19 72048 0.26 0.72 0.17 0.1 0.03 57651 0.07 0.21 0.08 0.02 0.01 59578 0.09 0.21 0.03 0.14 -0.01 77349 0.23 1.08 -0.12 0.19 0.15 86446 0.18 1.87 0.18 0.27 0.15 105241 0.37 3.06 0.03 0.26 0.22 83253 0.35 1.33 0.02 0.21 0.24 56084 0.14 -0.09 0.13 0.01 0.04