For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact onsatisfaction?E Click the icon to view the job satisfaction data.Determine the regression model for overall satisfaction as a function of years of service and department that has the largest R. Let "Administrative" be the baseline department, let X, represent Maintenance, let X, represent Management, let X, represent Production, let X, represent Quality Control, and let X5 representShipping / Receiving, coding each department variable with a 1 if the person is in that department and 0 otherwise. In addition, let X, represent Years. Enter the terms of the equation so that the X,-values are in ascending numerical order by base. Select the correct choice below and fill in any answer boxes within yourchoice(Type integers or decimals rounded to three decimal places as needed.)O A.O B.X, +0X, +X4+(Xs +(XОС.+(xOD.+ )x-O E.OF. For the provided data, develop a regression model for overall satisfaction as a function of years of service and department that has the largest R. Note that the categorical variable department has multiple levels and will require the use of multiple dummy variables. Which department, if any, has the highest impact onMaintenancesatisfaction?E Click the icon to view the job satisfaction data.Job satisfaction dataDetermine the regression model for overall satisfaction as a function of years of seht Production, let X, represent Quality Control, and let X, representthe correct choice below and fill in any answer boxes within yourWorkTools andWork/LifeShipping / Receiving, coding each department variable with a 1 if the person is int모SatisfactionYears Ideas Communication Recognition TrainingDepartmentManagementConditionsInformationBalancechoice(Type integers or decimals rounded to three decimal places as needed.)Administrative16Production1625O A.+( X+( xProduction4Quality Control10OB.+ ()X,X2 +X, +Xg +( X69MaintenanceProductionProductionQuality Control915OC.+ ( X87ProductionProduction10OD.+ (X+4Production8ProductionProductionO E.78Production7OF.+( XShipping / ReceivingProductionProductionProductionManagement2187Productionning/ ReceivingShippingManagementAdministrativeProductionShippingonReceivingProductionShipping / Receiving10ProductionProductionAdministrative1446.Shipping / Receiving184.4.

Modify the data as follows: Department Satisfaction X1 X2 X3 X4 X5 X6 Management 9 0 1 0 0 0 1 Administrative 3 0 0 0 0 0 16 Production 5 0 0 1 0 0 16 Production 4 0 0 1 0 0 15 Quality Control 10 0 0 0 1 0 1 Maintenance 8 1 0 0 0 0 17 Maintenance 9 1 0 0 0 0 15 Production 8 0 0 1 0 0 13 Quality Control 7 0 0 0 1 0 11 Production 10 0 0 1 0 0 3 Production 4 0 0 1 0 0 6 Production 9 0 0 1 0 0 1 Production 7 0 0 1 0 0 3 Production 9 0 0 1 0 0 2 Production 6 0 0 1 0 0 3 Shipping/Receiving 6 0 0 0 0 1 21 Production 6 0 0 1 0 0 2 Production 7 0 0 1 0 0 2 Production 7 0 0 1 0 0 15 Management 8 0 1 0 0 0 3 Production 7 0 0 1 0 0 5 Shipping/Receiving 4 0 0 0 0 1 8 Management 3 0 1 0 0 0 3 Administrative 9 0 0 0 0 0 2 Production 8 0 0 1 0 0 8 Shipping/Receiving 5 0 0 0 0 1 32 Production 6 0 0 1 0 0 17 Shipping/Receiving 9 0 0 0 0 1 2 Production 9 0 0 1 0 0 15 Production 3 0 0 1 0 0 5 Administrative 6 0 0 0 0 0 14 Shipping / Receiving 8 0 0 0 0 0 18In Excel, go to Data --> Data Analysis --> Regression and enter the details as follows: Step 2 Result: SUMMARY OUTPUT Regression Statistics Multiple R 0.45061 R Square 0.20305 Adjusted R Square 0.01178 Standard Error 2.09908 Observations 32 ANOVA df SS MS F Significance F Regression 6 28.06501526 4.677502543 1.061585101 0.411186669 Residual 25 110.1537347 4.40614939 Total 31 138.21875 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 7.768 1.274 6.097 0.000 5.144 10.391 X1 2.355 1.829 1.287 0.210 -1.412 6.122 X2 -0.864 1.707 -0.506 0.617 -4.381 2.652 X3 -0.221 1.199 -0.185 0.855 -2.691 2.248 X4 1.341 1.856 0.722 0.477 -2.482 5.164 X5 -0.170 1.496 -0.114 0.910 -3.252 2.911 X6 -0.101 0.058 -1.755 0.092 -0.220 0.018 So, the regression equation is as follows: Y = 7.768 + 2.355 X1 + (-0.864) X2 + (-0.221) X3 + 1.341 X4 + (-0.170) X5 + (-0.101) X6 The highest impact (i.e. the highest slope value 2.355) is for X1 i.e. Maintenance