Develop a regression model with employment years as the output variable. Do not include more than 10 input variables. Try to increase the accuracy of the model by different techniques. Write the final model equation and present a detailed arguments about how employment years can be increased. what do i choose for variables.

 

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IdNum WorkHrs Occup_n 50 1.Manag 30 7.Laborer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 40 1.Manag 45 3.Tech/Sales 50 4.Admin 56 3.Tech/Sales 40 5.Service 40 6.Prod'n 50 2.Prof'nal 30 1.Manag 32 2.Prof'nal 55 6.Prod'n 45 3.Tech/Sales 40 4.Admin 40 7.Laborer 40 5.Service 45 3.Tech/Sales 40 1.Manag 40 4.Admin 50 7.Laborer 64 3.Tech/Sales 49 4.Admin MemUnion WrkYears EmpYears NumPromo FutPromo No Union 0 1.V Likely 0 5.V Unlikely 2 5.V Unlikely 0 1.V Likely 3 1.V Likely 0 3.Not sure 64 33 23 33 60 37 25 39 35 35 49 34 50 49 39 61 59 25 20 37 24 Age Educ_Yrs Sex 35 20 Male 14 Female No Union No Union 15 Male 14 Male Yes Union 12 Female No Union 14 Male No Union 14 Female No Union 13 Female No Union 18 Male No Union 16 Male No Union 12 Female No Union 13 Male Yes Union 12 Female No Union 14 Female No Union 4 Male Yes Union 16 Male No Union 12 Female No Union 16 Male No Union 16 Male No Union 11 Male No Union 13 Female No Union 12 Female No Union 14 25 12 3 15 40 10 10 15 15 13 30 12 20 31 23 20 43 7 3 20 7 3 11 9 1.5 4 20 1.5 1.5 5 3 1.5 5 6 10 22.08 0.5 0.75 1.5 1 1 3 5 0 2.Likely 0 5.V Unlikely 0 4.Unlikely 2 3.Not sure 0 2.Likely 0 5.V Unlikely 1 1.V Likely 0 1. V Likely 0 4.Unlikely 0 1.V Likely 0 5.V Unlikely 1 2.Likely 1 1.V Likely 3 1.V Likely 0 4.Unlikely 1 1.V Likely SexPromo Awarel4 1.Better Yes 3.No Effect No 1.Better Yes 3.No Effect Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes 3.No Effect Yes 1.Better Yes 3.No Effect Yes 2.Worse Yes 1.Better Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes Yes Yes Yes Yes 1. Better 1. Better 2. Worse 2. Worse IdNum WorkHrs Occup_n 50 1.Manag 30 7.Laborer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 40 1.Manag 45 3.Tech/Sales 50 4.Admin 56 3.Tech/Sales 40 5.Service 40 6.Prod'n 50 2.Prof'nal 30 1.Manag 32 2.Prof'nal 55 6.Prod'n 45 3.Tech/Sales 40 4.Admin 40 7.Laborer 40 5.Service 45 3.Tech/Sales 40 1.Manag 40 4.Admin 50 7.Laborer 64 3.Tech/Sales 49 4.Admin MemUnion WrkYears EmpYears NumPromo FutPromo No Union 0 1.V Likely 0 5.V Unlikely 2 5.V Unlikely 0 1.V Likely 3 1.V Likely 0 3.Not sure 64 33 23 33 60 37 25 39 35 35 49 34 50 49 39 61 59 25 20 37 24 Age Educ_Yrs Sex 35 20 Male 14 Female No Union No Union 15 Male 14 Male Yes Union 12 Female No Union 14 Male No Union 14 Female No Union 13 Female No Union 18 Male No Union 16 Male No Union 12 Female No Union 13 Male Yes Union 12 Female No Union 14 Female No Union 4 Male Yes Union 16 Male No Union 12 Female No Union 16 Male No Union 16 Male No Union 11 Male No Union 13 Female No Union 12 Female No Union 14 25 12 3 15 40 10 10 15 15 13 30 12 20 31 23 20 43 7 3 20 7 3 11 9 1.5 4 20 1.5 1.5 5 3 1.5 5 6 10 22.08 0.5 0.75 1.5 1 1 3 5 0 2.Likely 0 5.V Unlikely 0 4.Unlikely 2 3.Not sure 0 2.Likely 0 5.V Unlikely 1 1.V Likely 0 1. V Likely 0 4.Unlikely 0 1.V Likely 0 5.V Unlikely 1 2.Likely 1 1.V Likely 3 1.V Likely 0 4.Unlikely 1 1.V Likely SexPromo Awarel4 1.Better Yes 3.No Effect No 1.Better Yes 3.No Effect Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes 3.No Effect Yes 1.Better Yes 3.No Effect Yes 2.Worse Yes 1.Better Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes 3.No Effect Yes 3.No Effect No 3.No Effect Yes Yes Yes Yes Yes 1. Better 1. Better 2. Worse 2. Worse

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To develop a regression model with employment years as the output variable you can use the given dataset with the following variables 1 Input Variables Age The age of the employee EducYrs The number o... View the full answer