On excel, run a regression on the data and analyze the results into a parsimonious model, perferably with significant predictor variables that don't exist multicollinearity. show both regression and parsimonious model seaperate.

what information would be needed for the sum?

3. Using my _Regression Project spreadsheet to assist, run a regression on your data, but analyze the results and work your way toward a parsimonious model, preferably with significant predictor variables which don't exhibit multicollinearity. Name your spreadsheet Last Name FirstName Data Types Sort & Filter Data Tools Forecast Analysis H K. - M N A Tulsa Bank needs to hire additional employees and wants to first get a baseline of starting salaries which were paid in recent years. Variable Description Salary Monthly Starting Salary of Employees at a Tulsa Bank Years of Education at the Time of Hire Educ ExperTotal ExperBank Number of Months of Previous Work Experience at time of hire Number of Months of Previous Work Experience in the Banking Industry at time of hire Time Number of Months since January 1, 2015 when employee was hired (Inflactionary Factor) ENG 6/1 A 1 ID Salary Educ ExperTotal ExperBank Time 3900 15 0 0 2 1 1 3 2. 4020 13 44 40 7 4 3 4290 15 5 5 30 5 4 4380 11 7 AT 6 5 4380 11 8 00 6 wh 7 6. 4380 15 0 0 8 7 4380 15 0 0 10 Var 9 8 4380 15 5 5 6 Sala 10 9 4440 18 75 60 2 Edu 11 10 4500 11 52 45 3 Exp 12. 11 4500 15 8 6 19 Exp 13 12 4620 15 52 40 3 Tim 14 13 4800 11 70 60 20 14 4800 15 6 6 15 16 23 15 4800 15 11 11 12 17 16 4800 15 11 11 17 18 17 4800 15 63 49 22 18 4800 15 144 144 24 19 4800 15 163 150 12 20 4800 19 20 21 22. 23 24 15 228 200 26 21 4800 15 381 350 1 22 4800 19 214 214 15 23 4980 318 25 11 results 318 Data + A B C D E F G 1 ID Salary Educ ExperTotal ExperBank Time 24 23 4980 11 318 318 25 25 24 5100 11 96 80 33 26 25 5100 15 36 36 15 27 26 5100 15 59 50 14 28 27 5100 18 115 100 1 29 28 5100 18 165 155 4 30 29 5100 19 123 123 12 31 30 5160 15 18 18 12 32 31 5220 11 102 95 29 33 32 5220 15 127 110 29 34 33 5280 11 90 90 11 35 34 5280 11 190 170 1 36 35 5280 15 107 100 11 37 36 5400 11 173 173 34 38 37 5400 11 228 228 33 39 38 5400 15 26 26 11 40 39 5400 15 36 36 33 41 40 5400 15 38 36 22 42 41 15 82 74 29 43 42 5400 15 169 160 27 44 43 5400 15 244 232 45 44 5400 18 24 24 13 46 45 5400 18 49 49 27 results Data 5400 1 Ready Types H1 X A B D E F G 1 JID Salary Educ ExperTotal ExperBank Time 46 45 5400 18 49 49 27 47 46 5400 18 51 40 21 48 47 5400 18 122 122 33 49 48 5520 15 97 97 17 50 49 5520 15 196 150 32 51 50 5580 15 133 133 30 52 51 5640 15 55 55 9 53 52 5700 15 90 82 23 54 53 5700 15 117 110 25 55 54 5700 18 51 51 17 56 55 5700 18 61 61 11 57 56 5700 18 241 34 58 57 6000 15 121 121 30 59 58 6000 18 79 65 13 60 59 6120 15 209 200 21 61 60 6300 15 87 87 33 62 61 6300 18 231 231 15 63 62 4620 15 12 12 22 64 63 5040 18 14 14 3 65 64 5100 15 180 152 15 66 65 5100 15 315 290 2 67 66 5220 15 29 29 14 68 67 5400 15 7 7 21 results Data 241 + Ready HH Data All Types Filte Get & Transform... Queries & Co... Data Types H1 E B D F G ID Salary Educ ExperTotal ExperBank Time 67 5400 15 7 7 21 68 5400 15 38 38 11 69 5400 15 113 95 3 70 5400 18 18 18 8 71 5400 18 359 350 11 68 69 70 71 72 73 74 75 76 77 78 72 5700 18 36 36 5 73 6000 11 320 320 21 74 6000 15 24 24 2 75 6000 15 32 32 17 76 6000 15 49 49 8 77 6000 15 56 50 33 78 6000 15 252 245 11 79 6000 15 272 272 19 80 6000 18 25 25 13 81 6000 18 36 36 32 82 6000 18 56 56 12 83 6000 18 64 64 33 79 80 81 82 83 84 85 86 87 88 89 90 84 6000 18 108 90 16 85 6000 19 46 46 3 86 6300 18 72 66 17 87 6600 18 64 64 16 88 6600 18 84 84 33 89 6600 200 16 18 results 216 Data H Ready A B C D E F G ID Salary Educ ExperTotal ExperBank Time 1 90 89 6600 18 216 200 16 90 6840 18 42 42 7 91 92 91 6900 15 175 175 10 92 6900 18 132 132 24 93 94 95 93 8100 19 55 55 33 96 3. Using my _Regression Project spreadsheet to assist, run a regression on your data, but analyze the results and work your way toward a parsimonious model, preferably with significant predictor variables which don't exhibit multicollinearity. Name your spreadsheet Last Name FirstName Data Types Sort & Filter Data Tools Forecast Analysis H K. - M N A Tulsa Bank needs to hire additional employees and wants to first get a baseline of starting salaries which were paid in recent years. Variable Description Salary Monthly Starting Salary of Employees at a Tulsa Bank Years of Education at the Time of Hire Educ ExperTotal ExperBank Number of Months of Previous Work Experience at time of hire Number of Months of Previous Work Experience in the Banking Industry at time of hire Time Number of Months since January 1, 2015 when employee was hired (Inflactionary Factor) ENG 6/1 A 1 ID Salary Educ ExperTotal ExperBank Time 3900 15 0 0 2 1 1 3 2. 4020 13 44 40 7 4 3 4290 15 5 5 30 5 4 4380 11 7 AT 6 5 4380 11 8 00 6 wh 7 6. 4380 15 0 0 8 7 4380 15 0 0 10 Var 9 8 4380 15 5 5 6 Sala 10 9 4440 18 75 60 2 Edu 11 10 4500 11 52 45 3 Exp 12. 11 4500 15 8 6 19 Exp 13 12 4620 15 52 40 3 Tim 14 13 4800 11 70 60 20 14 4800 15 6 6 15 16 23 15 4800 15 11 11 12 17 16 4800 15 11 11 17 18 17 4800 15 63 49 22 18 4800 15 144 144 24 19 4800 15 163 150 12 20 4800 19 20 21 22. 23 24 15 228 200 26 21 4800 15 381 350 1 22 4800 19 214 214 15 23 4980 318 25 11 results 318 Data + A B C D E F G 1 ID Salary Educ ExperTotal ExperBank Time 24 23 4980 11 318 318 25 25 24 5100 11 96 80 33 26 25 5100 15 36 36 15 27 26 5100 15 59 50 14 28 27 5100 18 115 100 1 29 28 5100 18 165 155 4 30 29 5100 19 123 123 12 31 30 5160 15 18 18 12 32 31 5220 11 102 95 29 33 32 5220 15 127 110 29 34 33 5280 11 90 90 11 35 34 5280 11 190 170 1 36 35 5280 15 107 100 11 37 36 5400 11 173 173 34 38 37 5400 11 228 228 33 39 38 5400 15 26 26 11 40 39 5400 15 36 36 33 41 40 5400 15 38 36 22 42 41 15 82 74 29 43 42 5400 15 169 160 27 44 43 5400 15 244 232 45 44 5400 18 24 24 13 46 45 5400 18 49 49 27 results Data 5400 1 Ready Types H1 X A B D E F G 1 JID Salary Educ ExperTotal ExperBank Time 46 45 5400 18 49 49 27 47 46 5400 18 51 40 21 48 47 5400 18 122 122 33 49 48 5520 15 97 97 17 50 49 5520 15 196 150 32 51 50 5580 15 133 133 30 52 51 5640 15 55 55 9 53 52 5700 15 90 82 23 54 53 5700 15 117 110 25 55 54 5700 18 51 51 17 56 55 5700 18 61 61 11 57 56 5700 18 241 34 58 57 6000 15 121 121 30 59 58 6000 18 79 65 13 60 59 6120 15 209 200 21 61 60 6300 15 87 87 33 62 61 6300 18 231 231 15 63 62 4620 15 12 12 22 64 63 5040 18 14 14 3 65 64 5100 15 180 152 15 66 65 5100 15 315 290 2 67 66 5220 15 29 29 14 68 67 5400 15 7 7 21 results Data 241 + Ready HH Data All Types Filte Get & Transform... Queries & Co... Data Types H1 E B D F G ID Salary Educ ExperTotal ExperBank Time 67 5400 15 7 7 21 68 5400 15 38 38 11 69 5400 15 113 95 3 70 5400 18 18 18 8 71 5400 18 359 350 11 68 69 70 71 72 73 74 75 76 77 78 72 5700 18 36 36 5 73 6000 11 320 320 21 74 6000 15 24 24 2 75 6000 15 32 32 17 76 6000 15 49 49 8 77 6000 15 56 50 33 78 6000 15 252 245 11 79 6000 15 272 272 19 80 6000 18 25 25 13 81 6000 18 36 36 32 82 6000 18 56 56 12 83 6000 18 64 64 33 79 80 81 82 83 84 85 86 87 88 89 90 84 6000 18 108 90 16 85 6000 19 46 46 3 86 6300 18 72 66 17 87 6600 18 64 64 16 88 6600 18 84 84 33 89 6600 200 16 18 results 216 Data H Ready A B C D E F G ID Salary Educ ExperTotal ExperBank Time 1 90 89 6600 18 216 200 16 90 6840 18 42 42 7 91 92 91 6900 15 175 175 10 92 6900 18 132 132 24 93 94 95 93 8100 19 55 55 33 96