please show excel steps with pictures and answer to #5

SCORE LAG 184 155 132 118. 102 178 115 128 99 133. 158 125 113 121 172 159 135 155 128 127 134 126 131 136 155 125 128 164 90 146 159 125 129 149 182 172 137 171 144] -7 9 21 -7 15 4 2 13 35 -2 12 0 2 -12 10 49 14 7 22 62 45 13 16 -2 8 17 21 3 27 -5 19 12 36 4 -7 42 -11 30 34 EMP@EOYOS@EOYDEPT 7.29 7.29 2.92 2.12 0.77 0.77 0.8 0.8 13.78 4.7 9.65 9.65 2.19 2.19 0.5 0.5 12.76 2.73 4.49 0.51 5.14 2.04 6.94 6.94 1.88 0.39 6.35 0.28 3.78 3.78 11.7 11.7 6.1 3.17 7.59 5.29 0.56 0.56 3.42 3.42 0.31 0.31 0.77 0.44 4.24 4.24 0.96 0.96 0.37 0.37 0.42 0.42 0.39 0.39 4.87 4.87 0.74 0.74 4.28 2.79 0.92 0.28 4.8 4.8 3.42 3.42 2.61 1.28 7.34 7.34 3.76 2.34 6.52 0.28 3.16 2.75 1.27 0.73 4555 40 41 64 41 62 62 71 55 62 73 73 72 64 55 55 41 73 62 55 41 55 41 40 72 64 72 73 55 74 62 63 72 62 71 62 55 GRADE EDUC 8 13 13 4 2 ww Ne 3 9 8 2 11 10 8 8 17 8 8 4 10 2 10 8 2 10 2 11 13 8 8 2 11 14 8 8 8 10 10 17 3 3 13 13 16 13 12 12 16 16 12 16 12 16 14 18 16 14 13 18 13 16 16 12 18 12 16 16 14 14 12 14 20 16 14 16. 16 14 16 12 12 EMP@EVAIOS@EVATYPE 7.12 7.12 1.82 1.02 0.31 0.31 0.23 0.23 13.16 4.07 9.02 9.01 2.15 2.15 0.29 0.29 12.1 2.07 4.23 0.25 4.13 1.03 6 6 1.75 0.26 6.29 0.22 3.03 3.03 11.67 11.67 5.83 2.9 7.32 5.02 0.31 0.31 3.17 3.17 0.37 0.37 0.29 0.05 4.05 4.05 0.24 0.24 0.27 0.27 0.3 0.3 0.31 0.31 4.01 4.01 0.32 0.32 3.47 1.99 0.95 0.3 4.04 4.04 3.1 3.1 2.34 1.01 6.98 6.98 3.79 2.38 6.47 0.22 2.5 2.08 1.09 0.55 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1 1 1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 1 1 1 0 1 1 JOB 2 4 4 1 1 1 2 2 1 3 3 2 2 4 2 2 1 3 1 3 2 1 3 1 3 4 2 2 1 3 4 2 2 2 3 3 4 1 1 HERE 1 1 1 1 1 1 1 1 1 1 1 163 165 128 133 162 119 131 149 167 136 150 135 176 115 155 127 61 115 121 160 162 155 124 141 124 131 111 135 28 4 11 6 -5 8 34 62 24 13 21 8 64 19 0 -12 37 15 23 -14 -1 4 -2 12 13 79 9 10 1.39 6.89 6.52 1.29 4.95 0.53 0.37 2.42 0.37 1.31 0.56 0.81 17.24 0.33 5.86 1.17 4.72 3.16 1.69 1.42 12.58 2.86 5.32 1.42 1.24 4.76 4.35 4.01 1.39 5.12 5.39 1.29 4.95 0.53 0.37 0.74 0.37 1.31 0.56 0.81 17.24 0.33 5.86 1.17 0.28 0.86 0.71 1.42 3.99 2.86 0.51 0.74 1.24 4.76 2.69 0.56 61 74 72 63 64 65 74 55 41 63 41 73 62 72 65 62 62 55 55 72 72 72 73 73 61 63 73 62 8 +0 00 00 00 00 8 3 8 8 8 4 9 2 3 2 10 3 8 3 3 6 622 8 13 8 10 5 nx 8 8 3 10 14 16 12 14 14 16 12 12 12 14 14 12 18 12 13 14 13 14 14 14 14 16 14 14 14 12 14 1.09 6.75 6.17 1.02 3.99 0.27 0.35 2.1 0.32 1.04 0.31 0.27 17.19 0.3 5 0.97 4.79 2.58 1.29 0.96 11.59 2.01 5.05 0.96 1.04 4.22 3.69 3.7 1.08 4.98 5.03 1.02 3.99 0.27 0.35 0.42 0.32 1.04 0.31 0.27 17.19 0.3 5 0.97 0.35 0.28 0.31 0.96 3 2.01 0.25 0.28 1.04 4.22 2.02 0.28 1 1 1 1 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 1 0 ( 1 1 1 0 2 2 1 2 2 2 1 2 1 1 1 3 1 2 1 1 1 1 1 2 4 2 3 1 2 2 1 3 1 1 1 1 1 1 164 127 171 123 128 128 132 173 126 120 121 111 129 132 75 157 155 152 121 112 170 159 124 174 120 139 139 160 133 147 126 112 140 124 104 126 33 5 3 31 27 15 -6 -12 34 36 8 30 2 55 12 58 9 10 6 20 -2 5 18 4 13. 17 -7 9 0 0 -3 -3 14 15 -3 16 60 10 30 7.64 0.46 4.91 8.1 2.73 0.94 0.63 0.88 8.73 0.9 2.96 0.96 0.92 0.96 2.52 10.9 12.76 7.39 3.28 5.81 0.94 3.42 4.8 0.65 3.44 2.44 1.96 2.76 1.69 5.35 7.56 0.52 6,78 0.96 1,88 1.99 2.78 7.64 0.46 0.51 6.5 2.73 0.94 0.63 0.88 8.73 0.54 1.13 0.96 0.92 0.96 2.52 6.5 12.76 3.12 3.28 0.92 0.94 3.42 4.8 0.65 3.44 1.13 0.89 2.76 1.35 5.35 7.56 0.52 0.28 0.74 0.39 1.99 0.27 64 62 73 61 74 62 62 70 63 72 62 65 74 61 71 55 73 73 62 62 62 62 65 72 62 74 55 72 41 72 64 61 64 72 62 72 73 8 8 10 3 8 8 8 6 8 3 8 3 8 3 10 10 4 13 8 3 8 8 8 8 8 13 10 2 14 8 8 8 13 3 10 2 8 14 14 16 12 14 16 16 16 14 12 14 12 14 14 16 16 12 16 14 12 14 16 16 14 16 16 14 12 18 14 16 18 18 13 14 14 14 4 7.6 0.27 4.67 7.68 2.07 0.29 0.24 0.21 8.1 0.72 2.02 0.33 0.28 0.41 2.07 10.92 12.02 7.33 2.99 5.15 1.3 3.01 4.04 0.3 2.98 1.58 1.31 2 1.33 4.99 7.56 0.29 6.74 0.52 1.91 1.03 2.84 7.6 0.27 0.26 6.09 2.07 0.29 0.24 0.21 8.1 0.35 0.2 0.33 0.28 0.41 2.07 6.53 12.02 3.05 2.99 0.26 1.3 3.01 4.04 0.3 2.98 0.28 0.24 2 0.99 4.99 7.56 0.29 0.24 0.29 0.42 1.03 0.33 1 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0 1 0 2 2 3 1 2 2 2 1 2 1 2 1 2 1 3 3 1 4 2 1 2 2 2 2 2 4 3 1 4 2 2 2 4 1 3 1 2 1 1 1 1 1 1 1 1 140 106 138 135 105 143 131 108 171 152 171 152 115 80 115 134 132 137 129 154 113 104 131 117 171 90 123 101 40 117 132 20 7 7 5 35 2 27 -10 -7 22 15. 8 69 8 15 -8 10 19 12 11 2 5 3 7 69 1.99 -4 1.1 34 0.81 -7 18.15 9 1.19 6 1.25 8.25 0.96 -3 5.05 8.15 0.73 2.23 0.28 14.27 87 2.94 11.28 5.33 2.67 5.89 1.96 0.46 0.48 2.13 7.35 1.69 12.7 2.7 16.85 5.9 2.8 1.27 0.91 0.75 0.73 0.81 0.28 14.27 2.69 0.28 2.08 2.23 5.22 1.96 0.32 0.48 0.36 0.28 0.74 0.28 0.94 13.51 0.98 1.99 1.1 0.81 13.79 1.19 1.25 4.49 0.96 2.8 1.27 41 71 72 55 72 73 55 74 72 55 64 62 72 41 41 61 55 55 62 41 71 55 72 62 65 74 74 62 62 74 74 4 9 4 3 8 11 10 17 10 5 3 2 3 4 5 13 3 17 3 14 13 2 8 8 13 8 8 13 8 8 8 12 12 12 12 16 16 18 14 14 13 12 12 12 12 16 14 13 18 13 14 12 14 14 14 14 14 16 18 14 14 4.43 7.67 0.27 1.68 0.35 14.03 1.32 11.22 4.38 2.5 5.72 1.02 0.58 0.27 2.06 7.3 1.23 12.72 2.047 16.59 5.18 1.01 0.24 0.35 17.34 0.28 0.27 7.76 0.48 2.02 1.02 0.3 0.27 0.27 0.27 0.35 14.03 1.07 0.22 1.13 2.06 5.04 1.02 0.44 0.27 0.29 0.23 0.28 0.3 0.28 13.25 0.25 1.01 0.24 0.35 12.99 0.28 0.27 3.99 0.48 2.02 1.02 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 2 1 1 2 3 3 4 3 1 1 1 1 1 1 4 1 4 1 4 4 1 2 2 4 2 2 4222 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 159 105 106 116 171 143 90 115 117 26 6 10 21 35 3 24 10 1.84 11.38 2.05 0.58 2.74 7.52 0.65 0.92 0.42 1.84 10.13 0.64 0.58 2.74 5.32 0.65 0.92 0.42 72 55 73 41 62 65 74 41 71 24 4 8 3 8 8 8 3 8 18 12 14 12 16. 16. 14 12 16 0.96 10.82 1.68 0.28 2.06 7.33 0.26 0.31 0.28 0.96 9.57 0.27 0.28 2.06 5.12 0.26 0.31 0.28 1 1 0 0 1 1 0 0 0 1 12 1 222 2 1 2 0 0 0 0 0 0 1000 Problem 4: The company's manager is trying to determine which of the variables in the data have significant impact on the evaluation score. Specifically, the manager is interested in investigating the following variables to see which of these variables are significant predictors of the evaluation score. The variables include EMP@EVAL, POS@EVAL, TYPE and HERE. He needs your help. 1. Fit a multiple linear regression of "SCORE" on "EMP@EVAL", "POS@EVAL", "TYPE", and "HERE". (5 points) 2. Test the hypothesis that vs. (use .05 level of significance). (5 points) 3. Check the normality assumption. (5 points) 4. Interpret the coefficients estimates of your model in the context of the problem. (5 points) 5. Interpret the R-square value in the context of the problem. (5 points) 6. Predict the average performance score of all employees who have been employed and stayed at the same position for 9 years at the time of performance evaluation, received a one-year evaluation and is still with the company. (5 points)