SUMMARY OUTPUT Simple Linear Regression R Squared Regression Statistics Multiple R 0.1892 R Square 0.0358 Adjusted R Square 0.0210 Standard Error 1.2317 Observations 67.0000 ANOVA df SS MS F Significance F Regression 1.0000 3.6617 3.6617 2.4136 0.1251 Residual 65.0000 98.6124 1.5171 Total 66.0000 102.2740 Coefficientsandard Errot Stat P-value Lower 95% Upper 95% ower 95.0%upper 95.0% Intercept 29.0841 0.2348 123.8455 0.0000 28.6151 29.5531 28.6151 29.5531 NUMEXPL (X1) 0.0033 0.0021 1.5536 0.1251 -0.0009 0.0074 -0.0009 0.0074SUMMARY OUTPUT Second Order Adjusted R Squared Regression Statistics Multiple R 0.1894 R Square 0.0359 Adjusted R Square 0.0057 Standard Error 1.2413 Observations 67.0000 ANOVA df SS MS F ignificance F Regression 2.0000 3.6678 1.8339 1.1903 0.3108 Residual 64.0000 98.6062 1.5407 Total 66.0000 102.2740 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% ower 95.0%upper 95.0% Intercept 29.1039 0.3922 74.2088 0.0000 28.3204 29.8873 28.3204 29.8873 NUMEXPL (X1) 0.0029 0.0066 0.4335 0.6661 -0.0103 0.0161 -0.0103 0.0161 (X2)numexpl^2 0.0000 0.0000 0.0633 0.9497 0.0000 0.0000 0.0000 0.0000SUMMARY OUTPUT Original Regression Model Regression Statistics Multiple R 0.9058 R Square 0.8205 Adjusted R Square 0.8058 Standard Error 0.5486 Observations 67.0000 ANOVA df SS MS F Significance F Regression 5.0000 83.9146 16.7829 55.7618 0.0000 Residual 61.0000 18.3595 0.3010 Total 66.0000 102.2740 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 29.6187 0.4960 59.7201 0.0000 28.6269 30.6104 28.6269 30.6104 NUMEXPL (X1) 0.0028 0.0009 2.9661 0.0043 0.0009 0.0047 0.0009 0.0047 MARGIN (X2) 0.0063 0.0222 0.2856 0.7762 -0.0380 0.0506 -0.0380 0.0506 PCOST (X3) 0.0852 0.0396 2.1539 0.0352 0.0061 0.1644 0.0061 0.1644 X4(H) -2.4639 0.2040 -12.0786 0.0000 2.8718 -2.0560 2.8718 -2.0560 X5(C) -1.1907 0.1671 -7.1272 0.0000 -1.5248 -0.8567 -1.5248 -0.8567A B C D E F G SALARY (Y) NUMEXPL (X1) MARGIN (X2) IPCOST (X3) X4(H) X5(C) DEGREE N - 29.5 58 19.4 10.14 C 29.3 37 17.7 9.18 29.8 135 20.4 6.84 C 29.2 69 20.5 7.59 C 28.9 48 19.1 4.96 H 31.7 159 23.3 10.52 M 27.5 42 23.4 8.61 H 10 00 29.4 37 23.1 10.72 C 10 30.4 71 18.5 5.65 M 11 27.7 69 16.4 5.46 H 12 30.9 121 24.6 7.37 M 13 28.9 389 11.0 7.40 H 14 29.7 99 20.9 9.05 C 15 30.3 62 23.0 8.81 M 16 31.3 107 15.3 10.94 M 17 30.0 42 18.8 6.84 C 18 30.0 35 21.0 6.45 M 19 28.5 42 10.5 6.06 H 20 29.9 31 19.3 10.20 C 21 29.7 78 18.0 9.60 22 30.2 132 23.5 7.88 23 29.7 37 22.4 6.71 24 29.9 89 22.8 10.04 25 29.0 101 21.7 8,39 26 29.4 60 18.0 5.24 27 30.3 48 21.9 9.60 28 30.4 75 22.6 11.63 M 29 31.1 71 24.5 9.65 M 30 29.4 47 24.2 7.94 C 31 30.7 39 22.7 9.67 M 32 30.2 50 23.1 9.66 M 33 30.7 40 16.1 10.31 M 34 28.5 102 16.2 6.67 H 35 29.0 38 21.9 6.45 C 36 29.2 80 20.9 10.07 C 37 28.1 77 14.0 7.06 H 38 27.7 28 19.8 9.70 H 39 27.3 30 6.7 3.16 H 40 31.3 34 21.4 10.91 M 41 27.4 28 16.0 8.19 H 42 29.3 230 14.9 5.70 C 43 28.7 121 19.3 6.42 H 44 29.7 146 20.9 5.74 C 45 29.3 124 17.6 6.13 C 46 28.3 40 16.3 8.86 H 47 25.7 130 15.6 4.11 H 48 27.2 60 15.9 6.13 H 49 29.2 94 22.6 9.95 C 50 30.2 43 19.6 7.83 M 51 30.7 111 18.2 6.70 M 52 29.4 37 23.0 11.25 C 53 28.4 76 15.5 4.77 H 54 30.1 188 18.9 5.94 M 55 28.5 64 12.6 4.81 H 56 28.8 185 17.7 8.66 H 57 32.4 371 22.3 7.45 M 58 28.4 81 23.1 5.14 H 59 29.7 62 20.9 9.26 C 60 27.0 30 9.8 1.44 H 61 28.2 103 22.1 7.98 H 62 27.6 29 9.7 6.09 H 63 30.7 28 17.1 8.71 M 64 28.7 34 16.8 5.11 H 65 29.4 279 23.2 6.20 C 66 29.9 35 23.4 8.42 C 67 31.3 43 18.3 7.52 M 68 28.5 77 19.0 7.85 H 69Answers keep 4 decimal places. The objective of this assignment is to get you familiar with the regression analysis procedure. The order of the question indicates the analyzing step when you are working on any regression analysis. A survey of information systems managers was used to predict the yearly salary of beginning programmer/analysts in a metropolitan area. Managers specied their standard salary for a beginning programmer/analyst, the number of employees in the rm's information processing staff, the rrn's gross prot margin in cents per dollar of sales, the rm's information processing cost as a percentage of total administrative costs and the nal degree. SALARY: yearly salary (thousand-$) UMEXPL: the number of employees in the rm's information processing staff MARGIN: the rrn's gross prot margin in cents per dollar of sales IPCOST: the rm's information processing cost as a percentage of total administrative costs DEGREE: the nal degree of the employee, where H stands for High School degree, C stands for college degree, M stands for Master degree Since the DEGREE is a categorical variable, which has 3 different categories (g,M), we will dene DEGREE as following: 1, if the degree is H X4, it IS a dummy varlable { 0' otherwise 1, if the degree is C X5, it is a dummy var1able{ 0' otherwise 6). What is the mean yearly salary for an employee holding master degree, working in a rm having 100 employees in the rm's information processing staff, the rm's gross prot margin is 10 cent per dollar sold, the rm's information processing cost is 20 percent? mat: 29.6187+ 0.0028t 1001+ 0.0063(10)+0.0852(20)2.4639(0)1.1907(0) $31.6673 thousands of dollars 7). what is the 95% prediction interval of the yearly salary for an employee as described in 6)? Interpret it. (30.5703, 32.7644) The condence interval for the average yearly salary of an employee holding a masters degree, working in a rm having 100 employees in the rm's information processing staff, a 10 cent per dollar/ sold prot margin, and a 20 percent information processing cost will be between $305703 and $32.7644 thousands of dollars. 1 8). What is the 95% condence interval of the mean yearly salary for the employees as described in 6)? Interpret it. Condence interval: (31.5333, 31.8014) The condence interval for the mean yearly salary for an employee holding master degree, working in a rm having 100 employees in the rm's information processing staff, having a gross prot margin of 10 cent per dollar sold, and the rms information processing cost at 20 percent, will be between $315333 and $31.8014 thousands of dollars. 9) Please use Excel to estimate the following simple liner models: SALARY 2 b0 + b1 * UMEXPL And use Excel to estimate the second-order polynomial regression model below: SALARY 2 be + 191 * UMEXPL + b2 * UMEXPL2 What g the adjusted r-squares for simple liner regression model and second-order polynomial regression model? Which model t the data better, and Why? Simple linear regression model Adjusted R2=0.020969 Second order regression model Adjusted R2=0.005734