Question

1.

Use Excel to create a regression model at a confidence level of 0.95. Based on your test results, pick the best conclusion.The data for the model seek to explain variability in the firm's net profit based on number of hours worked and client type (Manufacturing, Services, and Government). The client type is represented by a dummy variable. The model equation predicts the following net profit for a 100 hour project:

a) Manufacturing: $2302.27; Services: $1869.81; Government: $-586.26 b) Manufacturing: $2302.27; Services: $1869.81; Government: $0 c) Manufacturing: $2302.27; Services: $1869.81; Government: $22.86 d) Manufacturing: $4002.12; Services: $3569.66; Government: $1699.85

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Answer 1

Y = 71.0000 2127.0000 X1 2689.0000 X2 Correlation matrix X1 X2 хз 0.5957 0.1577 0.4123 1 0.33110.5113 0.3311 0.51130.4629 0.5957 0.1577 0.4123 X1 X2 X3 ANOVA table Source Regression (between yi and yi) Residual (between yi and yi) Total (between yi and y Coefficient Table Iteration 1 (adjusted R-squared 0.569) 10.4629 Sum of Squa Mean Squ F Statistic 2 14813168 7406584 P-value DF 8.1883 0.0116 8 7236280 904535 10 22049448 2204945 Coeff SE t-stat lower t0.025(7) upper t0.975(7) Stand Coe p-value VIF 0 0.5663 586.26974.2029-0.6018 1869.81 764.5388 2.4457 2302.27895.0616 2.5722 22.8611 29.3345 0.7793 2889.8794 61.966 185.7827 46.5039 1717.3683 3677.6601 0.6353 0.04439 1.5642 4418.7513 0.7242 0.03689 1.8376 X1 X2 X3 92.2260.197 0.4613 1.4806 Coefficient Table Iteration 2 (adjusted R-squared 0.590) Coeff SE t-stat lower t0.025(8) upper t0.975(8) Stand Coe p-value VIF 1167.5862 3677.8071 0.7227 0.01334 1.2727 4364.0631 0.849 0.00603 1.2727 0.885 71 475.53520.1493 2127 672.5084 3.1628 2689 726.3921 3.7019 -1025.5862 576.1929 1013.9369 0 X1 X2

a) Manufacturing: $2302.27; Services: $1869.81; Government: $-586.26