33. Regression Analysis NOTE: You need not use Excel, but data should be consistent with the information below. SUMMARY OUTPUT Regression Statistics Multiple R 0.92 R Square 0.85 Adjusted R Square 0.83 Standard Error 123,526.71 Observations 12 ANOVA df F Significance F 57 0.00 Regression Residual Total 1 10 11 SS 864,663,529,783 152,588,470,217 1,017,252,000,000 MS 864,663,529,783 15,258,847,022 t Stat Intercept Total Machine Hours Coefficients 534,766.55 8.54 Standard Error 83,965.17 1.13 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 6.37 0.00 347,680.46 721,852.64 347,680.46 721,852.64 7.53 0.00 6.01 11.07 6.01 11.07 Line Fit Plot Total Equipment Costs $1,800,000 $1,600,000 $1,400,000 $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200,000 $0 Total Equipment Costs Predicted Total Equipment Costs 20,000 40,000 60,000 80,000 100,000 120,000 140,000 Total Machine Hours RESIDUAL OUTPUT Observation 1 2 3 4 5 6 7 8 9 10 11 12 Predicted Total Equipment Cost 995,970 790,990 739,746 1,457,173 1,508,417 842,235 924,227 1,026,717 1,334,185 1,057,463 1,518,666 1,088,210 Residuals 108,030 (70,990) (139,746) (137,173) (140,417) (98,235) 131,773 65,283 (62,185) 94,537 161,334 87,790 b. The cost equation is: Y = $534,767 + $8.54X c. Using the equation from part b, substitute 90,000 machine hours for X as follows: Y = $534,767 +($8.54 x 90,000 machine hours) Y = $534,767 + $768,600 Y = $1,303,367 With 90,000 machine hours, estimated costs total $1,303,367