A manufacturer produces custom metal blanks that are used by its customers for c

Question

A manufacturer produces custom metal blanks that are used by its customers for computer-aided machining. The accompanying data were sampled from the accounting records of 40 orders filled during the previous three months. Formulate the regression model with y as the average dollar cost per unit, x1 as the material cost per unit, and x2 as the labor hours per unit. Complete parts (a) through (f) below.

(b) Assuming that a multiple regression is appropriate, fit the indicated multiple regression. Show a summary of the fitted model.

The fit has R^2 = a and se = $b

AverageCost MaterialCost LaborHours 49.66 2.073786 0.517 20.95 1.144052 0.167 39.08 3.567564 0.550 30.37 1.041930 0.300 51.90 1.819300 0.417 28.89 2.239936 0.433 36.88 2.659335 0.317 34.88 1.215309 0.383 38.44 1.556652 0.417 46.33 1.730625 0.517 47.91 1.921983 0.550 46.32 1.399554 0.467 26.71 1.430400 0.383 28.28 0.890043 0.267 28.50 1.776192 0.267 40.20 2.980699 0.217 39.86 3.164976 0.533 38.73 2.796540 0.417 32.86 2.138906 0.350 41.91 1.751680 0.517 38.00 2.140803 0.350 40.41 2.418570 0.450 48.43 2.616747 0.533 27.84 1.415414 0.383 38.81 2.762331 0.333 40.36 3.753200 0.383 52.93 1.824724 0.483 48.89 1.632712 0.500 46.58 1.583934 0.450 42.05 2.092356 0.533 39.24 1.841216 0.500 34.01 3.805637 0.400 35.46 1.565060 0.450 32.42 1.326424 0.217 49.62 1.797400 0.483 47.39 2.343585 0.567 34.38 3.064560 0.567 26.20 1.107249 0.300 29.16 3.072600 0.500 33.59 1.216800 0.483

Explanation / Answer

R^2 will be 0.3650 and Se will be 6.6175

SUMMARY OUTPUT Regression Statistics Multiple R 0.604134812 R Square 0.364978871 Adjusted R Square 0.330653404 Standard Error 6.617471937 Observations 40 ANOVA df SS MS F Significance F Regression 2 931.2482884 465.62414 10.63289 0.000224689 Residual 37 1620.264589 43.790935 Total 39 2551.512878 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 18.83841233 4.681353779 4.0241377 0.000272 9.35308859 28.3237361 MaterialCost 0.036743654 1.436816326 0.025573 0.979735 -2.87452276 2.94801007 LaborHours 46.16079239 10.45228742 4.416334 8.41E-05 24.98244639 67.3391384

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