Question: Nine similar machines are used in a manufacturing process at an assembly plant. The operations manager suspects that the repair costs for the machines are influenced by the operator of the machine. From past experience he knows that the age of the machine affects repair costs as well. The data on the next page shows repair costs (COST) for the machines over the past six months, the age (AGE) of each machine in years at the beginning of the six month period, the number of items processed (ITEMS) by the machine during the period, and the operator of the machine (OPER). Two dummy variables are also presented to capture the three levels of the variable OPER. They are listed as DA and DB in the table.

A. What does this model predict to be the effect of age of the machine on repair cost?

B. Does the age of the machine have a significant impact on repair costs of the machine as the manager suspected?

C. Report the estimated regression equation for operator B.

D. Interpret the numeric value of the coefficient for the variable DA.

E. Is there a significant difference in mean repair cost for operator B and operator C? Test an appropriate hypothesis.

F. Test both dummy variables, DA and DB, jointly for significance using an incremental sum of squares test.

G. Define multicollinearity and heteroscedasticity. Is there any evidence that either of these problems exist in this model?

H. Report the results of a formal test for non-normality of the error terms in this regression.

I. A second order model is presented on the last page of the printout. Is this second order model an improvement over the first order model regarding the objective of the study?