Question

The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four explanatory variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table.

Time until First Engine Overhaul	Annual Miles Driven	Average Load Weight	Average Driving Speed	Oil Change Interval
7.9	42.7	18.0	43.0	14.0
0.9	98.4	29.0	48.0	24.0
⋮	⋮	⋮	⋮	⋮
6.3	61.6	25.0	59.0	20.0

Excel Data File:

Time Until First Engine Overhaul	Annual Miles Driven	Average Load Weight	Average Driving Speed	Oil Change Interval
7.9	42.7	18	43	14
0.9	98.4	29	48	24
8.6	43.3	17	64	17
1.4	110.6	32	55	25
1.2	102.3	26	56	21
1.8	97.3	28	64	23
2.4	93.1	26	53	19
7.5	54.4	21	65	11
8	51.6	18	56	12
4.2	84.8	27	52	31
0.3	120.5	27	56	25
5.3	77.3	20	49	25
4.9	69	23	49	29
5.4	55.2	26	59	25
5.7	67.1	23	59	29
8.8	39.4	23	53	18
5.3	52.4	21	55	30
6.1	54.3	17	53	12
4.5	74.3	26	67	23
6.2	58.6	21	54	16
6.9	52	17	56	20
7.3	68.9	18	49	14
3.9	94.4	28	58	20
7.3	45.4	16	55	19
6.3	61.6	25	59	20

a. For each explanatory variable, discuss whether it is likely to have a positive or negative causal effect on time until the first engine overhaul.

b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.)

TimeˆTime^  =  +  Miles +     Load +     Speed +  Oil

c. Based on part (a), are the signs of the regression coefficients logical?

Answer 1