The owner of a moving company typically has their most experienced manager predict the total number of labor hours that will be required to complete an upcoming move. The approach has proven useful in the past, but the owner has their business objective of developing a more accurate method of predicting labor hours. In a preliminary effort to provide a more accurate method, the owner has decided to use the number of cubic feet moved as the independent variable and has collected data for 36 moves. The data is seen below: Hours Feet 24.00 545 13.50 400 26.25 562 25.00 540 9.00 220 20.00 344 22.00 569 11.25 340 50.00 900 12.00 285 38.75 865 40.00 831 19.50 344 18.00 360 28.00 750 27.00 650 21.00 415 15.00 275 25.00 557 45.00 1028 29.00 793 21.00 523 22.00 564 16.50 312 37.00 757 32.00 600 34.00 796 25.00 577 31.00 500 24.00 695 40.00 1054 27.00 486 18.00 442 62.50 1249 53.75 995 79.50 1397 Perform a residual analysis for these data. Based on these results, evaluate whether the assumptions of regression have been seriously violated.
The owner of a moving company typically has their most experienced manager predict the total number of labor hours that will be required to complete an upcoming move. The approach has proven useful in the past, but the owner has their business objective of developing a more accurate method of predicting labor hours. In a preliminary effort to provide a more accurate method, the owner has decided to use the number of cubic feet moved as the independent variable and has collected data for 36 moves. The data is seen below:
|
Hours |
Feet |
|
24.00 |
545 |
|
13.50 |
400 |
|
26.25 |
562 |
|
25.00 |
540 |
|
9.00 |
220 |
|
20.00 |
344 |
|
22.00 |
569 |
|
11.25 |
340 |
|
50.00 |
900 |
|
12.00 |
285 |
|
38.75 |
865 |
|
40.00 |
831 |
|
19.50 |
344 |
|
18.00 |
360 |
|
28.00 |
750 |
|
27.00 |
650 |
|
21.00 |
415 |
|
15.00 |
275 |
|
25.00 |
557 |
|
45.00 |
1028 |
|
29.00 |
793 |
|
21.00 |
523 |
|
22.00 |
564 |
|
16.50 |
312 |
|
37.00 |
757 |
|
32.00 |
600 |
|
34.00 |
796 |
|
25.00 |
577 |
|
31.00 |
500 |
|
24.00 |
695 |
|
40.00 |
1054 |
|
27.00 |
486 |
|
18.00 |
442 |
|
62.50 |
1249 |
|
53.75 |
995 |
|
79.50 |
1397 |
Perform a residual analysis for these data. Based on these results, evaluate whether the assumptions of regression have been seriously violated.
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