Y	X
20	2
60	4
46	3
41	2
12	1
137	10
68	5
89	5
4	1
32	2
144	9
156	10
93	6
36	3
72	4
100	8
105	7
131	8
127	10
57	4
66	5
101	7
109	7
74	5
134	9
112	7
18	2
73	5
111	7
96	6
123	8
90	5
20	2
28	2
3	1
57	4
86	5
132	9
112	7
27	1
131	9
34	2
27	2
61	4
77	5

The Tri-City Office Equipment Corporation sells an imported copier on a franchise basis and performs preventive maintenance and repair service on this copier. The data above have been collected from 45 recent calls on users to perform routine preventive maintenance service; for each call, X is the number of copiers serviced and Y is the total number of minutes spent by the service person. Assume that first-order regression model is appropriate. Using this copier maintenance data below, The users of the copiers are either training institutions that use a small model, or business firms that use a large, commercial model. An analyst at Tri-City wishes to fit a regression model including both number of copiers serviced (XI) and type of copier (X2 ) as predictor variables and estimate the effect of copier model (S-small, L-large) on number of minutes spent on the service call. Records show that the models serviced in the 45 calls were:

Assume that regression model: