Work the following problems from your text:
Section 9.1, Page 495, #7
Section 9.1, Page 495, #9
Section 9.1, Page 495, #13
Section 9.1, Page 496, #17
Section 9.1, Page 496, #19
Section 9.1, Page 497, #25
Section 9.1, Page 498, #33
Section 9.1, Page 499, #41

Section 9.2, Page 505, #5
Section 9.2, Page 505, #15
Section 9.2, Page 506, #19
Section 9.2, Page 508, #25
Section 9.2, Page 508, #27
Section 9.2, Page 509, #35
Section 9.2, Page 509, #37
Section 9.2, Page 510, #39
Section 9.2, Page 510, #40
Section 9.2, Page 510, #47
Section 9.2, Page 510, #49

Section 9.3, Page 519, #1
Section 9.3, Page 519, #3
Section 9.3, Page 519, #7
Section 9.3, Page 521, #17
Section 9.3, Page 522, #25
Section 9.3, Page 523, #37
Section 9.3, Page 523, #39

Section 9.4, Page 527, #3
Section 9.4, Page 528, #5 (see printout below)

The data for problem #5 on page 528 (above) were entered into Minitab with the following results. The variables y, x1 and x2 are as defined in the text.
The regression equation is:
y = -2518.36 +126.82 x1 + 66.36 x2

Predictor	Coefficient	SE Coef	t	p
Constant	-2518.36	434.98	-5.79	0.0004
x1	126.82	275.76	0.46	0.658
x2	66.36	43.13	1.54	0.162

s = 28.49; R-sq = 98.5%; R-sq(adj) = 98.1%

To provide another view, the data were also entered into STATCRUNCH as accessed from CourseCompass. The results of the calculation may be compared with those obtained using Minitab.