9. $366.67 at 12% and $733.33 at 6%
11. 2
2
3
qt 13. 60 mph 15. 44, 38.25
17. 65 mph 19. About 1.753 ft 21. 2 meters
23. About 132.7 ft
25. Red Riding Hood, 54 mph; wolf, 48 mph
27. 2.234 in. 2.234 in. 29. 4.658 in. 31. 8.02 in.
33. 11.47 ft or 29.91 ft 35. 6.205 miles
37. 157.6 mph 39. 2.2 4.4 4 ft
Section 2.4, page 118
1. (1, 8) 3. (1.142855, 2.0625)
5. (.3409, .0003222)
7. (a) (1, 4) (b) (1, 4) and (2, 4) (c) (3, 20)
9. 52.86 mph 11. 21 ft
13. 450 ft 900 ft 15. $8800
17. 34.2 cm 34.2 cm 17.1 cm
19. (a) 4.4267 by 4.4267 in. (b) 10/3 by 10/3 in.
21. (a) Approximately 206
(b) Approximately 269; approximately $577
23. (a) 600 (b) 958
25. x 9.306; area 220.18 ft
2
27. Approximately (1.871, 1.5) 29. 12 times
Section 2.5, page 128
1.
The model y .5x 1.5 is better.
976 ANSWERS
3. (a) Data points are (0, 171.3), (2, 179.8), (4, 188), (6, 201.5).
For y 4.9x 170, the residuals are 1.3, 0, 1.6, 2.1;
their sum is 1.8. For y 5x 171, the residuals are .3,
1.2, 3, .5; their sum is 3.4.
(b) The sum is 8.66 for y 4.9x 170 and 10.78 for y
5x 171.
(c) The first model is a better fit.
5. The sum is 1.9055, whereas the sums for the other two mod-
els are 2.8 and 2.75.
7. Negative 9. Negative
11.
(a) The data appears linear.
(b) Positive correlation
13.
(a) The data is not linear.
15.
(a) The data appears linear.
(b) Negative correlation
17. (a) y 14.9x 2822
(b) 5057, 6994, 9080; the estimates are quite close to the
actual values.
(c) 6323.5; 6500
19. (a) (6, 1.8), (7, 2.3), (8, 2.5), (9, 3.1), (10, 3.9),
(11, 3.8), (12, 4), (13, 4.4), (14, 4.8), (15, 5.1)
(b) y .3594x .2036
(c) About $6.98 billion
1,000,000
5
0
60
80
0
0
13
12,000
0
0
15
MODEL: y x
Squared
Data Point Model Point Residual Residual
(1, 2) (1, 1) 1 1
(2, 2) (2, 2) 0 0
(3, 3) (3, 3) 0 0
(4, 3) (4, 4) 11
(5, 5) (5, 5) 0 0
Sum: 0 Sum: 2
MODEL: y .5x 1.5
Squared
Data Point Model Point Residual Residual
(1, 2) (1, 2) 0 0
(2, 2) (2, 2.5) 0.5 0.25
(3, 3) (3, 3) 0 0
(4, 3) (4, 3.5) 0.5 0.25
(5, 5) (5, 4) 1 1
Sum: 0 Sum: 1.5