Chapter 11 Inferences About Population Variances

Learning Objectives

1. Understand the importance of variance in a decision-making situation.

2 Understand the role of statistical inference in developing conclusions about the variance of a single population.

3. Know that the sampling distribution of (n - 1) s2/ 2 has a chi-square distribution and be able to use this result to develop a confidence interval estimate of  2.

4. Be able to compute p-values using the chi-square distribution.

5. Know how to test hypotheses involving  2.

6. Understand the role of statistical inference in developing conclusions about two population variances.

2 2 7. Know that the sampling distribution of s1/ s 2 has an F distribution and be able to use this result to test hypotheses involving two population variances.

8. Be able to compute p-values using the F distribution.

11 - 1 Chapter 11

Solutions:

1. a. 11.070

b. 27.488

c. 9.591

d. 23.209

e. 9.390

2. s2 = 25

2 2 a. With 19 degrees of freedom .05 = 30.144 and .95 = 10.117

19(25) 19(25)  2  30.144 10.117

15.76   2  46.95

2 2 b. With 19 degrees of freedom .025 = 32.852 and .975 = 8.907

19(25) 19(25)  2  32.852 8.907

14.46   2  53.33

c. 3.8    7.3

2 2 2 (n 1) s (16  1)(9.5) 3.  2   27.08  0 50

Degrees of Freedom = (16 - 1) = 15

Using  2 table, p-value is between .025 and .05

Actual p-value = .0281

p-value  .05, reject H0

Critical value approach

2 .05 = 24.996

2 Reject H0 if   24.996

27.08 > 24.996, reject H0

11 - 2 Inferences About Population Variances

4. a. n = 18

s2 = .36

2 .05 = 27.587

2 .95 = 8.672 (17 degrees of freedom)

17(.36) 17(.36)  2  27.587 8.672

.22   2  .71

b. .47    .84

(x  x )2 5. a. s2 i  31.07 n 1

s 31.07  5.57

2 2 b. .025 = 16.013 .975 = 1.690

(8 1)(31.07) (8  1)(31.07)  2  16.013 1.690

13.6   2  128.7

c. 3.7    11.3

(x  x )2 6. a. s2 i  176.96 n 1

s 176.96  13.30

2 2 b. .025 = 11.143 .975 = 0.484

(5 1)(176.96) (5  1)(176.96)  2  11.143 .484

63.5   2  1462.5

8.0    38.2

(x  x )2 7. a. s2 i  2.62 n 1

s 2.62  1.62

11 - 3 Chapter 11

2 2 b. .025 = 16.013 .095 = 1.690

(8 1)(2.62) (8  1)(2.62)  2  16.013 1.690

1.14   2  10.85

c. 1.07    3.29

(x  x )2 .0929 8. a. s2 i   .00845 n 1 12  1

b. s .00845  .092

c. 11 degrees of freedom

2 2 .025 = 21.920 .975 = 3.816

(12 1).00845 (12  1).00845  2  21.920 3.816

.0042   2  .0244

.065   .156

2 9. H0:  .0004

2 Ha:  .0004

2 2 (n 1) s (30  1)(.0005)  2   36.25  0 .0004

Degrees of freedom = n - 1 = 29

Using  2 table, p-value is greater than .10

Actual p-value = .1664

p-value > .05, do not reject H0. The product specification does not appear to be violated.

10.  2 .75)2 = .5625

2 H0:  .5625

2 Ha:  .5625

(n 1) s2 (30  1)(.95) 2  2    46.53  2 .5625

Degrees of freedom = n - 1 = 29

11 - 4 Inferences About Population Variances

Using  2 table, p-value is between .01 and .025

Actual p-value = .0208

p-value  .05, reject H0. The standard deviation for television sets is greater than the standard deviation for VCR’s.

2 11. H0:  = .009216

2 Ha:   .009216

(n 1) s2 (20  1)(.114) 2  2    26.79  2 .009216

Degrees of freedom = n - 1 = 19

Using  2 table, area in upper tail is greater than .10

Two-tail p-value is greater than .20

Actual p-value = .2191

p-value > .05, do not reject H0. Cannot conclude the variance in interest rates has changed.

(x  x )2 12. a. s2 i  .8106 n 1

2 b. H0:  .94

2 Ha:  .94

2 2 (n 1) s (12  1)(.8106)  2   9.49  0 .94

Degrees of freedom = n - 1 = 11

Using  2 table, area in upper tail is greater than .10

Two-tail p-value is greater than .20

Actual p-value = .8457

p-value > .05, cannot reject H0.

13. a. F.05 = 3.33

b. F.025 = 2.76

c. F.01 = 4.50

d. F.10  1.94

11 - 5 Chapter 11

2 s1 5.8 14. a. F 2   2.4 s2 2.4

Degrees of freedom 15 and 20

Using F table, p-value is between .025 and .05

Actual p-value = .0334

2 2 p-value  .05, reject H0. Conclude 1  2

b. F.05 = 2.20

Reject H0 if F  2.20

2 2 2.4  2.20, reject H0. Conclude 1  2

2 15. a. Larger sample variance is s1

2 s1 8.2 F 2   2.05 s2 4

Degrees of freedom 20 and 25

Using F table, area in tail is between .025 and .05

Two-tail p-value is between .05 and .10

Actual p-value = .0904

p-value > .05, do not reject H0.

b. Since we have a two-tailed test

F / 2 F .025  2.30

Reject H0 if F  2.30

2.05 < 2.30, do not reject H0

2 2 16. H0 : 1  2

2 2 Ha :1  2

2 2 s1 94 F 2  2  2.63 s2 58 Degrees of freedom 25 and 29

Using F table, p-value is less than .01

11 - 6 Inferences About Population Variances

Actual p-value = .0067

p-value  .01, reject H0. Conclude adults have a greater variance in online times.

17. a. Population 1 is 4 year old automobiles

2 2 H0 : 1  2

2 2 Ha : 1  2

2 2 s1 170 b. F 2  2  2.89 s2 100

Degrees of freedom 25 and 24

Using F table, p-value is less than .01

Actual p-value = .0057

p-value  .01, reject H0. Conclude that 4 year old automobiles have a larger variance in annual repair costs compared to 2 year old automobiles. This is expected due to the fact that older automobiles are more likely to have some more expensive repairs which lead to greater variance in the annual repair costs.

2 2 18. H0 : 1  2

2 2 Ha :1  2

2 2 s1 4.27 F 2  2  3.54 s2 2.27

Degrees of freedom 9 and 6

Using F table, area in tail is between .05 and .10

Two-tail p-value is between .10 and .20

Actual p-value = .1379

p-value > .05, do not reject H0. Cannot conclude any difference between variances of the two industries.

2 2 19. H0 : 1  2

2 2 Ha :1  2

2 s1  .0489

2 s2  .0059

11 - 7 Chapter 11

2 s1 .0489 F 2   8.28 s2 .0059

Degrees of freedom 24 and 21

Using F table, area in tail is less than .01

Two-tail p-value is less than .02

Actual p-value  0

p-value  .05, reject H0. The process variances are significantly different. Machine 1 offers the best opportunity for process quality improvements.

Note that the sample means are similar with the mean bag weights of approximately 3.3 grams. However, the process variances are significantly different.

2 2 20. H0 : 1  2

2 2 Ha : 1  2

2 s1 11.1 F 2   5.29 s2 2.1

Degrees of freedom 25 and 24

Using F table, area in tail is less than .01

Two-tail p-value is less than .02

Actual p-value  0

p-value  .05, reject H0. The population variances are not equal for seniors and managers.

(x  x )2 21. a. s2  i n 1

2 sNov = 9664

2 2 sDec = 19,238 (Population 1 since s is larger)

2 2 b. H0 : 1  2

2 2 Ha : 1  2

2 s1 19,238 F 2   1.99 s2 9664

Degrees of freedom 9 and 9

Using F table, area in tail is greater than .10

11 - 8 Inferences About Population Variances

Two-tail p-value is greater than .20

Actual p-value = .3197

p-value > .05, do not reject H0. There is no evidence that the population variances differ.

22. a. Population 1 - Wet pavement.

2 2 H0 : 1  2

2 2 Ha : 1  2

2 2 s1 32 F 2  2  4.00 s2 16

Degrees of freedom 15 and 15

Using F table, p-value is less than .01

Actual p-value = .0054

p-value  .05, reject H0. Conclude that there is greater variability in stopping distances on wet pavement.

b. Drive carefully on wet pavement because of the uncertainty in stopping distances.

23. a. s2 = (30) 2 = 900

2 2 b. .05 = 30.144 and .95 = 10.117 (19 degrees of freedom)

(19)(900) (19)(900)  2  30.144 10.117

567   2  1690

c. 23.8    41.1

24. With 12 degrees of freedom,

2 2 .025 = 23.337 .975 = 4.404

(12)(14.95)2 (12)(14.95) 2  2  23.337 4.404

114.9   2  609

10.72    24.68

x 25. a. x i  260.16 n

11 - 9 Chapter 11

(x  x )2 b. s2 i  4996.8 n 1

s 4996.79  70.69

2 2 c. .025 = 32.852 .975 = 8.907 (19 degrees of freedom)

(20 1)(4996.8) (20  1)(4996.8)  2  32.852 8.907

2890   2  10,659

53.76    103.24

2 26. a. H0:  .0001

2 Ha:  .0001

(n 1) s2 (15  1)(.014) 2  2    27.44  2 .0001

Degrees of freedom = n - 1 = 14

Using  2 table, p- value is between .01 and .025

Actual p-value = .0169

p-value  .10, reject H0. Variance exceeds maximum variance requirement.

2 b. .05 = 23.685

2 .95 = 6.571 (14 degrees of freedom)

(14)(.014)2 (14)(.014) 2  2  23.685 6.571

.00012   2  .00042

2 27. H0:  .02

2 Ha:  .02

2 2 2 (n 1) s (41  1)(.16)  2   51.20  0 .02

Degrees of freedom = n - 1 = 40

Using  2 table, p- value is greater than .10

Actual p-value = .1104

11 - 10 Inferences About Population Variances

p-value > .05, do not reject H0. The population variance does not appear to be exceeding the standard.

2 28. H0:  

2 Ha:  

2 2 (n 1) s (22  1)(1.5)  2   31.50  0 1

Degrees of freedom = n - 1 = 21

Using  2 table, p-value is between .05 and .10

Actual p-value = .0657

2 p-value  .10, reject H0. Conclude that  > 1.

(x  x )2 101.56 29. s2 i   12.69 n 1 9  1

2 H0:  = 10

2 Ha:   10

(n 1) s2 (9  1)(12.69)  2    10.16  2 10

Degrees of freedom = n - 1 = 8

Using  2 table, area in tail is greater than .10

Two-tail p- value is greater than .20

Actual p-value = .5086

p-value > .10, do not reject H0

30. a. Try n = 15

2 .025 = 26.119

2 .975 = 5.629 (14 degrees of freedom)

(14)(64) (14)(64)  2  26.119 5.629 34.3   2  159.2

5.86    12.62

11 - 11 Chapter 11

A sample size of 15 was used.

b. n = 25; expect the width of the interval to be smaller.

2 .05 = 39.364

2 .975 = 12.401 (24 degrees of freedom)

(24)(8)2 (24)(8) 2  2  39.364 12.401

39.02   2  126.86

6.25    11.13

2 2 31. H0 : 1  2

2 2 Ha : 1  2

Population 1 is NASDAQ

2 2 s1 15.8 F 2  2  4.00 s2 7.9

Degrees of freedom 9 and 9

Using F table, area in tail is between .025 and .05

Two-tail p-value is between .05 and .10

Actual p-value = .0510

p-value > .05, do not reject H0. Cannot conclude population variances differ. But with such a low p- value, a larger sample is recommended.

2 2 32. H0 : 1  2

2 2 Ha : 1  2

Use critical value approach since F tables do not have 351 and 72 degrees of freedom.

F.025 = 1.47

Reject H0 if F  1.47

2 2 s1 .940 F 2  2  1.39 s2 .797

F < 1.47, do not reject H0. We are not able to conclude students who complete the course and students who drop out have different variances of grade point averages.

11 - 12 Inferences About Population Variances

2 2 33. H0 : 1  2

2 2 Ha : 1  2

Population 1 has the larger sample variance.

2 s1 5.4 F 2   2.35 s2 2.3

Degrees of freedom 15 and 15

Using F table, area in tail is between .05 and .10

Two-tail p-value is between .10 and .20

Actual p-value = .1091

p-value > .10, do not reject H0. Cannot conclude that there is a difference between the population variances.

2 2 34. H0 : 1  2

2 2 Ha : 1  2

2 s1 25 F 2   2.08 s2 12

Degrees of freedom 30 and 24

Using F table, area in tail is between .025 and .05

Two-tail p-value is between .05 and .10

Actual p-value = .0689

p-value  .10, reject H0. Conclude that the population variances are not equal.

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