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Chapter 12: Q10CQQ (page 566)

Estimating Length Using the same results displayed in Exercise 8, does it appear that the length estimates are affected by the subject’s major?

Short Answer

Expert verified

As the p-value equal to 0.13 is greater than 0.05, it can be concluded that the factor ‘major’ does not have a significant effect on the visual estimation of the length of the classroom.

Step by step solution

01

Step 1

Refer to Exercise 8 for the minitab output.

02

Analysis of the two-way analysis of variance

The hypotheses analyzed in the two-way analysis of variance test are as follows.

  • Effect of the interaction term.
  • Effect of factor 1, sex
  • Effect of factor 2, major

The null hypothesis to test the effect of factor 2, major, is as follows.


There is no significant effect of major on the visual estimates of the class length.

The value of the F-statistic for the factor ‘major’ is equal to 0.13.

The corresponding p-value is equal to 0.876.

As the p-value is greater than 0.05, the null hypothesis is failed to reject.

Thus, there is not enough evidence to conclude that the type of major has a significant effect on the length estimates.

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Most popular questions from this chapter

Cola Weights Identify the value of the test statistic in the display included with Exercise 1. In general, do larger test statistics result in larger P-values, smaller P-values, or P-values that are unrelated to the value of the test statistic

Pancake Experiment Listed below are ratings of pancakes made by experts (based on data from Minitab). Different pancakes were made with and without a supplement and with different amounts of whey. The results from two-way analysis of variance are shown. Use the displayed results and a 0.05 significance level. What do you conclude?

Whey


0%

10%

20%

30%

No Supplement

4.4

4.5

4.3

4.6

4.5

4.8

4.5

4.8

4.8

4.6

4.7

5.1

Supplement

3.3

3.2

3.1

3.8

3.7

3.6

5.0

5.3

4.8

5.4

5.6

5.3

Arsenic in Rice Listed below are amounts of arsenic in samples of brown rice from three different states. The amounts are in micrograms of arsenic and all samples have the same serving size. The data are from the Food and Drug Administration. Use a 0.05 significance level to test the claim that the three samples are from populations with the same mean. Do the amounts of arsenic appear to be different in the different states? Given that the amounts of arsenic in the samples from Texas have the highest mean, can we conclude that brown rice from Texas poses the greatest health problem?

Arkansas

4.8

4.9

5

5.4

5.4

5.4

5.6

5.6

5.6

5.9

6

6.1

California

1.5

3.7

4

4.5

4.9

5.1

5.3

5.4

5.4

5.5

5.6

5.6

Texas

5.6

5.8

6.6

6.9

6.9

6.9

7.1

7.3

7.5

7.6

7.7

7.7

Fast Food Dinner Service Times Data Set 25 “Fast Food” in Appendix B lists drivethrough service times (seconds) for dinners at McDonald’s, Burger King, and Wendy’s. Using those times with a TI-83>84 Plus calculator yields the following display. Using a 0.05 significance level, test the claim that the three samples are from populations with the same mean. What do you conclude?

Speed Dating Data Set 18 “Speed Dating” in Appendix B lists attribute ratings of females by males who participated in speed dating events, and some of those values are included in the table on the top of the next page. Analysis of variance is used with the values in that table, and the StatCrunch results are shown on the next page following the data. Use a 0.05 significance level to test the claim that males in the different age brackets give attribute ratings with the same mean. Does age appear to be a factor in the male attribute ratings?


Age 20-22

32

34

37

40.5

33

28

31

50

39

41

Age 23-26

40

21

14

32

26

34

31

34

34

34

Age 27-29

31

39

27

34

43

31

30

38

37

34
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