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Using Nonparametric Tests. In Exercises 1鈥10, use a 0.05 significance level with the indicated test. If no particular test is specified, use the appropriate nonparametric test from this chapter.

Presidents, Popes, Monarchs Listed below are numbers of years that U.S. presidents, popes, and British monarchs lived after their inauguration, election, or coronation, respectively. Assume that the data are samples randomly selected from larger populations. Test the claim that the three samples are from populations with the same median.

Presidents

10

29

26

28

15

23

17

25

0

20

4

1

24

16

12


4

10

17

16

0

7

24

12

4

18

21

11

2

9

36


12

28

3

16

9

25

23

32








Popes

2

9

21

3

6

10

18

11

6

25

23

6

2

15

32


25

11

8

17

19

5

15

0

26







Monarchs

17

6

13

12

13

33

59

10

7

63

9

25

36

15


Short Answer

Expert verified

There is not enough evidence to warrant rejection of the claim that the three samples come from the populations with the same median.

Step by step solution

01

Given information

Three samples given are showing the number of years the US presidents, popes, and British monarchs lived after their inauguration, election or coronation respectively.

02

Appropriate test

As the number of samples is three and the equality of medians of the populations of the samples need to be tested, the Kruskal Wallis test is required to be conducted.

03

Identify the hypothesis

The null hypothesis for testing the equality of medians is as follows:

Thethree samples come from populations with the same median.

The alternative hypothesis is as follows:

Thethree samples do not come from populations with the same median.

04

Assign ranks

Combine the three samples and write the A for presidents, B for pope, and C for monarchs as the sample name.

Denote a rank of 1 to the smallest observation, 2 to the next smallest observation until all the observations are assigned ranks.

If some values are equal, assign the mean value of the ranks to all the similar values.

The following table shows the ranks of all the values:

Values

Ranks

Sample

Values

Ranks

Sample

10

26.5

A

2

6

B

29

69

A

9

22.5

B

26

65.5

A

21

53.5

B

28

67.5

A

3

8.5

B

15

39.5

A

6

15.5

B

23

56

A

10

26.5

B

17

46.5

A

18

49.5

B

25

62

A

11

30

B

0

2

A

6

15.5

B

20

52

A

25

62

B

4

11

A

23

56

B

1

4

A

6

15.5

B

24

58.5

A

2

6

B

16

43

A

15

39.5

B

12

33.5

A

32

70.5

B

4

11

A

25

62

B

10

26.5

A

11

30

B

17

46.5

A

8

20

B

16

43

A

17

46.5

B

0

2

A

19

51

B

7

18.5

A

5

13

B

24

58.5

A

15

39.5

B

12

33.5

A

0

2

B

4

11

A

26

65.5

B

18

49.5

A

17

46.5

C

21

53.5

A

6

15.5

C

11

30

A

13

36.5

C

2

6

A

12

33.5

C

9

22.5

A

13

36.5

C

36

73.5

A

33

72

C

12

33.5

A

59

75

C

28

67.5

A

10

26.5

C

3

8.5

A

7

18.5

C

16

43

A

63

76

C

9

22.5

A

9

22.5

C

25

62

A

25

62

C

23

56

A

36

73.5

C

32

70.5

A

15

39.5

C

05

Test Statistic

Let\({n_1}\)denote the sample size corresponding to presidents鈥 ages.

Let\({n_2}\)denote the sample size corresponding to popes鈥 ages.

Let\({n_3}\)denote the sample size corresponding to monarchs鈥 ages.

Thus,

\(\begin{array}{l}{n_1} = 38\\{n_2} = 24\\{n_3} = 14\end{array}\)

The value of N is equal to

\(\begin{array}{c}N = 38 + 24 + 14\\ = 76\end{array}\)

The sum of the ranks corresponding to presidents is computed below:

\(\begin{array}{c}{R_1} = 26.5 + 69 + ... + 70.5\\ = 1485.5\end{array}\)

The sum of the ranks corresponding to popes is computed below:

\(\begin{array}{c}{R_2} = 6 + 22.5 + .... + 65.5\\ = 806.5\end{array}\)

The sum of the ranks corresponding to popes is computed below:

\(\begin{array}{c}{R_3} = 46.5 + 15.5 + .... + 39.5\\ = 634\end{array}\)

Thus, the value of the test statistic is computed as follows:

\(\begin{array}{c}H = \frac{{12}}{{N\left( {N + 1} \right)}}\left( {\frac{{{R_1}^2}}{{{n_1}}} + \frac{{{R_2}^2}}{{{n_2}}} + \frac{{{R_3}^2}}{{{n_3}}}} \right) - 3\left( {N + 1} \right)\\ = \frac{{12}}{{76\left( {76 + 1} \right)}}\left( {\frac{{{{1485.5}^2}}}{{38}} + \frac{{{{806.5}^2}}}{{24}} + \frac{{{{634}^2}}}{{14}}} \right) - 3(76 + 1)\\ = 2.5288\end{array}\)

Thus, the value of H comes out to be equal to 2.5288.

06

Conclusion

Let k be the number of samples.

Thus, k=3.

The degrees of freedom are computed as follows:

\(\begin{array}{c}df = k - 1\\ = 3 - 1\\ = 2\end{array}\)

The critical value of\({\chi ^2}\)with 2 degrees of freedom at\(\alpha = 0.05\)is equal to 5.9915.

Since the value of H is less than the critical value, the decision is fail to reject the null hypothesis.

There is not enough evidence to warrant rejection of the claim that the three samples come from the populations with the same median.

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Most popular questions from this chapter

Wilcoxon Signed-Ranks Test for Body Temperatures The table below lists body temperatures of seven subjects at 8 AM and at 12 AM (from Data Set 3 鈥淏ody Temperatures in Appendix B). The data are matched pairs because each pair of temperatures is measured from the same person. Assume that we plan to use the Wilcoxon signed-ranks test to test the claim of no difference between body temperatures at 8 AM and 12 AM.

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GroupTreatedwith20 mgofLipitor

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-29

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GroupTreatedwith80 mgofLipitor

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Using the Kruskal-Wallis Test. In Exercises 5鈥8, use the Kruskal-Wallis test.

Correcting the H Test Statistic for Ties In using the Kruskal-Wallis test, there is a correction factor that should be applied whenever there are many ties: Divide H by

\(1 - \frac{{\sum T }}{{{N^3} - N}}\)

First combine all of the sample data into one list, and then, in that combined list, identify the different groups of sample values that are tied. For each individual group of tied observations, identify the number of sample values that are tied and designate that number as t, then calculate\(T = {t^3} - t\). Next, add the T values to get\(\sum T \). The value of N is the total number of observations in all samples combined. Use this procedure to find the corrected value of H for Example 1 in this section on page 628. Does the corrected value of H differ substantially from the value found in Example 1?

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