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

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 0.01 significance level to test the claim that the three samples are from populations with the same median.

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

Short Answer

Expert verified

There is not enough evidence to support the claim that thethree samples come from populations with the same median.

Step by step solution

01

Given information

Three samples are given showing the amount of arsenic present in brown rice servings of the three different states.

The significance level is 0.01.

02

Hypotheses

The Kruskal-Wallis test is used to test the difference of medians between the given threesamples.

The null hypothesis is as follows:

The three samples are obtained from populations with the same median.

The alternative hypothesis is as follows:

The three samples are obatined from populations with different medians.

This is a two-tailed test.

03

Assign ranks and calculate the sum of the ranks

The ranks of the observations from the three samples are given using the following steps:

  • Combine the three samples and tag the sample name against each observation.
  • Ranks are assigned to all observations. The smallest observation is assigned a rank of 1; the next smallest observation is assigned a rank of 2, and so on.
  • If two observations have the same value, the mean of the ranks is assigned to them.

The following table shows the ranks:

Amount of Arsenic

City Name

Ranks

4.8

Arkansas

5

4.9

Arkansas

6.5

5

Arkansas

8

5.4

Arkansas

13

5.4

Arkansas

13

5.4

Arkansas

13

5.6

Arkansas

19.5

5.6

Arkansas

19.5

5.6

Arkansas

19.5

5.9

Arkansas

24

6

Arkansas

25

6.1

Arkansas

26

1.5

California

1

3.7

California

2

4

California

3

4.5

California

4

4.9

California

6.5

5.1

California

9

5.3

California

10

5.4

California

13

5.4

California

13

5.5

California

16

5.6

California

19.5

5.6

California

19.5

5.6

Texas

19.5

5.8

Texas

23

6.6

Texas

27

6.9

Texas

29

6.9

Texas

29

6.9

Texas

29

7.1

Texas

31

7.3

Texas

32

7.5

Texas

33

7.6

Texas

34

7.7

Texas

35.5

7.7

Texas

35.5

The sum of the ranks corresponding to Arkansas is computed as follows:

\(\begin{array}{c}{R_1} = 5 + 6.5 + 8 + .... + 26\\ = 192\end{array}\)

The sum of the ranks corresponding to California is computed as follows:

\(\begin{array}{c}{R_2} = 1 + 2 + 3 + .... + 19.5\\ = 116.5\end{array}\)

The sum of the ranks corresponding toTexas is computed as follows:

\(\begin{array}{c}{R_3} = 19.5 + 23 + 27 + .... + 35.5\\ = 357.5\end{array}\)

04

Determine the sample sizes

Here,

\({n_1} = {n_2} = {n_3} = 12\)

and

\(\begin{array}{c}N = 12 + 12 + 12\\ = 36\end{array}\)

05

Calculate the test statistic

The value of the test statistic is computed as shown below:

\(\begin{array}{c}H = \frac{{12}}{{N\left( {N + 1} \right)}}\left( {\frac{{{R_1}^2}}{{{n_1}}} + \frac{{{R_2}^2}}{{{n_2}}} + \frac{{{R_3}^2}}{{{n_3}}}} \right) - 3\left( {N + 1} \right)\\ = \frac{{12}}{{36\left( {37} \right)}}\left( {\frac{{{{192}^2}}}{{12}} + \frac{{{{116.5}^2}}}{{12}} + \frac{{{{357.5}^2}}}{{12}}} \right) - 3\left( {37} \right)\\ = 22.816\end{array}\)

06

Determine the critical value and conclusion of the test

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-square for\(\alpha = 0.01\)with 2 degrees of freedom is equal to 9.21.

Since the test statistic value is greater than the critical value, the null hypothesis is rejected.

It can be concluded that the three samples are obtained from populations with different medians.

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Most popular questions from this chapter

Using the Mann-Whitney U Test The Mann-Whitney U test is equivalent to the Wilcoxon rank-sum test for independent samples in the sense that they both apply to the same situations and always lead to the same conclusions. In the Mann-Whitney U test we calculate

\(z = \frac{{U - \frac{{{n_1}{n_2}}}{2}}}{{\sqrt {\frac{{{n_1}{n_2}\left( {{n_1} + {n_2} + 1} \right)}}{{12}}} }}\)

Where

\(U = {n_1}{n_2} + \frac{{{n_1}\left( {{n_1} + 1} \right)}}{2} - R\)

and R is the sum of the ranks for Sample 1. Use the student course evaluation ratings in Table 13-5 on page 621 to find the z test statistic for the Mann-Whitney U test. Compare this value to the z test statistic found using the Wilcoxon rank-sum test.

Blanking Out on Tests In a study of students blanking out on tests, the arrangement of test items was studied for its effect on anxiety. The following scores are measures of “debilitating test anxiety” (based on data from “Item Arrangement, Cognitive Entry Characteristics, Sex and Test Anxiety as Predictors of Achievement in Examination Performance,” by Klimko, Journal of Experimental Education, Vol. 52, No. 4.) Is there sufficient evidence to support the claim that the two samples are from populations with different medians? Is there sufficient evidence to support the claim that the arrangement of the test items has an effect on the score? Use a 0.01 significance level.

Questions Arranged From Easy To Difficult

24.64

39.29

16.32

32.83

28.02

33.31

20.6

21.13

26.69

28.9

26.43

24.23

7.1

32.86

21.06

28.89

28.71

31.73

30.02

21.96

25.49

38.81

27.85

30.29

30.72

Questions Arranged From Difficult To Easy

33.62

34.02

26.63

30.26

35.91

26.68

29.49

35.32

27.24

32.34

29.34

33.53

27.62

42.91

30.2

32.54

In Exercises 5 and 6, use the scatterplot to find the value of the rank correlation coefficient\({r_s}\)and the critical values corresponding to a 0.05 significance level used to test the null hypothesis of\(\rho \)= 0. Determine whether there is a correlation.

Distance/Time Data for a Dropped Object

Finding Critical Values An alternative to using Table A–9 to find critical values for rank correlation is to compute them using this approximation:

\({r_s} = \pm \sqrt {\frac{{{t^2}}}{{{t^2} + n - 2}}} \)

Here, t is the critical t value from Table A-3 corresponding to the desired significance level and n - 2 degrees of freedom. Use this approximation to find critical values of\({r_s}\)for Exercise 15 “Blood Pressure.” How do the resulting critical values compare to the critical values that would be found by using Formula 13-1 on page 633?

Runs Test Consider sample data consisting of genders of criminals charged with hacking computer systems of corporations. Determine whether the following are true or false.

a. If the runs test suggests that sample data occur in a random order, then it follows that the data have been randomly selected.

b. If the runs test suggests that sample data occur in a random order, then there is not a significant difference between the proportions of males and females.

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