Ages of Best Actresses and Best Actors Listed below are ages of Best Actresses and Best Actors at the times they won Oscars (from Data Set 14 “Oscar Winner Age” in Appendix B). Do these data suggest that there is a correlation between ages of Best Actresses and Best Actors?

Actress

61

32

33

45

29

62

22

44

54

Actor

45

50

48

60

50

39

55

44

33

Short Answer

Expert verified

The rank correlation coefficient between the ages of actors and actresses is equal to -0.644

It can be concluded that there is no correlation between the ages of actors and actresses.

Step by step solution

01

Given information

Data are provided on the two samples on ages of actresses and actors.

02

Determine the rank correlation test and identify the statistical hypothesis

Rank correlation coefficient is a non-parametric test that is used to test the significance of the correlation between the two ordinal variables.

The null hypothesis is set up as follows:

There is no correlation between the ages of actresses and actors.

\({\rho _s} = 0\)

The alternative hypothesis is set up as follows:

There is a significant correlation between the ages of actresses and actors.

\({\rho _s} \ne 0\)

The test is twotailed.

03

Assign ranks

Compute the ranks of each of the two samples.

For the first sample, assign rank 1 for the smallest observation, rank 2 to the second smallest observation, and so on until the largest observation.If some observations are equal, thenthe mean of the ranks is assigned to each of the observations.

Similarly, assign ranks to the second sample.

The following table shows the ranks of the two samples:

Ranks of Ages of Actresses

8

3

4

6

2

9

1

5

7

Ranks of Ages of Actors

4

6.5

5

9

6.5

2

8

3

1

04

Calculate the Spearman rank correlation coefficient

Since there are ties present, the following formula is used to compute the rank correlation coefficient:

\({r_s} = \frac{{n\sum {xy} - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {n\left( {\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \sqrt {n\left( {\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} }}\)

The table below shows the required calculations:

Ranks of Ages of Actresses(x)

Ranks of Ages of Actors(y)

xy

\({x^2}\)

\({y^2}\)

8

4

32

64

16

3

6.5

19.5

9

42.25

4

5

20

16

25

6

9

54

36

81

2

6.5

13

4

42.25

9

2

18

81

4

1

8

8

1

64

5

3

15

25

9

7

1

7

49

1

\(\sum x \)=45

\(\sum y \)=45

\(\sum {xy} \)=186.5

\(\sum {{x^2}} \)=285

\(\sum {{y^2}} \)=284.5

Here, n = 9.

Substituting the values in the formula, the value of\({r_s}\)is obtained as follows:

\(\begin{array}{c}{r_s} = \frac{{n\sum {xy} - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {n\left( {\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \sqrt {n\left( {\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} }}\\ = \frac{{9\left( {186.5} \right) - \left( {45} \right)\left( {45} \right)}}{{\sqrt {9\left( {285} \right) - {{\left( {45} \right)}^2}} \sqrt {9\left( {284.5} \right) - {{\left( {45} \right)}^2}} }}\\ = - 0.644\end{array}\)

Therefore, the value of the Spearman rank correlation coefficient is equal to -0.644.

05

Determine the critical value and conclusion of the test

The critical values of the rank correlation coefficient for n=9 and\(\alpha = 0.05\)are -0.700 and 0.700.

Since the value of the rank correlation coefficient falls in the interval bounded by the critical values, so the decision if fail to reject the null hypothesis.

There is not enough evidence to conclude that there is a correlation between the ages of actresses and actors.

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Most popular questions from this chapter

Student Evaluations of Professors Example 1 in this section used samples of course evaluations, and the table below lists student evaluations of female professors and male professors (from Data Set 17 “Course Evaluations” in Appendix B). Are the requirements for using the Wilcoxon rank-sum test satisfied? Why or why not?

Female

3.9

3.4

3.7

4.1

3.7

3.5

4.4

3.4

4.8

4.1

2.3

4.2

3.6

4.4

Male

3.8

3.4

4.9

4.1

3.2

4.2

3.9

4.9

4.7

4.4

4.3

4.1



Foot Length , Height For the sample data given in Exercise 4, identify at least one advantage of using the appropriate non-parametric test over the parametric test.

Sign Test vs. Wilcoxon Signed-Ranks Test Using the data in Exercise 1, we can test for no difference between body temperatures at 8 AM and 12 AM by using the sign test or the Wilcoxon signed-ranks test. In what sense does the Wilcoxon signed-ranks test incorporate and use more information than the sign test?

Radiation in Baby Teeth Listed below are amounts of strontium-90 (in millibecquerels, or mBq, per gram of calcium) in a simple random sample of baby teeth obtained from Pennsylvania residents and New York residents born after 1979 (based on data from “An Unexpected Rise in Strontium-90 in U.S. Deciduous Teeth in the 1990s,” by Mangano et al., Science of the Total Environment). Use a 0.05 significance level to test the claim that the median amount of strontium-90 from Pennsylvania residents is the same as the median from New York residents.

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155

142

149

130

151

163

151

142

156

133

138

161

New York

133

140

142

131

134

129

128

140

140

140

137

143

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