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Testing for a Linear Correlation. In Exercises 13鈥28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

Oscars Listed below are ages of Oscar winners matched by the years in which the awards were won (from Data Set 14 鈥淥scar Winner Age鈥 in Appendix B). Is there sufficient evidence to conclude that there is a linear correlation between the ages of Best Actresses and Best Actors? Should we expect that there would be a correlation?

Actress

28

30

29

61

32

33

45

29

62

22

44

54

Actor

43

37

38

45

50

48

60

50

39

55

44

33

Short Answer

Expert verified

The scatter plot is shown below:

The value of the correlation coefficient is 鈥0.288.

The p-value is 0.364.

There is not enough evidence to support the claim that there existsa linear correlation between the ages of actresses and actors.

No correlation is expected between the ages of best actressesand actors in Oscars.

Step by step solution

01

Given information

The data is recorded for the ages of best actresses and actorsin Oscars.

Best Actress

Best Actor

28

43

30

37

29

38

61

45

32

50

33

48

45

60

29

50

62

39

22

55

44

44

54

33

02

Sketch a scatterplot

A scatterplot is used to reveal the pattern of association between two variables.

Steps to sketch a scatterplot:

  1. Define two axesfor the ages of actors and actresseseach year.
  2. Mark each paired set of values on the graph.

The resultant scatterplot is shown below.

03

Compute the measure of the correlation coefficient

The correlation coefficient formula is

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

For the variable of age for best actress (x) and age of best actor (y), compute the correlation coefficient.

The valuesare listedin the table below:

x

y

\({x^2}\)

\({y^2}\)

\(xy\)

28

43

784

1849

1204

30

37

900

1369

1110

29

38

841

1444

1102

61

45

3721

2025

2745

32

50

1024

2500

1600

33

48

1089

2304

1584

45

60

2025

3600

2700

29

50

841

2500

1450

62

39

3844

1521

2418

22

55

484

3025

1210

44

44

1936

1936

1936

54

33

2916

1089

1782

\(\sum x = 469\)

\(\sum y = 542\)

\(\sum {{x^2}} = 20405\)

\(\sum {{y^2} = } \;25162\)

\(\sum {xy\; = \;} 20841\)

Substitute the values in the formula:

\(\begin{aligned} r &= \frac{{12\left( {20841} \right) - \left( {469} \right)\left( {542} \right)}}{{\sqrt {12\left( {20405} \right) - {{\left( {469} \right)}^2}} \sqrt {12\left( {25162} \right) - {{\left( {542} \right)}^2}} }}\\ &= - 0.288\end{aligned}\)

Thus, the correlation coefficient is 鈥0.288.

04

Step 4:Conduct a hypothesis test for correlation

Definethe actual measure of the correlation coefficient between the age of best actress and actor in Oscars as\(\rho \).

For testing the claim, form the hypotheses:

\(\begin{array}{l}{H_o}:\rho = 0\\{H_a}:\rho \ne 0\end{array}\)

The samplesize is 12 (n).

The test statistic is computed as follows:

\(\begin{aligned} t &= \frac{r}{{\sqrt {\frac{{1 - {r^2}}}{{n - 2}}} }}\\ &= \frac{{ - 0.288}}{{\sqrt {\frac{{1 - {{\left( { - 0.288} \right)}^2}}}{{12 - 2}}} }}\\ &= - 0.951\end{aligned}\)

Thus, the test statistic is鈥0.951.

The degree of freedom is

\(\begin{aligned} df &= n - 2\\ &= 12 - 2\\ &= 10.\end{aligned}\)

The p-value is computed from the t-distribution table.

\(\begin{aligned} p{\rm{ - value}} &= 2P\left( {T < - 0.951} \right)\\ &= 0.364\end{aligned}\)

Thus, the p-value is 0.364.

Since the p-value is greater than 0.05, the null hypothes is fails to berejected.

Therefore, there is not enough evidence to conclude that there is no linear correlation between the age of actors and actresses.

05

Discuss if there exists a correlation between the ages of actors and actresses

It was not expected that actors and actresses have acorrelation between their ages.They can beparts of different movies.There is no clear logic for any association between their ages.

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Most popular questions from this chapter

In Exercises 5鈥8, we want to consider the correlation between heights of fathers and mothers and the heights of their sons. Refer to theStatCrunch display and answer the given questions or identify the indicated items.

The display is based on Data Set 5 鈥淔amily Heights鈥 in Appendix B.

Identify the multiple regression equation that expresses the height of a son in terms of the height of his father and mother.

Interpreting\({R^2}\)For the multiple regression equation given in Exercise 1, we get \({R^2}\)= 0.928. What does that value tell us?

Explore! Exercises 9 and 10 provide two data sets from 鈥淕raphs in Statistical Analysis,鈥 by F. J. Anscombe, the American Statistician, Vol. 27. For each exercise,

a. Construct a scatterplot.

b. Find the value of the linear correlation coefficient r, then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables.

c. Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot.

x

10

8

13

9

11

14

6

4

12

7

5

y

9.14

8.14

8.74

8.77

9.26

8.10

6.13

3.10

9.13

7.26

4.74

Testing for a Linear Correlation. In Exercises 13鈥28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

22. Crickets and Temperature A classic application of correlation involves the association between the temperature and the number of times a cricket chirps in a minute. Listed below are the numbers of chirps in 1 min and the corresponding temperatures in 掳F (based on data from The Song of Insects, by George W. Pierce, Harvard University Press). Is there sufficient evidence to conclude that there is a linear correlation between the number of chirps in 1 min and the temperature?

Actress

28

30

29

61

32

33

45

29

62

22

44

54

Actor

43

37

38

45

50

48

60

50

39

55

44

33

Interpreting the Coefficient of Determination. In Exercises 5鈥8, use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables.

Pizza and Subways r = 0.992 (x = cost of a slice of pizza, y = subway fare in New York City

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