/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Q22BSC Testing for a Linear Correlation... [FREE SOLUTION] | 91影视

91影视

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

Short Answer

Expert verified

The scatterplot is shown below:

The value of the correlation coefficient is 0.874.

The p-value is 0.005.

There is enough evidence to support the claim for a linear correlation between chirps in one minute and temperature.

Step by step solution

01

Given information

The data is recorded forchirps of crickets and temperatures in degrees Fahrenheit.

Chirps in 1 min

Temperature

882

69.7

1188

93.3

1104

84.3

864

76.3

1200

88.6

1032

82.6

960

71.6

900

79.6

02

Sketch a scatterplot

Scatterplot projects a paired set of observationsontwo axes scaled for the two variables.

Steps to sketch a scatterplot:

  1. Describe two axes, x and y, for chirps in 1 minute and temperature, respectively.
  2. Mark the points on the graph.

The graph is shown below.

03

Compute the measure of the correlation coefficient

The correlation coefficient formula is

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

Describe variables x and y as chirps in 1 minute and temperature, respectively.

The valuesare listed in the table below:

x

y

\({x^2}\)

\({y^2}\)

\(xy\)

882

69.7

777924

4858.09

61475.4

1188

93.3

1411344

8704.89

110840.4

1104

84.3

1218816

7106.49

93067.2

864

76.3

746496

5821.69

65923.2

1200

88.6

1440000

7849.96

106320

1032

82.6

1065024

6822.76

85243.2

960

71.6

921600

5126.56

68736

900

79.6

810000

6336.16

71640

\(\sum x = 8130\)

\(\sum y = 646\)

\(\sum {{x^2}} = 8391204\)

\(\sum {{y^2} = } \;52626.6\)

\(\sum {xy\; = \;} 663245.4\)

Substitute the values in the formula:

\(\begin{aligned} r &= \frac{{8\left( {663245.4} \right) - \left( {8130} \right)\left( {646} \right)}}{{\sqrt {8\left( {8391204} \right) - {{\left( {8130} \right)}^2}} \sqrt {8\left( {52626.6} \right) - {{\left( {646} \right)}^2}} }}\\ &= 0.874\end{aligned}\)

Thus, the correlation coefficient is 0.874.

04

Step 4:Conduct a hypothesis test for correlation

Definethe actual measure of the correlation coefficient between chirps and temperature 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 8(n).

The test statistic is computed as follows:

\(\begin{aligned} t &= \frac{r}{{\sqrt {\frac{{1 - {r^2}}}{{n - 2}}} }}\\ &= \frac{{0.874}}{{\sqrt {\frac{{1 - {{\left( {0.874} \right)}^2}}}{{8 - 2}}} }}\\ &= 4.406\end{aligned}\)

Thus, the test statistic is 4.406.

The degree of freedom is

\(\begin{aligned} df &= n - 2\\ &= 8 - 2\\ &= 6.\end{aligned}\)

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

\(\begin{aligned} p{\rm{ - value}} &= 2P\left( {t > 4.406} \right)\\ &= 0.0045\\ &\approx 0.005\end{aligned}\)

Thus, the p-value is 0.005.

Since thep-value is lesser than 0.05, the null hypothesis is rejected.

Therefore, there is enough evidence to conclude a linear correlation between chirps in 1 minute and temperature.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

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.)

Revised mpg Ratings Listed below are combined city-highway fuel economy ratings (in mi>gal) for different cars. The old ratings are based on tests used before 2008 and the new ratings are based on tests that went into effect in 2008. Is there sufficient evidence to conclude that there is a linear correlation between the old ratings and the new ratings? What do the data suggest about the old ratings?

Old

16

27

17

33

28

24

18

22

20

29

21

New

15

24

15

29

25

22

16

20

18

26

19

\({s_e}\)Notation Using Data Set 1 鈥淏ody Data鈥 in Appendix B, if we let the predictor variable x represent heights of males and let the response variable y represent weights of males, the sample of 153 heights and weights results in\({s_e}\)= 16.27555 cm. In your own words, describe what that value of \({s_e}\)represents.

In Exercises 9鈥12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 鈥淐ar Measurements鈥 in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal).

Which regression equation is best for predicting city fuel consumption? Why?

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.)

Sports Repeat the preceding exercise using diameters and volumes.

Interpreting r. In Exercises 5鈥8, use a significance level of A = 0.05 and refer to the accompanying displays.

5. Bear Weight and Chest Size Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in Data Set 9 鈥淏ear Measurements鈥 in Appendix B; results are shown in the accompanying Statdisk display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight?

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.