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Interpreting the Coefficient of Determination. In Exercises 5–8, 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.

Crickets and Temperature r = 0.874 (x = number of cricket chirps in 1 minute, y = temperature in °F)

Short Answer

Expert verified

The coefficient of determination is 0.764.

The percentage of variation that can be explained by the linear association between the number of cricket chirps in 1 minute and the temperature is 76.4%.

23.6% of the variation in the response variable (temperature) is explained by other factors and random variation.

Step by step solution

01

Given information

The linear correlation coefficient between the number of cricket chirps in 1 min and the temperature is 0.874.

02

Coefficient of determination

The coefficient of determination is obtained by taking the square of linear correlation coefficient between the two variables.

Here, the linear correlation coefficient (r) between the number of cricket chirps in 1 minute and the temperature is 0.874.

Thus,

\(\begin{array}{c}{\rm{Coefficient}}\;{\rm{of}}\;{\rm{determination}} = {r^2}\\ = {0.874^2}\\ = 0.764\end{array}\)

Therefore, the value of the coefficient of determination is 0.764.

03

Percentage of variation

Here,

\(\begin{array}{c}{r^2} = 0.764\\ = \frac{{76.4}}{{100}} \times 100\% \\ = 76.4\% \end{array}\)

Therefore, the percentage of the variation explained by the linear association of the number of cricket chirps in 1 min and the temperature is 76.4%.

The remaining \(100\% - 76.4\% = 23.6\% \) variation is explained by other factors and random variation.

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Most popular questions from this chapter

Testing for a Linear Correlation. In Exercises 13–28, 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.)

Pizza and the Subway The “pizza connection” is the principle that the price of a slice of pizza in New York City is always about the same as the subway fare. Use the data listed below to determine whether there is a significant linear correlation between the cost of a slice of pizza and the subway fare.

Year

1960

1973

1986

1995

2002

2003

2009

2013

2015

Pizza Cost

0.15

0.35

1

1.25

1.75

2

2.25

2.3

2.75

Subway Fare

0.15

0.35

1

1.35

1.5

2

2.25

2.5

2.75

CPI

30.2

48.3

112.3

162.2

191.9

197.8

214.5

233

237.2

Notation Twenty different statistics students are randomly selected. For each of them, their body temperature (°C) is measured and their head circumference (cm) is measured.

a. For this sample of paired data, what does r represent, and what does \(\rho \)represent?

b. Without doing any research or calculations, estimate the value of r.

c. Does r change if the body temperatures are converted to Fahrenheit degrees

Coefficient of Determination Using the heights and weights described in Exercise 1, the linear correlation coefficient r is 0.394. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide?

Testing for a Linear Correlation. In Exercises 13–28, 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 “Oscar Winner Age” 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

let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level.

For 30 recent Academy Award ceremonies, ages of Best Supporting Actors (x) and ages of Best Supporting Actresses (y) are recorded. The 30 paired ages yield\(\bar x = 52.1\)years,\(\bar y = 37.3\)years, r= 0.076, P-value = 0.691, and

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