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91影视

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?

Short Answer

Expert verified

There is enough evidence to support the claim that there is a linear correlation between weights and chest sizes.

The chest sizes are easier to be recorded than weights.

As weights and chest sizes are highly correlated, chest sizes can be used to predict weights.

Step by step solution

01

Given information

The level of significance is 0.05.

The output for the hypothesis test for correlation between weights of bears and chest sizes are known.

02

Hypothesis test for correlation between weights and chest size

Let\(\rho \)be the true correlation measure between the two variables; weights and chest sizes.

The hypotheses be formulated as follows:

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

From the output the following measures are known,

\(\begin{array}{c}r = 0.963\\p{\rm{ - value}} = 0.000\end{array}\)

As the p-value is lesser than 0.05, the null hypothesis is rejected.

Thus, there is enough evidence at 0.05 level of significance to conclude that there is a significant correlation between the two variables; weight and chest sizeof bears.

03

Measurement of variables

Of the two measures, it is not easy to weigh the bears on a scale as they are too heavy to be lifted. On the other hand, the chest sizes are comparatively easier to be recorded for the bears in anethesized state.

04

Predict the measure of weight from chest size

The weight is highly correlated with the chest sizes, and hence the variable can be used to predict the weights.

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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?

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16

27

17

33

28

24

18

22

20

29

21

New

15

24

15

29

25

22

16

20

18

26

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Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

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Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Using the listed old/new mpg ratings, find the best predicted new

mpg rating for a car with an old rating of 30 mpg. Is there anything to suggest that the prediction is likely to be quite good?

Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Use the shoe print lengths and heights to find the best predicted height of a male who has a shoe print length of 31.3 cm. Would the result be helpful to police crime scene investigators in trying to describe the male?

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Lemon Imports

230

265

358

480

530

Crash Fatality Rate

15.9

15.7

15.4

15.3

14.9

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