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What is the relationship between the linear correlation coefficient rand the slope\({b_1}\)of a regression line?

Short Answer

Expert verified

There is a direct relationship between the correlation coefficient and slope of the regression line.

Step by step solution

01

Define the correlation coefficient r

A correlation coefficient provides a measure for the magnitude and direction of linear association between the variables.

02

State the slope of the regression line

The slope of the regression line helps in describing the level ofchange in y variable due to the unit change in x variable.

03

Describe the relationship between the correlation coefficient and the slope

The slope is computed as,

\({b_1} = r\frac{{{s_y}}}{{{s_x}}}\)

where\({b_1}\)represents the slope of regression equation, r represents the correlation coefficient,\({s_y}\)represents the standard deviation of y and\({s_x}\)represents the standard deviation of x.

It can be observed from the above formula that as the value of correlation increases, the value of slope increases. Similarly, as the value of correlation decreases, the value of slope decreases.

Thus, the relationship between the correlation coefficient r and the slope of the regression line\({b_1}\)is a direct relationship.

Also, this implies, if the value of r is positive, the slope value is also positive. And, if the value of r is negative, then the slope value is negative as the measure of ratio for standard deviations must always be positive.

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Most popular questions from this chapter

Different hotels on Las Vegas Boulevard (โ€œthe stripโ€) in Las Vegas are randomly selected, and their ratings and prices were obtained from Travelocity. Using technology, with xrepresenting the ratings and yrepresenting price, we find that the regression equation has a slope of 130 and a y-intercept of -368.

a. What is the equation of the regression line?

b. What does the symbol\(\hat y\)represent?

In Exercises 5โ€“8, we want to consider the correlation between heights of fathers and mothers and the heights of their sons. Refer to the

StatCrunch display and answer the given questions or identify the indicated items.

The display is based on Data Set 5 โ€œFamily Heightsโ€ in Appendix B.

A son will be born to a father who is 70 in. tall and a mother who is 60 in. tall. Use the multiple regression equation to predict the height of the son. Is the result likely to be a good predicted value? Why or why not?

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Lemons and Car Crashes Listed below are annual data for various years. The data are weights (metric tons) of lemons imported from Mexico and U.S. car crash fatality rates per 100,000 population (based on data from โ€œThe Trouble with QSAR (or How I Learned to Stop Worrying and Embrace Fallacy),โ€ by Stephen Johnson, Journal of Chemical Information and Modeling, Vol. 48, No. 1). Is there sufficient evidence to conclude that there is a linear correlation between weights of lemon imports from Mexico and U.S. car fatality rates? Do the results suggest that imported lemons cause car fatalities?

Lemon Imports

230

265

358

480

530

Crash Fatality Rate

15.9

15.7

15.4

15.3

14.9

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 50 randomly selected speed dates, attractiveness ratings by males of their

female date partners (x) are recorded along with the attractiveness ratings by females of their male date partners (y); the ratings are from Data Set 18 โ€œSpeed Datingโ€ in Appendix B. The 50 paired ratings yield\(\bar x = 6.5\),\(\bar y = 5.9\), r= -0.277, P-value = 0.051, and\(\hat y = 8.18 - 0.345x\). Find the best predicted value of\(\hat y\)(attractiveness rating by female of male) for a date in which the attractiveness rating by the male of the female is x= 8.

Explore! Exercises 9 and 10 provide two data sets from โ€œGraphs in Statistical Analysis,โ€ 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

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