3rd Edition

ISBN: 9780135163146

Author: Gould

Publisher: PEARSON

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Chapter 4, Problem 76CRE

Heights and Weights of Men The table shows the heights (in inches) and weights (in pounds) of 14 college men. The scatterplot shows that the association is linear enough to proceed.

Chapter 4, Problem 76CRE, Heights and Weights of Men The table shows the heights (in inches) and weights (in pounds) of 14 , example 1

a. Find the equation for the regression line with weight (in pounds) as the response and height (in inches) as the predictor. Report the slope and the intercept of the regression line, and explain what they show. If the intercept is not appropriate to report, explain why.

b. Find the correlation between weight (in pounds) and height (in inches).

c. Find the coefficient of determination and interpret it.

d. If you changed each height to centimeters by multiplying heights in inches by 2.54, what would the new correlation be? Explain.

e. Find the equation with weight (in pounds) as the response and height (in inches) as the predictor, and interpret the slope.

f. Summarize what you found: Does changing units change the correlation? Does changing units change the regression equation?

Chapter 4, Problem 76CRE, Heights and Weights of Men The table shows the heights (in inches) and weights (in pounds) of 14 , example 2

Compare Compare Correlation is the measure of the degree of the relationship between the variables. In regression, the relationship between the variables to predict one by another is assessed. In correlation there is no cause and effect between the variables, whereas in regression there is a cause and effect between the variables. By using correlation, we cannot predict anything, but regression is a predictive tool. Coefficients are symmetrical in correlation but in regression they are asymmetrical. Correlation is independent of change in origin and scale, but regression is not independent of change in scale.

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Chapter 4, Problem 75CRE

Chapter 4, Problem 77CRE

Chapter 4 Solutions

INTRODUCTORY STATISTICS (LOOSELEAF)

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Solution Summary: The author explains the equation for regression line with weight as response and height (in inches) as predictor.

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