In Exercises 5–8, 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 “Family Heights” in Appendix B.

Identify the following:

a. The P-value corresponding to the overall significance of the multiple regression equation

b. The value of the multiple coefficient of determination\({R^2}\).

c. The adjusted value of \({R^2}\)

Short Answer

Expert verified

The P-value is less than 0.0001.

The multiple coefficient of determinationis 0.3649.

The adjusted multiple coefficient of determinationis 0.3552.

Step by step solution

01

Given information

The multiple regression output is provided.

02

Determine the P-value

The p-value, which determines the overall significance of the model, is obtained for the F-test in ANOVA output.

From the provided output, the P-value can be observedin the last column oftheanalysis of variance table for multiple regression model.

Therefore, the P-value is less than 0.0001.

03

State the multiple coefficient of determination

The multiple coefficient of determination is the R-squared measure of the regression output.

From the provided output, themultiple coefficient of determination can be observed in the summary of fit.

Therefore, the multiple coefficient of determinationis 0.3649.

04

State the adjusted value of the multiple coefficient of determination

The adjusted R-squared measure is also computed for determining the accuracy of prediction in the model.

From the provided output, the adjusted value of themultiple coefficient of determination can be observed in the summary of fit.

Therefore, the adjusted multiple coefficient of determinationis 0.3552.

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Most popular questions from this chapter

The following exercises are based on the following sample data consisting of numbers of enrolled students (in thousands) and numbers of burglaries for randomly selected large colleges in a recent year (based on data from the New York Times).

Repeat the preceding exercise, assuming that the linear correlation coefficient is r= 0.997.

Exercises 13–28 use the same data sets as Exercises 13–28 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 foot lengths and heights to find the best predicted height of a male

who has a foot length of 28 cm. Would the result be helpful to police crime scene investigators in trying to describe the male?

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.)

Sports Repeat the preceding exercise using diameters and volumes.

Interpreting a Computer Display. In Exercises 9–12, refer to the display obtained by using the paired data consisting of Florida registered boats (tens of thousands) and numbers of manatee deaths from encounters with boats in Florida for different recent years (from Data Set 10 in Appendix B). Along with the paired boat, manatee sample data, StatCrunch was also given the value of 85 (tens of thousands) boats to be used for predicting manatee fatalities.

Testing for Correlation Use the information provided in the display to determine the value of the linear correlation coefficient. Is there sufficient evidence to support a claim of a linear correlation between numbers of registered boats and numbers of manatee deaths from encounters with boats?

Interpreting\({R^2}\)In Exercise 2, the quadratic model results in = 0.255. Identify the percentage of the variation in Super Bowl points that can be explained by the quadratic model relating the variable of year and the variable of points scored. (Hint: See Example 2.) What does the result suggest about the usefulness of the quadratic model?

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