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Chapter 10: Q2BSC (page 468)
Interpreting r For the same two variables described in Exercise 1, if we find that r = 0, does that indicate that there is no association between those two variables?
No, 0 correlation does not indicate that there is no association between the two variables.
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In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal).
Which regression equation is best for predicting city fuel consumption? Why?
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 CPI/subway fare data from the preceding exercise and find
the best predicted subway fare for a time when the CPI reaches 500. What is wrong with this prediction?
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 \({R^2}\)
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).
If you had computed the value of the linear correlation coefficient to be 1.500, what should you conclude?
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 pizza costs and subway fares to find the best predicted
subway fare, given that the cost of a slice of pizza is $3.00. Is the best predicted subway fare likely to be implemented?
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