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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).

If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why?

Short Answer

Expert verified

HWY is the best predictor in the single variable study for predicting fuel consumption of the city.

Step by step solution

01

Given information

The table represents the predictor variables, P-value, \({R^2}\), Adjusted-\({R^2}\) and the regression equations.

02

State the criteria for best predictor variables

The p-value determines the significance of the variable in regression analysis. The R-squared measure is the coefficient of determination measure, which measures the sum of squares explained by the regression line.The R-squared adjusted measures the accuracy while considering the count of independent variables in the model. A good-fit model gives a better prediction.

The optimum level of each measure defines the best predictor in the regression model.

03

Identify the best predictor model

From the provided three models with one predictor variable, the model with predictor HWY (highway fuel consumption) has the:

  • smallest P-value is 0.000
  • highest measures of both\({R^2}\)and adjusted\({R^2}\)value which is 0.924 and 0.920 respectively.

This implies that HWY is the best predictor of the city’s fuel consumption variable.

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Most popular questions from this chapter

Critical Thinking: Is the pain medicine Duragesic effective in reducing pain? Listed below are measures of pain intensity before and after using the drug Duragesic (fentanyl) (based on data from Janssen Pharmaceutical Products, L.P.). The data are listed in order by row, and corresponding measures are from the same subject before and after treatment. For example, the first subject had a measure of 1.2 before treatment and a measure of 0.4 after treatment. Each pair of measurements is from one subject, and the intensity of pain was measured using the standard visual analog score. A higher score corresponds to higher pain intensity.

Pain Intensity Before Duragesic Treatment

1.2

1.3

1.5

1.6

8

3.4

3.5

2.8

2.6

2.2

3

7.1

2.3

2.1

3.4

6.4

5

4.2

2.8

3.9

5.2

6.9

6.9

5

5.5

6

5.5

8.6

9.4

10

7.6










Pain Intensity After Duragesic Treatment

0.4

1.4

1.8

2.9

6

1.4

0.7

3.9

0.9

1.8

0.9

9.3

8

6.8

2.3

0.4

0.7

1.2

4.5

2

1.6

2

2

6.8

6.6

4.1

4.6

2.9

5.4

4.8

4.1










Two Independent Samples The methods of Section 9-2 can be used to test the claim that two populations have the same mean. Identify the specific claim that the treatment is effective, then use the methods of Section 9-2 to test that claim. The methods of Section 9-2 are based on the requirement that the samples are independent. Are they independent in this case?

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?

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?

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.)

Pizza and the Subway The “pizza connection” is the principle that the price of a slice of pizza in New York City is always about the same as the subway fare. Use the data listed below to determine whether there is a significant linear correlation between the cost of a slice of pizza and the subway fare.

Year

1960

1973

1986

1995

2002

2003

2009

2013

2015

Pizza Cost

0.15

0.35

1

1.25

1.75

2

2.25

2.3

2.75

Subway Fare

0.15

0.35

1

1.35

1.5

2

2.25

2.5

2.75

CPI

30.2

48.3

112.3

162.2

191.9

197.8

214.5

233

237.2

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