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Driving speed and fuel consumption Exercise 9 (page 171) gives data on the fuel consumption y of a car at various speeds x. Fuel consumption is measured in liters of gasoline per 100 kilometers driven, and speed is measured in kilometers per hour. A statistical software package gives the least-squares regression line y^=11.058–0.01466x. Use the residual plot to determine if this linear model is appropriate.

Short Answer

Expert verified

No, it is not appropriate.

Step by step solution

01

Given information

The figure is:

02

Concept

The least-squares regression line reduces the sum of squares of vertical distances between the observed points and the line to zero.

03

Explanation

The regression equation is:

y=11.0580.01466x

The data points on the residual plot should follow a linear pattern, which is an important property of an adequate linear regression model. The data points in the presented residual plot do not form a linear pattern, making the model incorrect. Simply put, the existence of curvature in the residual plot indicates that the model is ineffective.

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Most popular questions from this chapter

Drilling down beneath a lake in Alaska yields chemical evidence of past changes in climate. Biological silicon, left by the skeletons of single-celled creatures called diatoms, is a measure of the abundance of life in the lake. A rather complex variable based on the ratio of certain isotopes relative to ocean water gives an indirect measure of moisture, mostly from snow. As we drill down, we look further into the past. Here is a scatterplot of data from 2300 to 12,000 years ago:

a. Identify the unusual point in the scatterplot and estimate its x and y coordinates.

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iii. the standard deviation of the residuals.

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b. y^=87.974+1.01216x

c. y^=87.974−0.060934x

d. y^=−0.060934+1.01216x

e. y^=−0.060934+87.947x

a. Is a line an appropriate model to use for these data? Explain how you know.

b. Find the correlation.

c. What is the equation of the least-squares regression line? Define any variables that you

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d. Interpret the values of s and r2.

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