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AP Statistics 5.4: Residual Plots

Use a residual plot to assess a linear regression model, looking for random scatter versus curvature or another systematic pattern.

Syllabus
Effective Fall 2025
Course
AP Statistics

5.4.C—Describe the form of association of bivariate data using residual plots question 1

[Maximum number: 4]

Jamal is researching the characteristics of a car that might be useful in predicting the fuel consumption rate (FCR); that is, the number of gallons of gasoline that the car requires to travel 100 miles under conditions of typical city driving. The length of a car is one explanatory variable that can be used to predict FCR. Graph I is a scatterplot showing the lengths of 66 cars plotted with the corresponding FCR. One point on the graph is labeled A.

GRAPH I

GRAPH I

Jamal examined the scatterplot and determined that a linear model would be a reasonable way to express the relationship between FCR and length. A computer output from a linear regression is shown below.
Linear Fit
FCR=1.595789+0.0372614\mathrm{FCR}=-1.595789+0.0372614 * Length
Summary of Fit

Table for Question 5.4.C—Describe the form of association of bivariate data using residual plots question 1 — AP Statistics

Question (a)

(a)

Write a few sentences to compare the association between the variables in graph II with the association between the variables in graph III.

Question (b)

(b)

Jamal wants to predict FCR using length and one of the other variables, engine size or wheel base. Based on your response to part (c), which variable, engine size or wheel base, should Jamal use in addition to length if he wants to improve the prediction? Explain why you chose that variable.

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