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The cases that make up this dataset are types of

The cases that make up this dataset are types of cars. The data include the engine size or displacement (in liters) and price of 318 vehicles sold in the United States in 2011. Use price as the response and engine displacement as the explanatory variable.

(a) Compare the two plots: Price versus Displacement and log10 Price versus log10 Displacement. Does either seem suited, even approximately, to the SRM?

(b) Would the linear equation, particularly the slope, have the same meaning in both cases considered in part (a)?

(c) Fit the preferred equation as identified in part (a); then use this rough test for equal variance. First, divide the plot in half at the median of the explanatory variable in your model. Second, count the number of points that lie outside the 95% prediction limits in each half of the data. Are these randomly distributed between the two halves? Are the error terms homoscedastic?

(a) Compare the two plots: Price versus Displacement and log10 Price versus log10 Displacement. Does either seem suited, even approximately, to the SRM?

(b) Would the linear equation, particularly the slope, have the same meaning in both cases considered in part (a)?

(c) Fit the preferred equation as identified in part (a); then use this rough test for equal variance. First, divide the plot in half at the median of the explanatory variable in your model. Second, count the number of points that lie outside the 95% prediction limits in each half of the data. Are these randomly distributed between the two halves? Are the error terms homoscedastic?

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