# Question

Refer to the previous exercise. The data file contains price, weight, and type of suspension system (FU = full, FE = front-end in the scatterplot shown).

a. Do you observe a linear relationship? Is the single regression line, which is ŷ = 1896 - 40.45x, the best way to fit the data? How would you suggest fitting the data?

b. Find separate regression equations for the two suspension types. Summarize your findings.

c. The correlation for all 12 data points is r = -0.32. If the correlations for the full and front-end suspension bikes are found separately, how do you believe the correlations will compare to r = 0.32? Find them, and interpret.

d. You see a mountain bike advertised for $700 that weighs 28.5 lb. The type of suspension is not given. Would you predict that this bike has a full or a frontend suspension? Statistically justify your answer.

a. Do you observe a linear relationship? Is the single regression line, which is ŷ = 1896 - 40.45x, the best way to fit the data? How would you suggest fitting the data?

b. Find separate regression equations for the two suspension types. Summarize your findings.

c. The correlation for all 12 data points is r = -0.32. If the correlations for the full and front-end suspension bikes are found separately, how do you believe the correlations will compare to r = 0.32? Find them, and interpret.

d. You see a mountain bike advertised for $700 that weighs 28.5 lb. The type of suspension is not given. Would you predict that this bike has a full or a frontend suspension? Statistically justify your answer.

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