use the chart to answer ALL of the following parts to the question.

1 Age (years) 1 Asking Price ($000) 22.4 7.9 8.1 5 12.6 6.6 6 11.5 Nm+ ionog fo$ 99 1 0.6 0.5 15 2.35 4.8 15 2.55 2.95 10 4.25 3.85 Do the residuals suggest a problem with the linear equation? OA. No, because there is a linear trend in the data, OB. No, because there is a curved trend in the data. OC. Yes, because there is a linear trend in the data OD. Yos, because there is a curved vend in the data. (d) Fit the equation Estimated Price = By + b,logage. Make sure to use natural logarithms when transforming the data. If there was a problem with the linear equation in part (c), do the residuals from this fit "fix" the problem? Estimated Price (in thousands of $) - - -Cplogage (Round to three decimal places as needed.) If there was a problem with the linear equation in part (c), do the residuals from this fit fix the problem? A. No, because the residuals from the log Ago) fit appear more random OB. Yes, because the residuals from the log(Age) fit appeor more random, OC. There was no problem with the linear fit in part (c). OD. Yes, because curved patterns can always be fixed with a logarithmic, Inverse, or root transformation. What does this graph have to say about the effects of increasing age on resale value? OA. The log equation implies that price changes at an increasing rate as cars age. OB. The log equation implies that price changes more rapidly among nower used cars, then slows as cars age. OC. The log equation implies that price changes at a constant rate as cars age. OD. The log equation implies that price changes more rapidly among older used cars, and remains nearly static among newer used cars. Compare the values of r2 and s, between these two equations. Does this comparison agree with your impression of the better model? Should these summary statistics be compared? Age logAge 2-0 2- SS thousand Se - $ thousand (Round to three decimal places as needed.) () Interpret the intercept and slope in the equation Estimated Price = bob, logAge. Choose the correct answer below. O A. The intercopt is the price of a car that is 1-year old. The slope shows that a 1% Increase in age results in a price change of approximately by thousand dollars. O B. The intercept should not be interpreted because it is the price of a D-year old car. The slope shows that a 1% increase in age results in a price change of approximately by thousand dollars. oc. The intercept is the price of a car that is 1-year old. The slope shows that a 1% increase in age results in a price change of approximately (b/100) thousand dollars OD. The intercept should not be interpreted because it is the price of a 0-year old car. The slope shows that a 1% increase in age results in a price change of approximately (61/100) thousand dollars. (h) Compare the change in asking price for cars that are 1 and 2 years old to the difference between cars that are 8 and 9 years old. Use the equation with the log of age as the explanatory variable. Is this difference the same or different? The price drop is for these two changes because the logistic regression equation is