I believe that cars with more mileage are worth less money. I decide to run a statistical analysis at the a = 0.01 significance level to test my claim. From a large website specializing in selling used cars, I sample 6 used 2012 sedans at random. For each sedan, I record its mileage and the price that the car is listed at. Data are as follows: Mileage Price 95060 8920 76120 11800 111140 9240 62320 17670 86310 12700 50620 17340 Assume that responses follow the model - Price Bo+B1(Mileage) + Where the error terms & satisfy the standard regression assumptions. a. (2pts) Compute the correlation between price and mileage. (Round answers to 4 decimal points) Correlation: b. (2pts) Which of the following is NOT an assumption made on the standard regression assumptions? They all have the same standard deviation They all have mean (expected value) 0 O They all follow a binomial distribution They are all independent of each other c. (2pts) Which of the following is true about the regression line? The regression line minimizes the MSE of forecast errors. The regression line is the line with the largest prediction accuracy. O The regression line minimizes the sum of the squares of the residuals. Regression may only be performed if the standard regression assumptions hold. O All of the above. d. (1pt) Is the slope of the regression line positive or negative? Positive Negative Neither There is not enough information to tell. e. (1pt) What is the null hypothesis for your hypothesis test? H: Bo > 0 H: Bo < 0 H: Bo = 0 O Ho: Bo # 0 O Ho: B1>0 Ho: B1 <0 ho: 1=0 b10 not enough information to tell f. (1pt) what is the alternative hypothesis for your test? ha: bo> 0 Ha: Bo <0 test