. Calculate the least squares regression line that best ts your data. Interpret (in context) what the slope estimate tells you about prices and ages of your used car model. Explain why the sign (positive/ negative) makes sense. . Produce a scatterplot of the relationship with the regression line on it. . Produce appropriate residual plots and comment on how well your data appear to t the conditions for a linear model. Don't worry about doing transformations at this point if there are problems with the conditions. . Find the car in your sample with the largest residual (in magnitude - positive or negative). For that car, nd its standardized and studentized residual. Based on these residuals, could this value be considered inuential? . Determine the leverages for the cars with the ten largest absolute residuals. What do these leverage values say about the potential for each of these ten cars to be inuential on your model? . Determine the Cook's distances for the cars with the ten largest absolute residuals. What do these Cook's distance values say about the inuence of each of these ten cars on your model? . Compute and interpret in context a 90% condence interval for the slope of your regression line. . Test the strength of the linear relationship between your variables using each of the three methods (test for correlation, test for slope, ANOVA for regression). Include hypotheses for each test and your conclusions in the context of the