1. x = handguns y = violent crime both are per hundred thousand population 1.What proportion of variation in crime is explained by variation in handgun ownership? 2.What is the regression equation? 3.What crime rate would we predict at an ownership rate of 200? Of 400? 4. Is there a significant relationship between crime and gun ownership? 5.What test did you conduct to answer q. 4? 6.What is the p-value of that test? 7.What is the standard error of the estimate? 8.What is the standard error of the x coefficient? 9. What is the quantity s y.x /s b1 used in calculating confidence intervals?

2. Residual Plot: Residuals plotted on the y axis against handgun ownership on the x axis. Is there evidence that the regression assumptions are violated? (Yes: This is a classic picture of autocorrelation.)

3. Do these residuals appear to have come from a normal distribution? (No. Residuals from a normal distribution lie on a straight line; this one diverges too far from straight.)

4. y = selling price of house x variables: Acres – lot size Size of house in square feet Whether or not the house has central air conditioning 1.What proportion of variation in selling price is explained by these three variables? 2.What is the regression equation? Predict a price for George’s house, which sits on a one-half acre lot, measures 1800 square feet and does not have central air. 3.Is there a significant relationship here? How do you know? 4.Which of the x variables are significantly related to price? Which are not? 5.Which of these variables would be considered a “dummy” variable? 6. Calculate a 95% prediction interval for the price of George’s house. Use t = 2.