1. Subsequent to the selection of the optimal regression relationship and the computation of the regression prediction equation, a) b) c) d) Residual analysis should be performed in relation to the independent variable. No further analysis is necessary; you use the model for prediction. Residual analysis should be performed in relation to the dependent variable. Plots of the residuals should be checked to make certain that every observation was given full consideration.

2. The smallest value that the standard error of estimate S E can assume is: a. -1 b. 0 c. 1 d. -2 3. In regression analysis, if the correlation coefficient between x and y is 1.0, then: a. the sum of squares for error must be 1.0 b. the sum of squares for regression must be 1.0 c. the sum of squares for error must be 0.0 d. the sum of squares for regression must be 0.0 e. the slope has to be 1.0 4. The regression equation represents a line that is fitted to the observed points so that the sum of squares of the vertical deviations from the line is: a.) Zero b.) The greatest possible. c.) Equal to the cross product of Y and X d.) None of the above. 5. A perfect linear relationship is indicated when ρ takes a value of: a.) Zero b.) -1 c.) +1 d.) Either +1 or -1 6. If b1 = -1.6 the coefficient of determination is .81, then the sample correlation will be: a.) .81 b.) .90 c.) -.90 d.) None of the above 7. In regression analysis, the residual represents the: a.) Difference between the actual y values and their predicted values. b.) Difference between the actual x values and their predicted values. c.) Square root of the coefficient of determination. d.) Change in y per unit change in x.

8. Which value of the coefficient of correlation r indicates a stronger correlation than 0.65? a.) .55 b.) -.75 c.) .60 d.) -.45 9. If the coefficient of correlation is -0.80, the percentage of the variation in y that