# Answer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade

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3 10. The correlation between the ages of the husbands and wives in the United States was which of the following? A B C. zero D E Answer: B. Men usually marry women of about their own age, but of course there are enough exceptions that the correlation is not perfect. 11. A study is done of the impact of a drug on body temperature and blood pressure. We have three observations: Temperature(F) Pressure (mg) Temp - 97 Pressure The correlation coefficient is closest to A B C D. zero E Hint: a good guess is possible by plotting the data carefully. a better one is possible by subtracting the lowest number in each list from all the other numbers in that list. Answer: A. Transform the first column of data by subtracting 97 from each number; transform the other column by subtracting 80 from each. The result is given above; the line Pressure = 10*Temp holds perfectly for all points. 12. The correlation between the average midterm score of each of 10 classes of statistics and the average final exam score was found to be A statistics instructor concludes that if a student has a B on the midterm, he has an 85 percent chance of a B on the final. This conclusion is: A. correct B. incorrect, because it is an ecological correlation, which means there is usually much more variance of individuals than of averages C. incorrect, because it is an average correlation, and there is usually much more variance of averages than of individuals. Answer: B.

4 13. A regression tries to predict student GPA percentile (on a zero-100 scale) from their SAT-Math score. You are to make the relevant prediction for a student who has a SAT math score of 600 (note that SAT scores go from 200 to 800). He would be expected to be closest to the -- percentile of GPA on the basis of the regression output below: Coefficient t-stat Intercept SAT-Math R-squared: 0.36 A. 10th percentile B. 20th percentile C. 60 th percentile D. 80th percentile E. 100th percentile Answer: D. Write the regression equation as GPA percentile = * SAT math, so GPA percentile = * 600 = We know, on the basis of the above regression, that the strongest part of the explanation of the score is due to the coefficent of the: A. intercept term, because the coefficient is larger. B. intercept term, because the t-stat is smaller C. SAT-math term, because the coefficient is smaller. D. SAT-math term, because the t-stat is larger. Answer: D. The size of the t-statistic, not the size of the coefficient, indicates the explanatory strength of a variable. 15. We know that the correlation coefficient between SAT-math and the GPA percentile is closest to: A. zero B C D E Answer: C. The R-squared is the square of the correlation coefficent r, so r = sqrt(.36) = If we knew that the SD of the SAT math score was 100 and the SD of the GPA percentile was 25, we could calculate the standard error of the regression (the RMSE) to be closest to (look at the next question for a hint at the formula): A. 20 B. 40 C. 60 D. 80 E The formula for the standard error of the regression (RMSE) is A. (1 - R2) * SD(x) B. Covariance (x,y) / SD(x) * SD(y) C. sqrt(1- R2) * SD(y) D. sqrt(1-r2) * SD(x) E. r * SD(y) / SD(x) Answer: C. The standard error of the regression gets smaller as the R-squared gets larger; if R-sq = 1, error = The formula for the slope of the regression line is (same options as the previous question) Answer: E 19. The formula for the correlation coefficient is (same options as the previous question) Answer: B

5 20. We plot the data lying behind the question 13 regression with the R command >>> Plot(SAT-Math, GPA) To draw a regression line on the plot, use the R command: A. >>> abline(0.36) B. >>> abline(20, 0.1) C. >>> abline (0.3, 5.0) D. >>> abline (0.1, 5.0) E. We cannot draw a regression line because we do not know the correlation coefficient. Answer: B 21. The regression line is drawn so that: A. The line goes through more points than any other possible line, straight or curved B. The line goes through more points than any other possible straight line. C. The same number of points are below and above the regression line. D. The sum of the absolute errors is as small as possible. E. The sum of the squared errors is as small as possible. Answer: E 22. In a regression, the --- that the standard error of the regression is, the greater the accuracy of the prediction will be. A. smaller. B. larger C. we do not know unless we know whether the slope of the regression is positive or negative. Answer: A. 23. In order for the regression technique to give the best and minimum variance prediction, all the following conditions must be met, EXCEPT for: A. The relation is linear. B. We have not omitted any significant variable. C. Both the X and Y variables (the predictors and the response) are normally distributed. D. The residuals (errors) are normally distributed. E. The variance around the regression line is about the same for all values of the predictor. Answer: C 24. Note that the last question said that all the conditions are needed for a best and minimum variance prediction. Very few real regressions can meet all the criteria, but their predictions may still be quite good -- at least unbiased, though not perhaps minimum variance. The cases in which the predictions will almost certainly be biased are: A. conditions A and B are not met. In case C, there is no problem, so regression will give the best and minimum variance prediction. In cases D and E, regression will not give the minimum variance prediction. 25. Ecological correlations are weaker than other types of correlation because: C. They are based on averages rather than individuals 26. If a regression has the problem of heteroscedasticity, A. The predictions it makes will be wrong on average. B. The predictions it makes will be correct on average, but we will not be certain of the RMSE C. It will also have the problem of an omitted variable or variables. D. It will also be based on a non-linear equation. Answer: B. Heteroscedasticity implies that the variance will differ for different values of the regressor.

6 The following regression is based on a randomly chosen subset of the ecgrow data set, with 30 countries. Call: ols(grate ~ invest + edu + gdp60 + openness + pop) Coefficients: Estimate t-stat Conf.interval (0.95) (Intercept) (-0.96, 1.28) invest (0.05, 0.14) edu (-0.12, 0.22) gdp (-0.05,-0.02) openness (1.19, 2.62) pop (-0.24, 0.35) SE of regression(or RMSE) = R-squared = If we had to drop two variables from the regression, we would pick: A. Edu and pop, because they have the lowest t-stats. B. GDP60 and edu, because they have the lowest coefficients. C. GDP60 and pop, because they have the lowest t-stats D. GDP60 and openness, because they have the highest t-stats in absolute value. Answer: A. 28. If we wanted to make a 95 percent confidence interval prediction, we would make a point prediction with the equation, but place around that a margin of error of about: A. +/ B. +/ C *.6493 D. +/- 2 *.9155 E. +/ Answer: D Suppose we had a much simpler regression, obtained with the command >>> regress(gdp85 ~ gdp60) Coefficients: Estimate t-stat Conf.interval (0.95) (Intercept) (0.63, 7.03) gdp (0.94, 1.13) SE of regression(or RMSE) = R-squared = If GDP in 1960 were 20 percent of US GDP (so gdp60 were 20), our prediction for gdp85 would be closest to: A. 20 B. 25 C. 30 D. 40 E. 50 Answer: B, since gdp85 = * (20) = = 24.63

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