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1 Class: Date: Regression Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Given the least squares regression line y8 = 5 2x: a. the relationship between x and y is positive. b. the relationship between x and y is negative. c. as x decreases, so does y. 2. A regression analysis between sales (in \$1,000) and advertising (in \$1,000) resulted in the following least squares line: y8 = x. This implies that: a. as advertising increases by \$1,000, sales increases by \$5,000. b. as advertising increases by \$1,000, sales increases by \$80,000. c. as advertising increases by \$5, sales increases by \$ Which of the following techniques is used to predict the value of one variable on the basis of other variables? a. Correlation analysis b. Coefficient of correlation c. Covariance d. Regression analysis 4. The residual is defined as the difference between: a. the actual value of y and the estimated value of y b. the actual value of x and the estimated value of x c. the actual value of y and the estimated value of x d. the actual value of x and the estimated value of y 5. Testing whether the slope of the population regression line could be zero is equivalent to testing whether the: a. sample coefficient of correlation could be zero b. standard error of estimate could be zero c. population coefficient of correlation could be zero d. sum of squares for error could be zero 6. A regression line using 25 observations produced SSR = and SSE = The standard error of estimate was: a b c If all the points in a scatter diagram lie on the least squares regression line, then the coefficient of correlation must be: a. 1.0 b. 1.0 c. either 1.0 or 1.0 d

2 8. If the coefficient of correlation is 0.60, then the coefficient of determination is: a b c d The standard error of estimate s ε is given by: a. SSE / (n 2) b. SSE / (n 2) c. SSE / (n 2) d. SSE / n If the standard error of estimate s ε = 20 and n = 10, then the sum of squares for error, SSE, is: a. 400 b. 3,200 c. 4,000 d. 40, In regression analysis, the coefficient of determination R 2 measures the amount of variation in y that is: a. caused by the variation in x. b. explained by the variation in x. c. unexplained by the variation in x. 12. In a regression problem, if the coefficient of determination is 0.95, this means that: a. 95% of the y values are positive. b. 95% of the variation in y can be explained by the variation in x. c. 95% of the y values are predicted correctly by the model. 13. The sample correlation coefficient between x and y is It has been found out that the p-value is when testing H 0 : ρ = 0 against the two-sided alternative H 1 : ρ 0. To test H 0 : ρ = 0 against the one-sided alternative H 1 : ρ > 0 at a significant level of 0.193, the p-value will be equal to a b c d In simple linear regression, which of the following statements indicates there is no linear relationship between the variables x and y? a. Coefficient of determination is 1.0. b. Coefficient of correlation is 0.0. c. Sum of squares for error is

3 15. In simple linear regression, the coefficient of correlation r and the least squares estimate b 1 of the population slope β 1 : a. must be equal. b. must have the same sign. c. are not related. 16. If the coefficient of correlation is 0.90, then the percentage of the variation in the dependent variable y that is explained by the variation in the independent variable x is: a. 90% b. 81% c. 95% 17. The width of the confidence interval estimate for the predicted value of y depends on a. the standard error of the estimate b. the value of x for which the prediction is being made c. the sample size d. All of these choices are true. 18. In a multiple regression model, the mean of the probability distribution of the error variable ε is assumed to be: a. 1.0 b. 0.0 c. k, where k is the number of independent variables included in the model. 19. In a multiple regression analysis involving 6 independent variables, the total variation in y is 900 and SSR = 600. What is the value of SSE? a. 300 b c For a multiple regression model the following statistics are given: Total variation in y = 250, SSE = 50, k = 4, and n = 20. Then, the coefficient of determination adjusted for the degrees of freedom is: a b c d A multiple regression model has the form: y8 = x 1 + 6x 2. As x 2 increases by one unit, holding x 1 constant, then the value of y will increase by: a. 2 units b units c. 6 units on average d. None of these choices 3

4 22. If all the points for a multiple regression model with two independent variables were right on the regression plane, then the coefficient of determination would equal: a. 0. b. 1. c. 2, since there are two independent variables. 23. For a multiple regression model, the total variation in y can be expressed as: a. SSR + SSE. b. SSR SSE. c. SSE SSR. d. SSR / SSE. 24. A multiple regression equation includes 5 independent variables, and the coefficient of determination is The percentage of the variation in y that is explained by the regression equation is: a. 81% b. 90% c. 86% d. about 16% 25. In a multiple regression model, the value of the coefficient of determination has to fall between a. 1 and +1. b. 0 and +1. c. 1 and 0. True/False Indicate whether the statement is true or false. 26. In a simple linear regression problem, the least squares line is y8 = x, and the coefficient of determination is The coefficient of correlation must be A zero correlation coefficient between a pair of random variables means that there is no linear relationship between the random variables. 28. In reference to the equation y8 = x x 2, the value 0.80 is the y-intercept. 29. In multiple regression, the standard error of estimate is defined by s ε = sample size and k is the number of independent variables. SSE / (n k), where n is the 30. One of the consequences of multicollinearity in multiple regression is biased estimates on the slope coefficients. 31. Multicollinearity is present when there is a high degree of correlation between the independent variables included in the regression model. 4

5 Short Answer Car Speed and Gas Mileage An economist wanted to analyze the relationship between the speed of a car (x) and its gas mileage (y). As an experiment a car is operated at several different speeds and for each speed the gas mileage is measured. These data are shown below. Speed Gas Mileage {Car Speed and Gas Mileage Narrative} Estimate the gas mileage of a car traveling 70 mph. 33. A scatter diagram includes the following data points: x y Two regression models are proposed: (1) y8 = x, and (2) y8 = 4.0x. Using the least squares method, which of these regression models provides the better fit to the data? Why? Sunshine and Skin Cancer A medical statistician wanted to examine the relationship between the amount of sunshine (x) in hours, and incidence of skin cancer (y). As an experiment he found the number of skin cancer cases detected per 100,000 of population and the average daily sunshine in eight counties around the country. These data are shown below. Average Daily Sunshine Skin Cancer per 100, {Sunshine and Skin Cancer Narrative} Draw a scatter diagram of the data and plot the least squares regression line on it. 35. {Sunshine and Skin Cancer Narrative} Estimate the number of skin cancer cases per 100,000 people who live in a state that gets 6 hours of sunshine on average. 36. {Sunshine and Skin Cancer Narrative} Can we conclude at the 1% significance level that there is a linear relationship between sunshine and skin cancer? 5

6 Game Winnings & Education An ardent fan of television game shows has observed that, in general, the more educated the contestant, the less money he or she wins. To test her belief she gathers data about the last eight winners of her favorite game show. She records their winnings in dollars and the number of years of education. The results are as follows. Contestant Years of Education Winnings {Game Winnings & Education Narrative} Draw a scatter diagram of the data. Comment on whether it appears that a linear model might be appropriate. 38. {Game Winnings & Education Narrative} Interpret the value of the slope of the regression line. 39. {Game Winnings & Education Narrative} Conduct a test of the population coefficient of correlation to determine at the 5% significance level whether a negative linear relationship exists between years of education and TV game shows' winnings. 6

7 Oil Quality and Price Quality of oil is measured in API gravity degrees--the higher the degrees API, the higher the quality. The table shown below is produced by an expert in the field who believes that there is a relationship between quality and price per barrel. Oil degrees API Price per barrel (in \$) A partial Minitab output follows: Descriptive Statistics Variable N Mean StDev SE Mean Degrees Price Covariances Degrees Price Degrees Price Regression Analysis Predictor Coef StDev T P Constant Degrees S = R-Sq = 92.46% R-Sq(adj) = 91.7% Analysis of Variance Source DF SS MS F P Regression Residual Error Total {Oil Quality and Price Narrative} Draw a scatter diagram of the data. Comment on whether it appears that a linear model might be appropriate to describe the relationship between the quality of oil and price per barrel. 7

8 41. The following 10 observations of variables x and y were collected. x y a. Calculate the standard error of estimate. b. Test to determine if there is enough evidence at the 5% significance level to indicate that x and y are negatively linearly related. c. Calculate the coefficient of correlation, and describe what this statistic tells you about the regression line. Sales and Experience The general manager of a chain of furniture stores believes that experience is the most important factor in determining the level of success of a salesperson. To examine this belief she records last month's sales (in \$1,000s) and the years of experience of 10 randomly selected salespeople. These data are listed below. Salesperson Years of Experience Sales (Sales and Experience Narrative} Determine the coefficient of determination and discuss what its value tells you about the two variables. 43. {Sales and Experience Narrative} Estimate with 95% confidence the average monthly sales of all salespersons with 10 years of experience. 44. {Sales and Experience Narrative} Compute the standardized residuals. 8

9 Life Expectancy An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death (y), the average number of hours of exercise per week (x 1 ), the cholesterol level (x 2 ), and the number of points that the individual's blood pressure exceeded the recommended value (x 3 ). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below. THE REGRESSION EQUATION IS y = x x x 3 Predictor Coef StDev T Constant x x x S = 9.47 R Sq = 22.5% ANALYSIS OF VARIANCE Source of Variation df SS MS F Regression Error Total {Life Expectancy Narrative} Is there sufficient evidence at the 5% significance level to infer that the number of points that the individual's blood pressure exceeded the recommended value and the age at death are negatively linearly related? 9

10 Regression Answer Section MULTIPLE CHOICE 1. ANS: B PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: D PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: C PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: C PTS: 1 REF: SECTION ANS: C PTS: 1 REF: SECTION ANS: C PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: D PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: C PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: A PTS: 1 REF: SECTION ANS: B PTS: 1 REF: SECTION TRUE/FALSE 26. ANS: F PTS: 1 REF: SECTION ANS: T PTS: 1 REF: SECTION ANS: T PTS: 1 REF: SECTION ANS: F PTS: 1 REF: SECTION ANS: F PTS: 1 REF: SECTION ANS: T PTS: 1 REF: SECTION

11 SHORT ANSWER 32. ANS: When x = 70, y8 = mpg. PTS: 1 REF: SECTION ANS: SSE = 4.95 and for models 1 and 2, respectively. Therefore, model (1) fits the data better than model (2). PTS: 1 REF: SECTION ANS: PTS: 1 REF: SECTION ANS: When x = 6, y8 = PTS: 1 REF: SECTION ANS: H 0 : ρ = 0 vs. H 1 : ρ 0 Rejection region: t > t 0.005,6 = Test statistic: t = Conclusion: Reject the null hypothesis. We conclude at the 1% significance level that there is a linear relationship between sunshine and skin cancer, according to this data. The relationship is positive, indicating that more sunshine is associated with more skin cancer cases. PTS: 1 REF: SECTION

12 37. ANS: It appears that a linear model is appropriate; further analysis is needed. PTS: 1 REF: SECTION ANS: For each additional year of education a contestant has, his or her winnings on TV game shows decreases by an average of approximately \$ PTS: 1 REF: SECTION ANS: H 0 : ρ = 0 vs. H 1 : ρ < 0 Rejection region: t < t 0.05,6 = Test statistic: t = Conclusion: Reject the null hypothesis. A negative linear relationship exists between years of education and TV game shows' winnings, according to this data. PTS: 1 REF: SECTION

13 40. ANS: A linear model might be appropriate to describe the relationship between the quality of oil and price per barrel. PTS: 1 REF: SECTION ANS: a. s ε = b. H 0 : β 1 = 0 vs. H 0 : β 1 < 0 Rejection region: t < t 0.05,8 = 1.86 Test statistic: t = Conclusion: Reject the null hypothesis. There is enough evidence at the 5% significance level to indicate that x and y have a negative linear relationship, according to this data. As speed increases, gas mileage decreases. c. r = This indicates a very strong negative linear relationship between the two variables. PTS: 1 REF: SECTION ANS: R 2 = , which means that 95.36% of the variation in sales is explained by the variation in years of experience of the salesperson. PTS: 1 REF: SECTION ANS: ± Thus LCL = (in \$1000s), and UCL = (in \$1000s). PTS: 1 REF: SECTION ANS: 1.100, 1.210, 0.373, 2.108, 0.483, 0.026, 1.086, 0.538, 0.178, and PTS: 1 REF: SECTION

14 45. ANS: H 0 : β 3 = 0 vs. H 1 : β 3 < 0 Rejection region: t < t 0.05, Test statistic: t = Conclusion: Don't reject the null hypothesis. No, sufficient evidence at the 5% significance level to infer that the number of points that the individual's blood pressure exceeded the recommended value and the age at death are negatively linearly related. PTS: 1 REF: SECTION

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