Ch16_Sect01-02_Keller MS_AISE TB

Ch16_Sect01-02_Keller MS_AISE TB - CHAPTER 16 SECTION 1-2:...

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Unformatted text preview: CHAPTER 16 SECTION 1-2: SIMPLE LINEAR REGRESSION AND CORRELATION MULTIPLE CHOICE 1. The regression line has been fitted to the data points (4, 8), (2, 5), and (1, 2). The sum of the squared residuals will be: a. 7 b. 15 c. 8 d. 22 ANS: D PTS: 1 REF: SECTION 16.1-16.2 2. If an estimated regression line has a y-intercept of 10 and a slope of 4, then when x = 2 the actual value of y is: a. 18 b. 15 c. 14 d. unknown. ANS: D PTS: 1 REF: SECTION 16.1-16.2 3. Given the least squares regression line : 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. d. None of these choices. ANS: B PTS: 1 REF: SECTION 16.1-16.2 4. A regression analysis between weight ( y in pounds) and height ( x in inches) resulted in the following least squares line: . This implies that if the height is increased by 1 inch, the weight, on average, is expected to: a. increase by 1 pound. b. decrease by 1 pound. c. increase by 5 pounds. d. increase by 24 pounds. ANS: C PTS: 1 REF: SECTION 16.1-16.2 5. A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line: . This implies that if advertising is $800, then the predicted amount of sales (in dollars) is: a. $4875 b. $123,000 c. $487,500 d. $12,300 ANS: B PTS: 1 REF: SECTION 16.1-16.2 This edition is intended for use outside of the U.S. only, with content that may be different from the U.S. Edition. This may not be resold, copied, or distributed without the prior consent of the publisher. 6. A regression analysis between sales (in $1,000) and advertising (in $1,000) resulted in the following least squares line: . 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 $80. d. None of these choices. ANS: A PTS: 1 REF: SECTION 16.1-16.2 7. 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 ANS: D PTS: 1 REF: SECTION 16.1-16.2 8. 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 ANS: A PTS: 1 REF: SECTION 16.1-16.2 9. In the simple linear regression model, the y-intercept represents the: a. change in y per unit change in x . b. change in x per unit change in y . c. value of y when x = 0. d. value of x when y = 0....
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This note was uploaded on 04/08/2012 for the course ADMS 2320 taught by Professor Rochon during the Fall '08 term at York University.

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Ch16_Sect01-02_Keller MS_AISE TB - CHAPTER 16 SECTION 1-2:...

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