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Ch16_Sect01-02_Keller MS_AISE TB

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

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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: 3. Given the least squares regression line : 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: 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.

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6. A regression analysis between sales (in \$1,000) and advertising (in \$1,000) resulted in the following least squares line: . This implies that: 7. Which of the following techniques is used to predict the value of one variable on the basis of other variables? 8. The residual is defined as the difference between: 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. ANS: C PTS: 1 REF: SECTION 16.1-16.2
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Ch16_Sect01-02_Keller MS_AISE TB - CHAPTER 16 SECTION 1-2...

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