6. 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 7. In the simple linear regression model, the slope represents the: a. value of y when x=0. b. average change in y per unit change in x. c. value of x when y=0. d. average change in x per unit change in y. 8. In regression analysis, the residuals represent 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 slope of the regression line. d. change in y per unit change in x. 9. The least squares method for determining the best fit minimizes: a. total variation in the dependent variable b. sum of squares for error c. sum of squares for regression d. All of these choices are true. 10. A regression analysis between sales (in $1000) and advertising (in $ 100 ) resulted in the following least squares line: y=75+6 x. 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

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6. In the simple linear regression model, the y-intercept represents the: a. change in y per...

6. 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

7. In the simple linear regression model, the slope represents the:

a. value of y when x=0.

b. average change in y per unit change in x.

c. value of x when y=0.

d. average change in x per unit change in y.

8. In regression analysis, the residuals represent 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 slope of the regression line.

d. change in y per unit change in x.

9. The least squares method for determining the best fit minimizes:

a. total variation in the dependent variable

b. sum of squares for error

c. sum of squares for regression

d. All of these choices are true.

10. A regression analysis between sales (in $1000) and advertising (in $ 100 ) resulted in the following least squares line: y=75+6 x. 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

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milcah answered 1 year ago

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