1. A measure of goodness of fit for the estimated regression equation is the   multiple coefficient of determination    
2. A multiple regression model has  more than one independent variable
3. A multiple regression model has the form y-hat = 7 + 2 x1 + 9 x2As x1 increase by 1 unit (holding x2 constant), y-hat is expected to   increase by 2 units
4. A procedure used for finding the equation of a straight line that provides the best approximation for the relationship between the independent and dependent variables is   the  least squares method
5. A variable that takes on the values of 0 or 1 and is used to incorporate the effect of qualitative variables in a regression model is called   a dummy variable
6. As the goodness of fit for the estimated multiple regression equation increases   the value of the multiple coefficient of determination increases
7. Exhibit 15-2. A regression model between sales (y in $1,000), unit price (x1 in dollars) and television advertisement (x2 in dollars) resulted in the following function y-hat = 7 – 3×1 + 5×2. For this model SSR = 3500, SSE = 1500, and the sample size is 18.Refer to Exhibit 15-2. The coefficient of the unit price indicates that if the unit price is  increased by $1 (holding advertising constant), sales are expected to decrease by $3,000
8. Exhibit 15-2. A regression model between sales (y in $1,000), unit price (x1 in dollars) and television advertisement (x2 in dollars) resulted in the following function y-hat = 7 – 3×1 + 5×2. For this model SSR = 3500, SSE = 1500, and the sample size is 18. Refer to Exhibit 15-2. The coefficient of x2 indicates that if television advertising is increased by $1 (holding the unit price constant), sales are expected to    increase by $5,000
9. Exhibit 15-2. A regression model between sales (y in $1,000), unit price (x1 in dollars) and television advertisement (x2 in dollars) resulted in the following function y-hat = 7 – 3×1 + 5×2. For this model SSR = 3500, SSE = 1500, and the sample size is 18. Refer to Exhibit 15-2. If we want to test for the significance of the regression model, the critical value of F using alpha = .05  3.68
10. Exhibit 15-2. A regression model between sales (y in $1,000), unit price (x1 in dollars) and television advertisement (x2 in dollars) resulted in the following function y-hat = 7 – 3×1 + 5×2. For this model SSR = 3500, SSE = 1500, and the sample size is 18. Refer to Exhibit 15-2, the test statistic F is  17.5
11. Exhibit 15-3. In a regression model involving 30 observations, the following estimated regression equation was obtained y-hat = 17 + 4×1 – 3×2 + 8×3 + 8×4. For this model SSR = 700 and SSE = 100. Refer to Exhibit 15-3. The computed F statistic for testing the significance of the above model is     43.75
12. For a multiple regression model, SSR = 600 and SSE = 200. The multiple coefficient of determination is   0.75
13. If a qualitative variable has k levels, the # of dummy variables required is   k – 1
14. If the coefficient of correlation is a positive value, then the slope of the regression line  must also be positive
15. If the coefficient of determination is a positive value, then the regression equation   could have either a positive or a negative slope
16. If the coefficient of determination is equal to 1, then the coefficient of correlation   can be either -1 or +1
17. In a multiple regression analysis involving 15 independent variables and 200 observations, SST = 800 and SSE = 240. The coefficient of determination is   0.700
18. In a multiple regression model, the error term ε is assumed to   be normally distributed
19. In a multiple regression model, the error term ε is assumed to be a random variable with a mean of   zero
20. In a multiple regression model, the variance of the error term ε is assumed to be  the same for all values of the independent variable
21. In a regression analysis if r2 = 1, then   SSR = SST
22. In a regression analysis if SSE = 200 and SSR = 300, then the coefficient of determination is 0.6000
23. In a regression analysis if SSE = 500 and SSR = 300, then the coefficient of determination is  0.3750
24. In a residual plot against x that does not suggest we should challenge the assumptions of our regression model, we would expect to see   a horizontal band of points centered near zero
25. In multiple regression analysis   there can be several independent variables, but only one dependent variable
26. In multiple regression analysis, the correlation among the independent variables is termed  multicollinearity
27. In order to test for the significance of a regression model involving 3 independent variables and 47 observations, the numerator and denominator degrees of freedom (respectively) for the critical value of F are   3 and 43
28. In order to test for the significance of a regression model involving 14 independent variables and 255 observations, the numerator and denominator degrees of freedom (respectively) for the critical value of F are   14 and 240
29. In regression analysis, an outlier is an observation whose   residual is much larger than the rest of the residual values
30. In regression analysis, the independent variable is    used to predict the dependent variable
31. In regression analysis, the response variable is the  dependent variable
32. In regression analysis, the variable that is being predicted is the   dependent variable
33. In simple linear regression, r2 is the  coefficient of determination
34. Larger values of r2 imply that the observations are more closely grouped about the  least squares line
35. Regression analysis was applied between sales (in $1,000) and advertising (in $100), and the following regression function was obtained. Y^ = 80 + 6.2x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is  $700,000
36. The adjusted multiple coefficient of determination is adjusted for   the number of independent variables
37. The correct relationship between SST, SSR, and SSE is given by   SSR = SST – SSE
38. The difference between the observed value of the dependent variable and the value predicted by using the estimated regression equation is the   residual
39. The multiple coefficient of determination is   SSR/SST
40. The numerical value of the coefficient of determination   can be larger or smaller than the coefficient of correlation
41. The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the   coefficient of determination
42. Which of the following is correct   SST = SSR + SSE

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