ch14

4. In regression analysis, the model in the form is called a. regression equation b. correlation equation c. estimated regression equation d. regression model

D

42. If the coefficient of correlation is a negative value, then the coefficient of determination a. must also be negative b. must be zero c. can be either negative or positive d. must be positive

D

1. In a regression analysis, the error term 3 is a random variable with a mean or expected value of a. zero b. one c. any positive value d. any value

A

10. The interval estimate of an *individual value* of y for a given value of x is a. prediction interval estimate b. confidence interval estimate c. average regression d. x versus y correlation interval

A

15. In regression analysis, which of the following is not a required assumption about the error term ? a. The expected value of the error term is one. b. The variance of the error term is the same for all values of X. c. The values of the error term are independent. d. The error term is normally distributed.

A

40. SSE can never be a. larger than SST b. smaller than SST c. equal to 1 d. equal to zero

A

Exhibit 14 - 1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 65. Refer to Exhibit 14-1. The least squares estimate of the slope is a. 1 b. 2 c. 3 d. 4

A

Exhibit 14 - 1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 67. Refer to Exhibit 14-1. The coefficient of correlation is a. 0.7906 b. - 0.7906 c. 0.625 d. 0.375

A

25. In simple linear regression analysis, which of the following is not true? a. The F test and the t test yield the same conclusion. b. The F test and the t test may or may not yield the same conclusion. c. The relationship between X and Y is represented by means of a straight line. d. The value of F = t2.

B

9. The interval estimate of the mean value of y for a given value of x is a. prediction interval estimate b. confidence interval estimate c. average regression d. x versus y correlation interval

B

You are given the following information about y and x. y Dependent Variable x Independent Variable 5 4 7 6 9 2 11 4 93. Refer to Exhibit 14-7. The least squares estimate of b0 (intercept) equals a. -10 b. 10 c. 0.5 d. -0.5

B

12. If only MSE is known, you can compute the a. r square b. coefficient of determination c. standard error d. all of these alternatives are correct

C

22. Larger values of r2 imply that the observations are more closely grouped about the a. average value of the independent variables b. average value of the dependent variable c. least squares line d. Origin

C

6. The model developed from sample data that has the form of is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model

C

8. In regression analysis, the unbiased estimate of the variance is a. coefficient of correlation b. coefficient of determination c. mean square error d. slope of the regression equation

C

You are given the following information about y and x. y Dependent Variable x Independent Variable 5 4 7 6 9 2 11 4 95. Refer to Exhibit 14-7. The coefficient of determination equals a. 0.3162 b. -0.3162 c. 0.10 d. -0.10

C

11. The standard error is the a. t-statistic squared b. square root of SSE c. square root of SST d. square root of MSE

D

16. A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation = 30,000 + 4 X The above equation implies that an a. increase of $4 in advertising is associated with an increase of $4,000 in sales b. increase of $1 in advertising is associated with an increase of $4 in sales c. increase of $1 in advertising is associated with an increase of $34,000 in sales d. increase of $1 in advertising is associated with an increase of $4,000 in sales

D

18. In a simple regression analysis (where Y is a dependent and X an independent variable), if the Y intercept is positive, then a. there is a positive correlation between X and Y b. if X is increased, Y must also increase c. if Y is increased, X must also increase d. None of these alternatives is correct.

D

28. In a regression and correlation analysis if r2 = 1, then a. SSE = SST b. SSE = 1 c. SSR = SSE d. SSR = SST

D

33. Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained. = 120 - 10 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to a. increase by 120 units b. increase by 100 units c. increase by 20 units d. decease by 20 units

D

36. If the coefficient of correlation is 0.8, the percentage of variation in the dependent variable explained by the variation in the independent variable is a. 0.80% b. 80% c. 0.64% d. 64%

D

39. If there is a very weak correlation between two variables, then the coefficient of determination must be a. much larger than 1, if the correlation is positive b. much smaller than -1, if the correlation is negative c. much larger than one d. None of these alternatives is correct.

D

48. If a data set has SSR = 400 and SSE = 100, then the coefficient of determination is a. 0.10 b. 0.25 c. 0.40 d. 0.80

D

51. In a regression analysis if SST = 500 and SSE = 300, then the coefficient of determination is a. 0.20 b. 1.67 c. 0.60 d. 0.40

D

63. Regression analysis was applied between sales (Y in $1,000) and advertising (X in $100), and the following estimated regression equation was obtained. = 80 + 6.2 X Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is a. $62,080 b. $142,000 c. $700 d. $700,000

D

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 101. Refer to Exhibit 14-8. The coefficient of correlation is a. -0.2295 b. 0.2295 c. 0.0527 d. -0.0572

A

38. In regression analysis, if the independent variable is measured in pounds, the dependent variable a. must also be in pounds b. must be in some unit of weight c. cannot be in pounds d. can be any units

D

29. In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of correlation is a. -0.7 b. +0.7 c. 0.49 d. -0.49

A

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 96. Refer to Exhibit 14-8. The total sum of squares (SST) is a. -156 b. 234 c. 1870 d. 1974

D

13. The value of the coefficient of correlation (R) a. can be equal to the value of the coefficient of determination (R2) b. can never be equal to the value of the coefficient of determination (R2) c. is always smaller than the value of the coefficient of determination d. is always larger than the value of the coefficient of determination

A

19. In regression analysis, the variable that is being predicted is the a. dependent variable b. independent variable c. intervening variable d. is usually x

A

2. The coefficient of determination a. cannot be negative b. is the square root of the coefficient of correlation c. is the same as the coefficient of correlation d. can be negative or positive

A

23. In a regression analysis, the coefficient of determination is 0.4225. The coefficient of correlation in this situation is a. 0.65 b. 0.1785 c. any positive value d. any value

A

35. If the coefficient of correlation is a positive value, then the regression equation a. must have a positive slope b. must have a negative slope c. could have either a positive or a negative slope d. must have a positive y intercept

A

37. In regression and correlation analysis, if SSE and SST are known, then with this information the a. coefficient of determination can be computed b. slope of the line can be computed c. Y intercept can be computed d. x intercept can be computed

A

44. If two variables, x and y, have a strong linear relationship, then a. there may or may not be any causal relationship between x and y b. x causes y to happen c. y causes x to happen d. None of these alternatives is correct.

A

46. A least squares regression line a. may be used to predict a value of y if the corresponding x value is given b. implies a cause-effect relationship between x and y c. can only be determined if a good linear relationship exists between x and y d. None of these alternatives is correct.

A

49. Compared to the confidence interval estimate for a particular value of y (in a linear regression model), the interval estimate for an average value of y will be a. narrower b. wider c. the same d. None of these alternatives is correct.

A

5. The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = 0 + 1x, is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model

A

7. In the following estimated regression equation a. b1 is the slope b. b1 is the intercept c. b0 is the slope d. None of these alternatives is correct.

A

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 107. Refer to Exhibit 14-10. The slope of the regression function is a. -1 b. 1.0 c. 11 d. 0.0

A

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 109. Refer to Exhibit 14-10. The coefficient of determination is a. 0.1905 b. -0.1905 c. 0.4364 d. -0.4364

A

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 111. Refer to Exhibit 14-10. The MSE is a. 17 b. 8 c. 34 d. 42

A

Exhibit 14-3 You are given the following information about y and x. y Dependent Variable x Independent Variable 12 4 3 6 7 2 6 4 76. Refer to Exhibit 14-3. The sample correlation coefficient equals a. -0.4364 b. 0.4364 c. -0.1905 d. 0.1905

A

Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 78. Refer to Exhibit 14-4. Based on the above estimated regression equation, if advertising is $3,000, then the point estimate for sales (in dollars) is a. $66,000 b. $5,412 c. $66 d. $17,400

A

Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 1 1 2 2 3 3 4 4 5 5 84. Refer to Exhibit 14-5. The least squares estimate of the slope is a. 1 b. -1 c. 0 d. 3

A

Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 1 1 2 2 3 3 4 4 5 5 87. Refer to Exhibit 14-5. The MSE is a. 0 b. -1 c. 1 d. 0.5

A

Exhibit 14-6 For the following data the value of SSE = 0.4130. y Dependent Variable x Independent Variable 15 4 17 6 23 2 17 4 90. Refer to Exhibit 14-6. The total sum of squares (SST) equals a. 36 b. 18 c. 9 d. 1296

A

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 99. Refer to Exhibit 14-8. The slope of the regression equation is a. -0.667 b. 0.667 c. 100 d. -100

A

Exhibit 14-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). ΣX = 90 Σ(y-ybar)(x-xbar) = 466 ΣY = 170 Σ(x-xbar)^2 = 234 n = 10 Σ(y-ybar)^2 = 1434 SSE = 505.98 105. Refer to Exhibit 14-9. The sample correlation coefficient equals a. 0.8045 b. -0.8045 c. 0 d. 1

A

Exhibit 14-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). ΣX = 90 Σ(y-ybar)(x-xbar) = 466 ΣY = 170 Σ(x-xbar)^2 = 234 n = 10 Σ(y-ybar)^2 = 1434 SSE = 505.98 106. Refer to Exhibit 14-9. The coefficient of determination equals a. 0.6472 b. -0.6472 c. 0 d. 1

A

14. In a regression analysis the standard error is determined to be 4. In this situation the MSE a. is 2 b. is 16 c. depends on the sample size d. depends on the degrees of freedom

B

20. The equation that describes how the dependent variable (y) is related to the independent variable (x) is called a. the correlation model b. the regression model c. correlation analysis d. None of these alternatives is correct.

B

21. In regression analysis, the independent variable is a. used to predict other independent variables b. used to predict the dependent variable c. called the intervening variable d. the variable that is being predicted

B

24. In a regression analysis, the coefficient of correlation is 0.16. The coefficient of determination in this situation is a. 0.4000 b. 0.0256 c. 4 d. 2.56

B

26. Correlation analysis is used to determine a. the equation of the regression line b. the strength of the relationship between the dependent and the independent variables c. a specific value of the dependent variable for a given value of the independent variable d. None of these alternatives is correct.

B

27. In a regression and correlation analysis if r2 = 1, then a. SSE must also be equal to one b. SSE must be equal to zero c. SSE can be any positive value d. SSE must be negative

B

30. In a regression analysis if SSE = 200 and SSR = 300, then the coefficient of determination is a. 0.6667 b. 0.6000 c. 0.4000 d. 1.5000

B

31. If the coefficient of determination is equal to 1, then the coefficient of correlation a. must also be equal to 1 b. can be either -1 or +1 c. can be any value between -1 to +1 d. must be -1

B

34. The coefficient of correlation a. is the square of the coefficient of determination b. is the square root of the coefficient of determination c. is the same as r-square d. can never be negative

B

43. It is possible for the coefficient of determination to be a. larger than 1 b. less than one c. less than -1 d. None of these alternatives is correct.

B

45. If the coefficient of determination is 0.81, the coefficient of correlation a. is 0.6561 b. could be either + 0.9 or - 0.9 c. must be positive d. must be negative

B

47. If all the points of a scatter diagram lie on the least squares regression line, then the coefficient of determination for these variables based on these data is a. 0 b. 1 c. either 1 or -1, depending upon whether the relationship is positive or negative d. could be any value between -1 and 1

B

52. Regression analysis was applied between sales (in $1000) and advertising (in $100) and the following regression function was obtained. = 500 + 4 X Based on the above estimated regression line if advertising is $10,000, then the point estimate for sales (in dollars) is a. $900 b. $900,000 c. $40,500 d. $505,000

B

53. The coefficient of correlation a. is the square of the coefficient of determination b. is the square root of the coefficient of determination c. is the same as r-square d. can never be negative

B

54. If the coefficient of correlation is 0.4, the percentage of variation in the dependent variable explained by the variation in the independent variable a. is 40% b. is 16%. c. is 4% d. can be any positive value

B

59. In a regression analysis if SST = 4500 and SSE = 1575, then the coefficient of determination is a. 0.35 b. 0.65 c. 2.85 d. 0.45

B

62. If the coefficient of determination is 0.9, the percentage of variation in the dependent variable explained by the variation in the independent variable a. is 0.90% b. is 90%. c. is 81% d. 0.81%

B

Exhibit 14 - 1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 68. Refer to Exhibit 14-1. The MSE is a. 1 b. 2 c. 3 d. 4

B

Exhibit 14 - 1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 64. Refer to Exhibit 14-1. The least squares estimate of the Y intercept is a. 1 b. 2 c. 3 d. 4

B

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 113. Refer to Exhibit 14-10. The point estimate of Y when X = -3 is a. 11 b. 14 c. 8 d. 0

B

Exhibit 14-2 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 1 4 2 3 3 2 4 1 5 69. Refer to Exhibit 14-2. The least squares estimate of b1 (slope) equals a. 1 b. -1 c. 6 d. 5

B

Exhibit 14-3 You are given the following information about y and x. y Dependent Variable x Independent Variable 12 4 3 6 7 2 6 4 74. Refer to Exhibit 14-3. The least squares estimate of b1 equals a. 1 b. -1 c. -11 d. 11

B

Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 79. Refer to Exhibit 14-4. The F statistic computed from the above data is a. 3 b. 45 c. 48 d. 50

B

Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 82. Refer to Exhibit 14-4. The critical t value for testing the significance of the slope at 95% confidence is a. 1.753 b. 2.131 c. 1.746 d. 2.120

B

Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 1 1 2 2 3 3 4 4 5 5 83. Refer to Exhibit 14-5. The least squares estimate of the Y intercept is a. 1 b. 0 c. -1 d. 3

B

Exhibit 14-6 For the following data the value of SSE = 0.4130. y Dependent Variable x Independent Variable 15 4 17 6 23 2 17 4 89. Refer to Exhibit 14-6. The y intercept is a. -1.5 b. 24 c. 0.50 d. -0.707

B

Exhibit 14-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). ΣX = 90 Σ(y-ybar)(x-xbar) = 466 ΣY = 170 Σ(x-xbar)^2 = 234 n = 10 Σ(y-ybar)^2 = 1434 SSE = 505.98 102. Refer to Exhibit 14-9. The least squares estimate of b1 equals a. 0.923 b. 1.991 c. -1.991 d. -0.923

B

You are given the following information about y and x. y Dependent Variable x Independent Variable 5 4 7 6 9 2 11 4 94. Refer to Exhibit 14-7. The sample correlation coefficient equals a. 0.3162 b. -0.3162 c. 0.10 d. -0.10

B

17. Regression analysis is a statistical procedure for developing a mathematical equation that describes how a. one independent and one or more dependent variables are related b. several independent and several dependent variables are related c. one dependent and one or more independent variables are related d. None of these alternatives is correct.

C

3. If the coefficient of determination is a positive value, then the coefficient of correlation a. must also be positive b. must be zero c. can be either negative or positive d. must be larger than 1

C

32. In a regression analysis, the variable that is being predicted a. must have the same units as the variable doing the predicting b. is the independent variable c. is the dependent variable d. usually is denoted by x

C

41. If the coefficient of correlation is -0.4, then the slope of the regression line a. must also be -0.4 b. can be either negative or positive c. must be negative d. must be 0.16

C

55. In regression analysis if the dependent variable is measured in dollars, the independent variable a. must also be in dollars b. must be in some units of currency c. can be any units d. cannot be in dollars

C

Exhibit 14 - 1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 4 2 3 1 4 4 6 3 8 5 SSE = 6 SST = 16 66. Refer to Exhibit 14-1. The coefficient of determination is a. 0.7096 b. - 0.7906 c. 0.625 d. 0.375

C

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 108. Refer to Exhibit 14-10. The Y intercept is a. -1 b. 1.0 c. 11 d. 0.0

C

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 112. Refer to Exhibit 14-10. The point estimate of Y when X = 3 is a. 11 b. 14 c. 8 d. 0

C

Exhibit 14-2 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 1 4 2 3 3 2 4 1 5 70. Refer to Exhibit 14-2. The least squares estimate of b0 (intercept)equals a. 1 b. -1 c. 6 d. 5

C

Exhibit 14-2 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 1 4 2 3 3 2 4 1 5 71. Refer to Exhibit 14-2. The point estimate of y when x = 10 is a. -10 b. 10 c. -4 d. 4

C

Exhibit 14-2 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 1 4 2 3 3 2 4 1 5 72. Refer to Exhibit 14-2. The sample correlation coefficient equals a. 0 b. +1 c. -1 d. -0.5

C

Exhibit 14-2 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 1 4 2 3 3 2 4 1 5 73. Refer to Exhibit 14-2. The coefficient of determination equals a. 0 b. -1 c. +1 d. -0.5

C

Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 81. Refer to Exhibit 14-4. The t statistic for testing the significance of the slope is a. 1.80 b. 1.96 c. 6.708 d. 0.555

C

Exhibit 14-6 For the following data the value of SSE = 0.4130. y Dependent Variable x Independent Variable 15 4 17 6 23 2 17 4 91. Refer to Exhibit 14-6. The coefficient of determination (r2) equals a. 0.7071 b. -0.7071 c. 0.5 d. -0.5

C

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 100. Refer to Exhibit 14-8. The Y intercept is a. -0.667 b. 0.667 c. 100 d. -100

C

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 97. Refer to Exhibit 14-8. The sum of squares due to error (SSE) is a. -156 b. 234 c. 1870 d. 1974

C

50. A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation = 60 - 8X The above equation implies that an a. increase of $1 in price is associated with a decrease of $8 in sales b. increase of $8 in price is associated with an decrease of $52,000 in sales c. increase of $1 in price is associated with a decrease of $52 in sales d. increase of $1 in price is associated with a decrease of $8000 in sales

D

56. If there is a very strong correlation between two variables then the coefficient of determination must be a. much larger than 1, if the correlation is positive b. much smaller than -1, if the correlation is negative c. any value larger than 1 d. None of these alternatives is correct.

D

57. If the coefficient of correlation is 0.90, then the coefficient of determination a. is also 0.9 b. is either 0.81 or -0.81 c. can be either negative or positive d. must be 0.81

D

58. A regression analysis between demand (Y in 1000 units) and price (X in dollars) resulted in the following equation = 9 - 3X The above equation implies that if the price is increased by $1, the demand is expected to a. increase by 6 units b. decrease by 3 units c. decrease by 6,000 units d. decrease by 3,000 units

D

60. Regression analysis was applied between sales (in $10,000) and advertising (in $100) and the following regression function was obtained. = 50 + 8 X Based on the above estimated regression line if advertising is $1,000, then the point estimate for sales (in dollars) is a. $8,050 b. $130 c. $130,000 d. $1,300,000

D

61. If the coefficient of correlation is a positive value, then a. the intercept must also be positive b. the coefficient of determination can be either negative or positive, depending on the value of the slope c. the regression equation could have either a positive or a negative slope d. the slope of the line must be positive

D

Exhibit 14-10 The following information regarding a dependent variable Y and an independent variable X is provided. ΣX = 16 Σ(y-ybar)(x-xbar) = -8 ΣY = 28 Σ(y-ybar)^2 = 8 n = 4 SST = 42 SSE = 34 110. Refer to Exhibit 14-10. The coefficient of correlation is a. 0.1905 b. -0.1905 c. 0.4364 d. -0.4364

D

Exhibit 14-3 You are given the following information about y and x. y Dependent Variable x Independent Variable 12 4 3 6 7 2 6 4 75. Refer to Exhibit 14-3. The least squares estimate of b0 equals a. 1 b. -1 c. -11 d. 11

D

Exhibit 14-3 You are given the following information about y and x. y Dependent Variable x Independent Variable 12 4 3 6 7 2 6 4 77. Refer to Exhibit 14-3. The coefficient of determination equals a. -0.4364 b. 0.4364 c. -0.1905 d. 0.1905

D

Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 1 1 2 2 3 3 4 4 5 5 85. Refer to Exhibit 14-5. The coefficient of correlation is a. 0 b. -1 c. 0.5 d. 1

D

Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Y X 1 1 2 2 3 3 4 4 5 5 86. Refer to Exhibit 14-5. The coefficient of determination is a. 0 b. -1 c. 0.5 d. 1

D

Exhibit 14-6 For the following data the value of SSE = 0.4130. y Dependent Variable x Independent Variable 15 4 17 6 23 2 17 4 88. Refer to Exhibit 14-6. The slope of the regression equation is a. 18 b. 24 c. 0.707 d. -1.5

D

Exhibit 14-7 You are given the following information about y and x. y Dependent Variable x Independent Variable 5 4 7 6 9 2 11 4 92. Refer to Exhibit 14-7. The least squares estimate of b1 (slope) equals a. -10 b. 10 c. 0.5 d. -0.5

D

Exhibit 14-8 The following information regarding a dependent variable Y and an independent variable X is provided ΣX = 90 Σ(y-ybar)(x-xbar) = -156 ΣY = 340 Σ(x-xbar)^2 = 234 n = 4 Σ(y-ybar)^2 = 1974 SSR = 104 98. Refer to Exhibit 14-8. The mean square error (MSE) is a. 1870 b. 13 c. 1974 d. 935

D

Exhibit 14-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). ΣX = 90 Σ(y-ybar)(x-xbar) = 466 ΣY = 170 Σ(x-xbar)^2 = 234 n = 10 Σ(y-ybar)^2 = 1434 SSE = 505.98 103. Refer to Exhibit 14-9. The least squares estimate of b0 equals a. 0.923 b. 1.991 c. -1.991 d. -0.923

D

Exhibit 14-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). ΣX = 90 Σ(y-ybar)(x-xbar) = 466 ΣY = 170 Σ(x-xbar)^2 = 234 n = 10 Σ(y-ybar)^2 = 1434 SSE = 505.98 104. Refer to Exhibit 14-9. The sum of squares due to regression (SSR) is a. 1434 b. 505.98 c. 50.598 d. 928.02

D

XXXXXX Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 80. Refer to Exhibit 14-4. To perform an F test, the p-value is a. less than .01 b. between .01 and .025 c. between .025 and .05 d. between .05 and 0.1

D