- In logistic regression,
| a. the dependent variable only assumes two continuous values. |
| b. there are two dependent variables. |
| c. the dependent variable only assumes two discrete values. |
| d. there can only be two independent variables. |
2. A regression model between sales (y in $1000), unit price (x1 in dollars), and television advertisement (x2 in dollars) resulted in the following function:
Ŷ = 7 – 3x1 + 5x2
For this model, SSR = 3500, SSE = 1500, and the sample size is 18. The adjusted multiple coefficient of determination for this problem is
| a. .70. |
| b. .66. |
| c. .2289. |
| d. .8367. |
3. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The multiple coefficient of determination is
| a. .68. |
| b. .42. |
| c. .32. |
| d. .50. |
4. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. At the 5% level, the model
| a. is not significant. |
| b. would be significant if the sample size was larger than 30. |
| c. is significant. |
| d. has significant individual parameters |
5. How many independent variables are there in the estimated regression model 
?
| a. 1 |
| b. 2 |
| c. 3 |
| d. 4 |
6. Which of the following represents the estimated value of the dependent variable(s) in the regression equation 
?
| a. b0 |
| b. x1, x2, and x3 |
| c. b1, b2, and b3 |
d.  |
7. A measure of identifying the effect of an unusual x value on the regression results is called
| a. Cook’s D. |
| b. odd ratio. |
| c. unusual regression. |
| d. Leverage. |
8. In a multiple regression model involving 30 observations, the following estimated regression equation was obtained:
Ŷ = 17 + 4x1 – 3x2 + 8x3 + 8x4
For this model, SSR = 700 and SSE = 100. The computed F statistic for testing the significance of the above model is
| a. 7.00. |
| b. 4.00. |
| c. 50.19. |
| d. 43.75. |
9. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source ofVariation | Degrees of Freedom | Sum ofSquares | MeanSquare | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
Carry out the test of significance for the parameter β1 at the 1% level. The null hypothesis should
| a. be revised to test using F statistic. |
| b. be tested for β₃ instead. |
| c. be rejected. |
| d. not be rejected. |
10. Which of the following does the graph of a multiple regression equation resemble?
| a. A nonlinear function in two-dimensional space |
| b. A two-dimensional line |
| c. A cylinder in three-dimensional space |
| d. A plane in three-dimensional space |
11. A logistic regression equation has what shape?
| a. Normal |
| b. Skewed to the left |
| c. S shape |
| d. Skewed to the right |
12. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The estimated income (in $) of a 30-year-old male is
| a. $51,000. |
| b. $21. |
| c. $51. |
| d. $90,000. |
13. What values are typically assigned to an indicator variable?
| a. there can be any number of dependent variables, but only one independent variable. |
| b. the multiple coefficient of determination must be larger than 1. |
| c. the adjusted coefficient of determination can never be negative. |
| d. there can be several independent variables, but only one dependent variable. |
14. Which of the following would indicate the possible presence of multicollinearity in a regression analysis?
| a. A significant p-value for a test of overall significance |
| b. The F value obtained from the table which is used to test if there is a relationship among the variables at the 5% level equals |
| c. A small value for the coefficient of determination |
| d. All of these choices |
15. The mathematical equation that explains how the dependent variable y is related to several independent variables x1, x2, …, xp and the error term ε is
| a. a multiple regression equation. |
| b. a multiple regression model. |
| c. a simple nonlinear regression model. |
| d. an estimated multiple regression equation. |
16. In a multiple regression analysis, SSR = 1000 and SSE = 200. The F statistic for this model is
| a. 800. |
| b. 5. |
| c. 1200. |
| d. Not enough information is provided to answer this question. |
17.In a multiple regression model involving 44 observations, the following estimated regression equation was obtained.
Ŷ = 29 + 18x1 + 43x2 + 87x3
For this model, SSR = 600 and SSE = 400. The computed F statistic for testing the significance of the above model is
| a. .667. |
| b. .600. |
| c. 1.50. |
| d. 20.00. |
18. In a multiple regression analysis involving 10 independent variables and 81 observations, SST = 120 and SSE = 42. The multiple coefficient of determination is
| a. .11. |
| b. .65. |
| c. .35. |
| d. .81 |
19. What is the predicted value of y derived from a logistic regression equation with β0 = 0.5, β1 = 0.25, β2 = 1.75, x1 = 5, and x2 = 1?
| a. 0.97 |
| b. 0.82 |
| c. 0.94 |
| d. 0.78 |
20. In a multiple regression model involving 30 observations, the following estimated regression equation was obtained:
Ŷ = 17 + 4x1 – 3x2 + 8x3 + 8x4
For this model, SSR = 700 and SSE = 100. At the 5% level,
| a. it can be concluded that the model is significant. |
| b. there is no evidence that the model is significant. |
| c. the conclusion is that the slope of x1 is significant. |
| d. there is evidence that the slope of x2 is significant. |
21. Which of the following would indicate the possible presence of multicollinearity in a regression analysis?
| a. A significant p-value for a test of overall significance |
| b. A high correlation between two or more independent variables |
| c. A small value for the coefficient of determination |
| d. All of these choices |
22. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The F value obtained from the table which is used to test if there is a relationship among the variables at the 5% level equals
| a. 19.41. |
| b. 3.63. |
| c. 3.41. |
| d. 3.81 |
23. In order to test for the significance of a regression model involving 4 independent variables and 36 observations, the numerator and denominator degrees of freedom (respectively) for the critical value of F are
| a. 4 and 32. |
| b. 4 and 31. |
| c. 4 and 36. |
| d. 3 and 35. |
24. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The test statistic for testing the significance of the model is
| a. 1.47. |
| b. 28.69. |
| c. .73. |
| d. 5.22. |
25. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. At the 5% level, the model
| a. has significant individual parameters. |
| b. is significant. |
| c. is not significant. |
| d. would be significant if the sample size was larger than 30. |
26. A regression model between sales (y in $1000), unit price (x1 in dollars), and television advertisement (x2 in dollars) resulted in the following function:
Ŷ = 7 – 3x1 + 5x2
For this model, SSR = 3500, SSE = 1500, and the sample size is 18. The coefficient of x2 indicates that if television advertisement is increased by $1 (holding the unit price constant), sales are expected to
| a. increase by $5000. |
| b. decrease by $2000. |
| c. increase by $12,000. |
| d. increase by $5. |
27. The adjusted multiple coefficient of determination is adjusted for the
| a. number of independent variables. |
| b. number of equations. |
| c. sample size. |
| d. number of dependent variables |
28. Which of the following does the graph of a multiple regression equation resemble?
| a. A nonlinear function in two-dimensional space |
| b. A plane in three-dimensional space |
| c. A cylinder in three-dimensional space |
| d. A two-dimensional line |
29. In a situation where the dependent variable can assume only one of the two possible discrete values,
| a. logistic regression should be applied. |
| b. there can only be two independent variables. |
| c. all the independent variables must have values of either zero or one. |
| d. we must use multiple regression. |
30. A regression analysis was performed to determine the relationship between the costs of a product (y), the time to make the product (x1 ), the number of different materials used (x2), and the amount spent on marketing the product (x3). The estimated regression equation is 
. What is the estimated cost if the time to make the product is 5 hours, the number of different materials used is 4, and the amount spent on marketing is $100?
| a. 205.4 |
| b. 185.4 |
| c. 213 |
| d. 83 |
31. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The yearly income (in $) expected of a 24-year-old male individual is
| a. $46,800. |
| b. $49,800. |
| c. $13,800. |
| d. $16,800. |
32. In a multiple regression model, the error term ε is assumed to be a random variable with a mean of
| a. zero. |
| b. 1. |
| c. any value. |
| d. -1. |
33. The equation which has the form of E(y) = Ŷ = b0 + b1x1 + b2x2 + … + bpxp is
| a. a multiple nonlinear regression model. |
| b. an estimated multiple regression equation. |
| c. a multiple regression equation. |
| d. an estimated multiple nonlinear regression equation. |
34. In multiple regression analysis, the correlation among the independent variables is termed
| a. adjusted coefficient of determination. |
| b. linearity. |
| c. adjusted correlation. |
| d. multicollinearity |
35. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The yearly income (in $) expected of a 24-year-old female individual is
| a. $19.80. |
| b. $49,800. |
| c. $19,800. |
| d. $49.80. |
36. What values are typically assigned to an indicator variable?
| a. 0 or 1 |
| b. It depends on the data collected. |
| c. –1 or 1 |
| d. 1 or 2 |
37. In regression analysis, the response variable is the
| a. intercept. |
| b. slope of the regression function. |
| c. independent variable. |
| d. dependent variable. |
38. A regression model in which more than one independent variable is used to predict the dependent variable is called
| a. a multiple regression model. |
| b. an adjusted prediction model. |
| c. a simple linear regression model. |
| d. an independent model. |
39. A variable that takes on the values of 0 or 1 and is used to incorporate the effect of categorical independent variables in a regression model is called
| a. an interaction. |
| b. a logit variable. |
| c. a constant variable. |
| d. a dummy variable. |
40. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The degrees of freedom for the sum of squares explained by the regression (SSR) are
| a. 3. |
| b. 15. |
| c. 13. |
| d. 2. |
41. For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of determination is
| a. .80. |
| b. .25. |
| c. .75. |
| d. .33. |
42. The correct relationship between SST, SSR, and SSE is given by
| a. n(SST) = p(SSR) + (n – p)(SSE). |
| b. SSR = SST + SSE. |
| c. SSE = SSR + SST. |
| d. SSR = SST – SSE. |
43. What type of model is the regression equation 
?
| a. It is a first-order model with three predictor variables. |
| b. It is a third-order model with three predictor variables. |
| c. It is a fourth-order model with one predictor variable. |
| d. It is a third-order model with one predictor variable. |
44. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
If you want to determine whether or not the coefficients of the independent variables are significant, the critical t value at α = .05 is
| a. 2.086. |
| b. 2.064. |
| c. 2.080. |
| d. 2.060. |
45. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
The p-value for testing the significance of the regression model is
| a. less than .01. |
| b. between .01 and .025. |
| c. greater than .10. |
| d. between .025 and .05. |
46. In multiple regression analysis, the general linear model
| a. must contain more than two independent variables. |
| b. can be used to accommodate curvilinear relationships between the independent variables and dependent variable. |
| c. cannot be used to accommodate curvilinear relationships between dependent variables and independent variables. |
| d. cannot use the standard multiple regression procedures for estimation and prediction. |
47. The following regression model
y = β0 + β1x1 + β2×12 + ε
is known as a
| a. second-order model with two predictor variables. |
| b. second-order model with one predictor variable. |
| c. simple first-order model with one predictor variable. |
| d. simple first-order model with two predictor variables. |
48. A test used to determine whether or not first-order autocorrelation is present is
| a. serial-autocorrelation test. |
| b. t test. |
| c. Durbin-Watson Test. |
| d. chi-square test. |
49. Which of the following values remains the same in the regression analysis for both the full and reduced models?
| a. SSR |
| b. SST |
| c. SSE |
| d. All of these choices |
50. Consider the sample correlation coefficients in the table below.
How much of the variability in time can be explained by boxes?
| a. .81% |
| b. 90.2% |
| c. 66.2% |
| d. 81.4% |
51. The range of the Durbin-Watson statistic is from
| a. -∞ to +∞. |
| b. 0 to 4. |
| c. 0 to 1. |
| d. -1 to 1. |
52. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
If we are interested in testing for the significance of the relationship among the variables (i.e., significance of the model), the critical value of F at α = .05 is
| a. 2.78. |
| b. 2.76. |
| c. 3.10. |
| d. 3.07. |
53. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. At the 5% level, the model
| a. is significant. |
| b. is not significant. |
| c. has significant individual parameters. |
| d. would be significant if the sample size was larger than 30. |
54. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. If we want to test for the significance of the model, the critical value of F at α = .05 is
| a. 2.96. |
| b. 3.33. |
| c. 3.35. |
| d. 3.34. |
55. In a multiple regression model involving 44 observations, the following estimated regression equation was obtained.
Ŷ = 29 + 18x1 + 43x2 + 87x3
For this model, SSR = 600 and SSE = 400. MSR for this model is
| a. 200. |
| b. 10. |
| c. 1000. |
| d. 43. |
56. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source ofVariation | Degrees of Freedom | Sum ofSquares | MeanSquare | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
Carry out the test of significance for the parameter β1 at the 1% level. The null hypothesis should
| a. not be rejected. |
| b. be rejected. |
| c. be revised to test using F statistic. |
| d. be tested for β₃ instead. |
57. The mathematical equation that explains how the dependent variable y is related to several independent variables x1, x2, …, xp and the error term ε is
| a. an estimated multiple regression equation. |
| b. a simple nonlinear regression model. |
| c. a multiple regression model. |
| d. a multiple regression equation. |
58. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The test statistic for testing the significance of the model is
| a. .73. |
| b. 1.47. |
| c. 5.22. |
| d. 28.69. |
59. A term used to describe the case when the independent variables in a multiple regression model are correlated is
| a. leverage. |
| b. regression. |
| c. multicollinearity. |
| d. correlation. |
60. What values are typically assigned to an indicator variable?
| a. 1 or 2 |
| b. –1 or 1 |
| c. It depends on the data collected. |
| d. 0 or 1 |
61. A regression model involving 4 independent variables and a sample of 15 observations resulted in the following sum of squares.
| SSR = 165 |
| SSE = 60 |
If we want to test for the significance of the model at a .05 level of significance, the critical F value (from the table) is
| a. 3.11. |
| b. 3.48. |
| c. 3.34. |
| d. 3.06. |
62. The equation which has the form of E(y) = Ŷ = b0 + b1x1 + b2x2 + … + bpxp is
| a. a multiple nonlinear regression model. |
| b. an estimated multiple nonlinear regression equation. |
| c. a multiple regression equation. |
| d. an estimated multiple regression equation. |
63. When autocorrelation is present, which of the following statements is true?
a. The error terms are related by the function  . |
| b. The error terms are independent. |
| c. The error terms are normally distributed with mean 0. |
| d. All of these choices. |
64. Consider the sample correlation coefficients in the table below.
How much of the variability in time can be explained by boxes?
| a. 90.2% |
| b. 81.4% |
| c. 66.2% |
| d. .81% |
65. All of the following variable selection procedures are heuristics except
| a. backward elimination. |
| b. stepwise regression. |
| c. forward selection. |
| d. best-subsets regression. |
66. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
The test statistic for testing the significance of the model is
| a. .73. |
| b. 3.70. |
| c. 1.37. |
| d. 18.93. |
67. The variable selection procedure that identifies the best regression model, given a specified number of independent variables, is
| a. forward selection. |
| b. backward elimination. |
| c. best-subsets regression. |
| d. stepwise regression |
68. The following regression model
y = β0 + β1x1 + β2×12 + ε
is known as a
| a. second-order model with one predictor variable. |
| b. simple first-order model with one predictor variable. |
| c. simple first-order model with two predictor variables. |
| d. second-order model with two predictor variables. |
69. When autocorrelation is present, one of the assumptions of the regression model is violated and that assumption is
| a. the values of the error term ε are independent. |
| b. the expected value of the error term ε is zero. |
| c. the values of the error term ε are normally distributed for all values of x. |
| d. the variance of the error term ε is the same for all values of x. |
70.
n a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
The multiple coefficient of determination is
| a. .73. |
| b. .33. |
| c. .27. |
| d. .50. |
71. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3= 7 |
| SST = 4800 | SSE = 1296 | |
At the 5% level, the coefficient of x2
| a. is significant. |
| b. cannot be tested, because not enough information is provided. |
| c. should be estimated again, because it is incorrect in the above equation. |
| d. is not found to be significant. |
72. In multiple regression analysis, the general linear model
| a. cannot be used to accommodate curvilinear relationships between dependent variables and independent variables. |
| b. must contain more than two independent variables. |
| c. cannot use the standard multiple regression procedures for estimation and prediction. |
| d. can be used to accommodate curvilinear relationships between the independent variables and dependent variable. |
73. A regression model between sales (y in $1000), unit price (x1 in dollars), and television advertisement (x2 in dollars) resulted in the following function:
Ŷ = 7 – 3x1 + 5x2
For this model, SSR = 3500, SSE = 1500, and the sample size is 18. The adjusted multiple coefficient of determination for this problem is
| a. .70. |
| b. .66. |
| c. .8367. |
| d. .2289. |
74. A regression model involved 18 independent variables and 200 observations. The critical value of t for testing the significance of each of the independent variable’s coefficients will have
| a. 200 degrees of freedom. |
| b. 181 degrees of freedom. |
| c. 199 degrees of freedom. |
| d. 18 degrees of freedom. |
75. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. If we want to test for the significance of the model, the critical value of F at α = .05 is
| a. 3.35. |
| b. 3.34. |
| c. 3.33. |
| d. 2.96. |
76. The _______ of an observation is determined by how far the values of the independent variables are from their means.
| a. collinearity |
| b. leverage |
| c. residual |
| d. odds ratio |
77. In multiple regression analysis, a variable that cannot be measured in numerical terms is called a
| a. categorical independent variable. |
| b. nonmeasurable random variable. |
| c. dependent variable. |
| d. constant variable. |
78. A regression model involving 4 independent variables and a sample of 15 observations resulted in the following sum of squares.
| SSR = 165 |
| SSE = 60 |
If we want to test for the significance of the model at a .05 level of significance, the critical F value (from the table) is
| a. 3.11. |
| b. 3.06. |
| c. 3.34. |
| d. 3.48. |
79. In a regression model involving more than one independent variable, which of the following tests must be used in order to determine if the relationship between the dependent variable and the set of independent variables is significant?
| a. F test |
| b. chi-square test |
| c. t test |
| d. Either a t test or a chi-square test can be used. |
80. In a multiple regression model involving 44 observations, the following estimated regression equation was obtained.
Ŷ = 29 + 18x1 + 43x2 + 87x3
For this model, SSR = 600 and SSE = 400. The computed F statistic for testing the significance of the above model is
| a. 20.00. |
| b. .600. |
| c. .667. |
| d. 1.50. |
81. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. The yearly income (in $) expected of a 24-year-old female individual is
| a. $49,800. |
| b. $49.80. |
| c. $19.80. |
| d. $19,800 |
82. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
Carry out the test to determine if there is a relationship among the variables at the 5% level. The null hypothesis should
| a. be rejected. |
| b. be revised to test for multicollinearity. |
| c. not be rejected. |
| d. test for individual significance instead. |
83. In multiple regression analysis, the word linear in the term “general linear model” refers to the fact that β0, β1, . . ., βp all have exponents of
| a. at least 1. |
| b. less than 0. |
| c. 0. |
| d. 1. |
84. The forward selection procedure starts with _____ independent variable(s) in the multiple regression model.
| a. no |
| b. two |
| c. all |
| d. one |
85. When performing a backward elimination procedure, how is the first variable deleted from the model selected?
| a. The variable with the largest coefficient for predicting y is selected first. |
| b. The variable with the smallest standard deviation is selected first. |
| c. The variable with the highest p-value for the test H0 : βi = 0 is selected first. |
| d. The variable with the lowest p-value when testing the significance of the coefficients is selected first. |
86. The test statistic for a Durbin–Watson test is d = 1.27 with n = 20 and k = 5. Which of the following conclusions can be made when testing H0: ρ = 0 versus Ha: ρ < 0 at the 1% significance level?
| a. There is evidence of negative autocorrelation. |
| b. There is no evidence of positive autocorrelation. |
| c. There is no evidence of multicollinearity. |
| d. There is no evidence of negative autocorrelation. |
87.
When dealing with the problem of nonconstant variance, the reciprocal transformation means using
| a. 1/x as the independent variable instead of x. |
| b. 1/y as the dependent variable instead of y. |
| c. y2 as the dependent variable instead of y. |
| d. x2 as the independent variable instead of x. |
88.
Which of the following is the multiple regression model that would be used to analyze data from a randomized block design with two treatments and four blocks?
a.  |
b.  |
c.  |
d.  |
89. Which procedure selects independent variables for inclusion in the model one at a time?
| a. Forward selection |
| b. Backward elimination |
| c. Logarithm-transformed regression |
| d. Nonlinear regression |
90. What is the predicted rate ( ) when temperature (x) is 60 for the regression equation 
?
| a. 695.68 |
| b. 1506.8 |
| c. 86.8 |
| d. Not enough information is given to compute the predicted rate |
91. Serial correlation is
| a. the correlation between serial numbers of the independent variables. |
| b. used to identify the effects of multicollinearity. |
| c. the same as autocorrelation. |
| d. the same as leverage. |
92. Multicollinearity may cause problems if the absolute value of the sample correlation coefficient for two of the independent variables exceeds what value?
| a. .7 |
| b. 1 |
| c. .5 |
| d. .05 |
93. If a categorical variable has k levels, the number of dummy variables required is
| a. k + 1. |
| b. k. |
| c. 2k. |
| d. k – 1. |
94. In order to test for the significance of a regression model involving 8 independent variables and 121 observations, the numerator and denominator degrees of freedom (respectively) for the critical value of F are
| a. 7 and 112. |
| b. 8 and 121. |
| c. 8 and 112. |
| d. 7 and 120. |
95. A regression analysis involved 17 independent variables and 697 observations. The critical value of t for testing the significance of each of the independent variable’s coefficients will have
| a. 696 degrees of freedom. |
| b. 679 degrees of freedom. |
| c. 714 degrees of freedom. |
| d. 16 degrees of freedom |
96. A regression model between sales (y in $1000), unit price (x1 in dollars), and television advertisement (x2 in dollars) resulted in the following function:
Ŷ = 7 – 3x1 + 5x2
For this model, SSR = 3500, SSE = 1500, and the sample size is 18. The multiple coefficient of determination for this problem is
| a. .7000. |
| b. .3040. |
| c. .2289. |
| d. .4368. |
97. In multiple regression analysis, a variable that cannot be measured in numerical terms is called a
| a. nonmeasurable random variable. |
| b. dependent variable. |
| c. constant variable. |
| d. categorical independent variable. |
98. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The interpretation of the coefficient of x1 is that
| a. a one unit change in x1 will lead to a 3.682 unit decrease in y. |
| b. The unit of measurement for y is required to interpret the coefficient. |
| c. a one unit increase in x1 will lead to a 3.682 unit decrease in y when all other variables are held constant. |
| d. a one unit increase in x1 will lead to a 3.682 unit decrease in x2 when all other variables are held constant. |
99. What is the predicted value of y derived from a logistic regression equation with β0 = 0.5, β1 = 0.25, β2 = 1.75, x1 = 5, and x2 = 1?
| a. 0.97 |
| b. 0.78 |
| c. 0.94 |
| d. 0.82 |
100. A measure of goodness of fit for the estimated regression equation is the
| a. multicollinearity. |
| b. multiple coefficient of determination. |
| c. mean square due to regression. |
| d. studentized residual. |
101. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. If we want to test for the significance of the model, the critical value of F at α = .05 is
| a. 3.35. |
| b. 3.33. |
| c. 2.96. |
| d. 3.34. |
102. Which of the following does the graph of a multiple regression equation resemble?
| a. A plane in three-dimensional space |
| b. A two-dimensional line |
| c. A nonlinear function in two-dimensional space |
| d. A cylinder in three-dimensional space |
103. A regression model involving 4 independent variables and a sample of 15 observations resulted in the following sum of squares.
| SSR = 165 |
| SSE = 60 |
The test statistic obtained from the information provided is
| a. 2.110. |
| b. 6.875. |
| c. 5.455. |
| d. 3.480. |
104. A multiple regression analysis was performed to determine the relationship between the satisfaction rating for a new candy bar and the amount of chocolate used and the amount of caramel used. What is the mean value of the satisfaction rating, if the estimated regression equation is 
, the amount of chocolate (x1 ) used is 0.35 ounce, and the amount of caramel (x2 ) used is 0.15 ounce?
| a. 4.235 |
| b. .50 |
| c. 6.73 |
| d. 8.47 |
105. How many independent variables are there in the estimated regression model ?
| a. 1 |
| b. 2 |
| c. 3 |
| d. 4 |
106. The following estimated regression equation was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).
Ŷ = 30 + .7x1 + 3x2
Also provided are SST = 1200 and SSE = 384. If we want to test for the significance of the model, the critical value of F at α = .05 is
| a. 2.96. |
| b. 3.34. |
| c. 3.35. |
| d. 3.33 |
107. The equation which has the form of E(y) = Ŷ = b0 + b1x1 + b2x2 + … + bpxp is
| a. a multiple nonlinear regression model. |
| b. an estimated multiple regression equation. |
| c. a multiple regression equation. |
| d. an estimated multiple nonlinear regression equation. |
108. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The interpretation of the coefficient of x1 is that
| a. a one unit increase in x1 will lead to a 3.682 unit decrease in y when all other variables are held constant. |
| b. a one unit increase in x1 will lead to a 3.682 unit decrease in x2 when all other variables are held constant. |
| c. The unit of measurement for y is required to interpret the coefficient. |
| d. a one unit change in x1 will lead to a 3.682 unit decrease in y. |
109. A logistic regression equation has what shape?
| a. Skewed to the right |
| b. Normal |
| c. S shape |
| d. Skewed to the left |
110. In a multiple regression model involving 30 observations, the following estimated regression equation was obtained:
Ŷ = 17 + 4x1 – 3x2 + 8x3 + 8x4
For this model, SSR = 700 and SSE = 100. The computed F statistic for testing the significance of the above model is
| a. 50.19. |
| b. 7.00. |
| c. 43.75. |
| d. 4.00 |
111. The mathematical equation that explains how the dependent variable y is related to several independent variables x1, x2, …, xp and the error term ε is
| a. a multiple regression model. |
| b. a multiple regression equation. |
| c. an estimated multiple regression equation. |
| d. a simple nonlinear regression model. |
112. If an independent variable is added to a multiple regression model, the R2 value
| a. is not affected by the variable added even if it is statistically significant. |
| b. becomes larger even if the variable added is not statistically significant. |
| c. becomes larger or smaller depending on the statistical significance of the variable. |
| d. might or might not become larger even if the variable added is statistically significant. |
113. An example of a first-order model with two predictor variables is
| a. y = b0 + b1x1 + ε. |
| b. y = b0 + b1x1 + b2x2 + ε. |
| c. y = b0 + b1x2 + ε. |
| d. y2 = b0 + b1x1 + b2x2 + ε |
114. In a stepwise regression, which of the following tests is used to determine whether an independent variable makes a significant contribution to the model?
| a. F test |
| b. Chi-square test |
| c. z test |
| d. β test |
115. serial correlation is
| a. the same as autocorrelation. |
| b. the same as leverage. |
| c. the correlation between serial numbers of the independent variables. |
| d. used to identify the effects of multicollinearity |
116. The correlation in error terms that arises when the error terms at successive points in time are related is termed
| a. leverage. |
| b. autocorrelation. |
| c. interaction. |
| d. multicorrelation. |
117. Which of the following statements about the backward elimination procedure is false?
| a. It begins with the regression model found using the forward selection procedure. |
| b. It does not permit an independent variable to be reentered once it has been removed. |
| c. It does not guarantee that the best regression model will be found. |
| d. It is a one-variable-at-a-time procedure. |
118. A variable such as z, whose value is z = x1x2, is added to a general linear model in order to account for potential effects of two variables x1 and x2 acting together. This type of effect is
| a. called interaction. |
| b. one of the transformation effects. |
| c. impossible to occur. |
| d. called multicollinearity effect. |
119. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3= 7 |
| SST = 4800 | SSE = 1296 | |
At the 5% level, the coefficient of x2
| a. is not found to be significant. |
| b. is significant. |
| c. cannot be tested, because not enough information is provided. |
| d. should be estimated again, because it is incorrect in the above equation. |
120. What type of model is the regression equation ?
| a. It is a first-order model with three predictor variables. |
| b. It is a fourth-order model with one predictor variable. |
| c. It is a third-order model with three predictor variables. |
| d. It is a third-order model with one predictor variable. |
121. In a stepwise regression, which of the following tests is used to determine whether an independent variable makes a significant contribution to the model?
| a. Chi-square test |
| b. β test |
| c. F test |
| d. z test |
122. In order to use the output from a multiple regression analysis to perform the ANOVA test on the difference among the means of three populations, how many dummy variables are needed to indicate treatments?
| a. 1 |
| b. 3 |
| c. 4 |
| d. 2 |
123. When dealing with the problem of nonconstant variance, the reciprocal transformation means using
| a. y2 as the dependent variable instead of y. |
| b. x2 as the independent variable instead of x. |
| c. 1/y as the dependent variable instead of y. |
| d. 1/x as the independent variable instead of x. |
124. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
If we are interested in testing for the significance of the relationship among the variables (i.e., significance of the model), the critical value of F at α = .05 is
| a. 2.76. |
| b. 3.07. |
| c. 3.10. |
| d. 2.78. |
125. When autocorrelation is present, one of the assumptions of the regression model is violated and that assumption is
| a. .5 |
| b. .05 |
| c. .7 |
| d. 1 |
126. Which of the following tests is used to determine whether an additional variable makes a significant contribution to a multiple regression model?
| a. an F test |
| b. a chi-square test |
| c. a t test |
| d. a z test |
127. When autocorrelation is present, one of the assumptions of the regression model is violated and that assumption is
| a. the variance of the error term ε is the same for all values of x. |
| b. the expected value of the error term ε is zero. |
| c. the values of the error term ε are independent. |
| d. the values of the error term ε are normally distributed for all values of x. |
128. The correlation in error terms that arises when the error terms at successive points in time are related is termed
| a. autocorrelation. |
| b. leverage. |
| c. interaction. |
| d. multicorrelation |
129. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
At the 5% level, the coefficient of x1
| a. is significant. |
| b. is not found to be significant. |
| c. should be estimated again, because it is incorrect in the above equation. |
| d. cannot be tested, because not enough information is provided. |
130. When autocorrelation is present, which of the following statements is true?
| a. The error terms are related by the function . |
| b. The error terms are independent. |
| c. The error terms are normally distributed with mean 0. |
| d. All of these choices |
131. All the independent variables in a multiple regression analysis
| a. can be either quantitative or qualitative or both. |
| b. must assume only positive values. |
| c. must be quantitative. |
| d. must be either quantitative or qualitative but not a mix of both. |
132. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
The test statistic for testing the significance of the model is
| a. 3.70. |
| b. 1.37. |
| c. 18.93. |
| d. .73. |
133. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
If you want to determine whether or not the coefficients of the independent variables are significant, the critical t value at α = .05 is
| a. 2.060. |
| b. 2.064. |
| c. 2.080. |
| d. 2.086. |
134. In a stepwise regression, which of the following tests is used to determine whether an independent variable makes a significant contribution to the model?
| a. Chi-square test |
| b. F test |
| c. β test |
| d. z test |
135. Serial correlation is
| a. the same as autocorrelation. |
| b. the same as leverage. |
| c. the correlation between serial numbers of the independent variables. |
| d. used to identify the effects of multicollinearity. |
136. In order to use the output from a multiple regression analysis to perform the ANOVA test on the difference among the means of three populations, how many dummy variables are needed to indicate treatments?
| a. 2 |
| b. 3 |
| c. 1 |
| d. 4 |
137. The forward selection procedure starts with _____ independent variable(s) in the multiple regression model.
| a. no |
| b. two |
| c. one |
| d. all |
138. Which of the following models is not intrinsically linear?
a.  |
b.  |
c.  |
| d. All of these models are intrinsically linear. |
139. A regression model involved 5 independent variables and 136 observations. The critical value of t for testing the significance of each of the independent variable’s coefficients will have
| a. 135 degrees of freedom. |
| b. 130 degrees of freedom. |
| c. 121 degrees of freedom. |
| d. 4 degrees of freedom |
140. What is the predicted value of y derived from a logistic regression equation with β0 = 0.5, β1 = 0.25, β2 = 1.75, x1 = 5, and x2 = 1?
| a. 0.82 |
| b. 0.94 |
| c. 0.97 |
| d. 0.78 |
141. A multiple regression model has the estimated form
Ŷ = 7 + 2x1 + 9x2
As x1 increases by 1 unit (holding x2 constant), y is expected to
| a. decrease by 2 units. |
| b. increase by 9 units. |
| c. decrease by 9 units. |
| d. increase by 2 units |
142. In a multiple regression model involving 30 observations, the following estimated regression equation was obtained:
Ŷ = 17 + 4x1 – 3x2 + 8x3 + 8x4
For this model, SSR = 700 and SSE = 100. The multiple coefficient of determination for the above model is
| a. .125. |
| b. .934. |
| c. .144. |
| d. .875 |
143. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The sum of squares due to error (SSE) equals
| a. 4853. |
| b. 373.31. |
| c. 485.3. |
| d. 6308.9. |
144. Using the Durbin-Watson test for negative autocorrelation, we conclude that negative autocorrelation is present if
| a. d < dU. |
| b. d < dL. |
| c. d > 4 – dL. |
| d. d < 4 – dU. |
145. What is the predicted profit when the number of items made (x1 ) is 2,500, and the number of stores stocking the product (x2 ) is 5, using the estimated regression equation 
?
| a. 1,345 |
| b. 23,200 |
| c. 8,095 |
| d. 75,595 |
146. A regression model involving 4 independent variables and a sample of 15 observations resulted in the following sum of squares.
| SSR = 165 |
| SSE = 60 |
The multiple coefficient of determination is
| a. .275. |
| b. .7333. |
| c. .3636. |
| d. .5. |
147. The null hypothesis in the Durbin-Watson test is always that there is
| a. negative autocorrelation. |
| b. no autocorrelation. |
| c. either positive or negative autocorrelation. |
| d. positive autocorrelation |
148. In a multiple regression analysis involving 5 independent variables and 30 observations, SSR = 360 and SSE = 40. The multiple coefficient of determination is
| a. .90. |
| b. .25. |
| c. .15. |
| d. .80 |
149. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
We want to test whether the parameter β1 is significant. The test statistic equals
| a. -1.4. |
| b. -5.0. |
| c. 3.6. |
| d. 1.4. |
150. The following model
y = β0 + β1x1 + ε
is referred to as a
| a. curvilinear model. |
| b. simple first-order model with one predictor variable. |
| c. simple second-order model with one predictor variable. |
| d. curvilinear model with one predictor variable. |
151. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
The test statistic for testing the significance of the model is
| a. 1.37. |
| b. 3.70. |
| c. 18.93. |
| d. .73. |
152. Which procedure selects independent variables for inclusion in the model one at a time?
| a. Logarithm-transformed regression |
| b. Backward elimination |
| c. Forward selection |
| d. Nonlinear regression |
153. The joint effect of two independent variables acting together is called
| a. transformation. |
| b. joint regression. |
| c. autocorrelation. |
| d. interaction. |
154. A regression model between sales (y in $1000), unit price (x1 in dollars), and television advertisement (x2 in dollars) resulted in the following function:
Ŷ = 7 – 3x1 + 5x2
For this model, SSR = 3500, SSE = 1500, and the sample size is 18. To test for the significance of the model, the test statistic F is
| a. 17.5. |
| b. .70. |
| c. 1.75. |
| d. 2.33. |
155. In a multiple regression model involving 30 observations, the following estimated regression equation was obtained:
Ŷ = 17 + 4x1 – 3x2 + 8x3 + 8x4
For this model, SSR = 700 and SSE = 100. The critical F value at α = .05 is
| a. 2.99. |
| b. 2.53. |
| c. 2.76. |
| d. 2.69 |
156. The numerical value of the coefficient of determination.
| a. is negative if the coefficient of correlation is negative. |
| b. is always smaller than the coefficient of correlation. |
| c. can be larger or smaller than the coefficient of correlation. |
| d. is always larger than the coefficient of correlation |
157. A regression analysis involved 6 independent variables and 27 observations. The critical value of t for testing the significance of each of the independent variable’s coefficients will have
| a. 27 degrees of freedom. |
| b. 21 degrees of freedom. |
| c. 26 degrees of freedom. |
| d. 20 degrees of freedom |
158. In a regression analysis of a first-order model involving 3 predictor variables and 25 observations, the following estimated regression equation was developed.
Ŷ = 10 – 18x1 + 3x2 + 14x3
Also, the following standard errors and the sum of squares were obtained.
| sb1 = 3 | sb2 = 6 | sb3 = 7 |
| SST = 4800 | SSE = 1296 | |
At the .05 level of significance, the coefficient of x3
| a. should be estimated again, because it is incorrect in the above equation. |
| b. cannot be tested, because not enough information is provided. |
| c. is significant. |
| d. is not found to be significant. |
159. A model in the form of y = β0 + β1z1 + β2z2 + . . . + βpzp + ε, where each independent variable zj (for j = 1, 2, . . ., p) is a function of x1, x2 , …, xk, is known as the
| a. pth-order z model. |
| b. general linear model. |
| c. general curvilinear model. |
| d. experimental model |
160. Models in which the parameters have exponents other than 1 are called
| a. independent models. |
| b. autocorrelated models. |
| c. linear models. |
| d. nonlinear models. |
161. Below you are given a partial computer output from a multiple regression analysis based on a sample of 16 observations.
| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 | 45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation | Degrees of Freedom | Sum of Squares | Mean Square | F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The t value obtained from the table which is used to test an individual parameter at the 1% level is
| a. 2.977. |
| b. 2.650. |
| c. 3.012. |
| d. 2.921. |
162. In a multiple regression analysis involving 5 independent variables and 30 observations, SSR = 360 and SSE = 40. The multiple coefficient of determination is
| a. .90. |
| b. .15. |
| c. .80. |
| d. .25. |
163. 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
| a. 3 and 43. |
| b. 47 and 3. |
| c. 2 and 43. |
| d. 3 and 47. |
164. In a multiple regression analysis involving 15 independent variables and 200 observations, SST = 800 and SSE = 240. The multiple coefficient of determination is
| a. .192. |
| b. .300. |
| c. .500. |
| d. .700. |
165. In a multiple regression model, the variance of the error term ε is assumed to be
| a. zero. |
| b. one. |
| c. the same for all values of the independent variable. |
| d. the same for all values of the dependent variable |
166. What is the multiple coefficient of determination for a multiple regression analysis involving 4 independent variables and 240 observations when SST = 870 and SSE = 325?
| a. .728 |
| b. .626 |
| c. .374 |
| d. .620 |
167. A regression analysis involved 8 independent variables and 99 observations. The critical value of t for testing the significance of each of the independent variable’s coefficients will have
| a. 90 degrees of freedom. |
| b. 97 degrees of freedom. |
| c. 98 degrees of freedom. |
| d. 7 degrees of freedom |
168. Consider the residual plot from the multiple regression analysis to determine the time required to load a truck given the number of boxes to be loaded and the average weight of the boxes.
How many data points in the residual plot given should be investigated further as potential outliers?
| a. 0 |
| b. 1 |
| c. 2 |
| d. 3 |
169. The ratio of MSR to MSE yields
| a. the F statistic. |
| b. the chi-square statistic. |
| c. SST. |
| d. SSR |
170. Consider the following hypothesis problem.
| n = 30 | H0: σ2 = 500 |
| s2 = 625 | Ha: σ2 ≠ 500 |
The test statistic equals
| a. 24.00. |
| b. 36.25. |
| c. 23.20. |
| d. 37.50 |
171. The contents of a sample of 26 cans of apple juice showed a standard deviation of .06 ounces. We are interested in testing whether the variance of the population is significantly more than .003. The test statistic is
| a. 30. |
| b. 1.2. |
| c. 500. |
| d. 31.2 |
172. The contents of a sample of 26 cans of apple juice showed a standard deviation of .06 ounces. We are interested in testing whether the variance of the population is significantly more than .003. The p-value for this test is
| a. less than .10. |
| b. zero. |
| c. greater than .10. |
| d. .05. |
173. Based on the sample evidence below, we want to test the hypothesis that population A has a larger variance than population B.
| Sample A | Sample B | |
| n | 11 | 10 |
| s2 | 12.1 | 5 |
The test statistic for this problem equals
| a. 2. |
| b. 2.42. |
| c. 1.1. |
| d. .4132 |
174. The bottler of a certain soft drink claims their equipment to be accurate and that the variance of all filled bottles is .05 or less. The null hypothesis in a test to confirm the claim would be written as
| a. H0: σ2 > .05. |
| b. H0: σ2 ≥ .05. |
| c. H0: σ2 < .05. |
| d. H0: σ2 ≤ .05. |
175. Consider the following hypothesis problem.
| n = 23 | s2 = 60 | H0: σ2 ≤ 66 |
| Ha: σ2 > 66 |
If the test is to be performed at the .05 level of significance, the null hypothesis
| a. should not be tested. |
| b. should be rejected. |
| c. should be revised. |
| d. should not be rejected. |
176. Consider the scenario where
The null hypothesis is to be tested at the 5% level of significance. The critical value(s) from the table is(are) _____.
| a. 5.629 |
| b. 5.629 and 26.119 |
| c. 6.262 and 27.488 |
| d. 27.488 |
177. A sample of 61 observations yielded a sample standard deviation of 6. If we want to test H0: σ2 = 40, the test statistic is
| a. 9. |
| b. 54.90. |
| c. 54. |
| d. 9.15. |
178. Given the sample information below, what test statistic should be used to determine whether the standard deviations are equal for two populations, prior to performing a hypothesis test for the equality of the population means?
| a. F = 2.49, with numerator degrees of freedom = 29 and denominator degrees of freedom = 19 |
| b. F = 0.40, with degrees of freedom = 19 |
| c. t = 18.75, with degrees of freedom = 19 |
| d. F = 1.58, with numerator degrees of freedom = 19 and denominator degrees of freedom = 29 |
179. Consider the following sample information from Population A and Population B.
| Sample A | Sample B | |
| n | 24 | 16 |
| s2 | 32 | 38 |
We want to test the hypothesis that the population variances are equal. The null hypothesis is to be tested at the 10% level of significance. The critical value from the F distribution table is
| a. 2.24. |
| b. 2.11. |
| c. 2.29. |
| d. 2.13. |
180.
The random variable for a chi-square distribution may assume
| a. any value between -1 to 1. |
| b. any value greater than zero. |
| c. any value between -∞ to +∞. |
| d. any negative value. |
181. Consider the following hypothesis problem.
| n = 14 | H0: σ2 ≤ 410 |
| s = 20 | Ha: σ2 > 410 |
The test statistic equals
| a. .63. |
| b. 13.33. |
| c. 13.68. |
| d. 12.68. |
182. The χ2 value for a one-tailed (upper tail) hypothesis test at 95% confidence and a sample size of 20 is _____.
| a. 10.851 |
| b. 10.117 |
| c. 30.144 |
| d. 31.410 |
183. The contents of a sample of 26 cans of apple juice showed a standard deviation of .06 ounces. We are interested in testing whether the variance of the population is significantly more than .003. The p-value for this test is
| a. zero. |
| b. .05. |
| c. less than .10. |
| d. greater than .10. |
184. The sampling distribution used when making inferences about a single population’s variance is
| a. a t distribution. |
| b. an F distribution. |
| c. a chi-square distribution. |
| d. a normal distribution |
185. The sampling distribution of the ratio of independent sample variances extracted from two normal populations with equal variances is the
| a. chi-square distribution. |
| b. normal distribution. |
| c. t distribution. |
| d. F distribution |
186. Consider the following hypothesis problem.
| n = 23 | s2 = 60 | H0: σ2 ≤ 66 |
| Ha: σ2 > 66 |
If the test is to be performed at the .05 level of significance, the null hypothesis
| a. should not be tested. |
| b. should be rejected. |
| c. should not be rejected. |
| d. should be revised. |
187. We are interested in testing whether the variance of a population is significantly more than 625. The null hypothesis for this test is
| a. H0: σ2 > 625. |
| b. H0: σ2 ≤ 625. |
| c. H0: σ2 ≥ 625. |
| d. H0: σ2 ≤ 25. |
188. A sample of 20 bottles of soda yielded a standard deviation of .25 ounce. A 95% confidence interval estimate of the variance for the population is _____.
| a. .0361 to .1333 |
| b. –.6378 to .6378 |
| c. .0394 to .1174 |
| d. .0023 to .0083 |
189. To avoid the problem of not having access to tables of the F distribution when a one-tailed test is required and with F values given for the lower tail, let the
| a. sample variance from the population with the smaller hypothesized variance be the numerator of the test statistic. |
| b. smaller sample variance be the numerator of the test statistic. |
| c. larger sample variance be the numerator of the test statistic. |
| d. sample variance from the population with the larger hypothesized variance be the numerator of the test statistic. |
190. The value of F.01 with 9 numerator and 20 denominator degrees of freedom is
| a. 2.39. |
| b. 2.91. |
| c. 2.94. |
| d. 3.46. |
191. The chi-square value for a one-tailed (upper tail) hypothesis test at a 5% level of significance and a sample size of 25 is
| a. 36.415. |
| b. 37.652. |
| c. 33.196. |
| d. 39.364. |
192. Which of the following rejection rules is proper?
| a. Reject H0 if p-value ≥ α/2. |
| b. Reject H0 if F ≥ Fα. |
| c. Reject H0 if F ≤ Fα/2. |
| d. Reject H0 if p-value ≤ Fα. |
193. The manager of the service department of a local car dealership has noted that the service times of a sample of 15 new automobiles has a standard deviation of 4 minutes. A 95% confidence interval estimate for the variance of service times for all their new automobiles is
| a. 8.576 to 39.794. |
| b. 9.46 to 34.09. |
| c. 2.144 to 9.948. |
| d. 2.93 to 6.31. |
194. χ2.975 = 8.231 indicates that
| a. 97.5% of the chi-square values are less than 8.231. |
| b. 5% of the chi-square values are equal to 8.231. |
| c. 2.5% of the chi-square values are greater than 8.231. |
| d. 97.5% of the chi-square values are greater than 8.231. |
195. Consider the following hypothesis problem.
| n = 30 | H0: σ2 = 500 |
| s2 = 625 | Ha: σ2 ≠ 500 |
The test statistic equals
| a. 36.25. |
| b. 37.50. |
| c. 23.20. |
| d. 24.00. |
196. Consider the following hypothesis problem.
| n = 30 | H0: σ2 = 500 |
| s2 = 625 | Ha: σ2 ≠ 500 |
At the 5% level of significance, the null hypothesis
| a. should be revised. |
| b. should not be tested. |
| c. should be rejected. |
| d. should not be rejected. |
197. The value of F.05 with 8 numerator and 19 denominator degrees of freedom is
| a. 2.48. |
| b. 3.63. |
| c. 2.96. |
| d. 2.58. |
198. The symbol used for the variance of the population is
| a. σ2. |
| b. s2. |
| c. s. |
| d. σ. |
199. The sampling distribution of the quantity (n – 1)s2/σ2 is the
| a. normal distribution. |
| b. chi-square distribution. |
| c. F distribution. |
| d. t distribution |
200. Which of the following is not a property of a chi-square distribution?
| a. χ2 is skewed to the right. |
| b. The number of degrees of freedom defines the shape of the distribution of χ2. |
| c. χ2 can have both positive and negative values. |
| d. All of these choices are properties of the χ2 distribution |
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