File: Ch12, Chapter 12: Simple Regression Analysis and Correlation

True/False

1. Correlation is a measure of the degree of a linear relationship between two variables.

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

2. If the correlation coefficient between two variables is -1, it means that the two variables are not related.

Response: See section 12.1 Correlation

Difficulty: Medium

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

3. The strength of a linear relationship in a simple linear regression change if the units of the data are converted, say from feet to inches.

Response: See section 12.1 Correlation

Difficulty: Hard

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables

4. The process of constructing a mathematical model or function that can be used to predict or determine one variable by another variable is called regression analysis.

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

5. In regression analysis, the variable that is being predicted is usually referred to as the independent variable.

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

6. In regression analysis, the predictor variable is called the dependent variable.

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

7. The first step in simple regression analysis is usually to construct a scatter plot.

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

8. The slope of the regression line, ŷ = 21 − 5x, is 5.

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

9. The slope of the regression line, ŷ = 21 − 5x, is 21.

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

10. For the regression line, ŷ = 21 − 5x, 21 is the y-intercept of the line.

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

11. The difference between the actual y value and the predicted ŷ value found using a regression equation is called the residual.

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

12. Data points that lie apart from the rest of the points are called deviants.

Response: See section 12.4 Residual Analysis

Difficulty: Medium

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

13. One of the assumptions of simple regression analysis is that the error terms are exponentially distributed

Response: See section 12.4 Residual Analysis

Difficulty: Medium

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

14. In simple regression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance.

Response: See section 12.4 Residual Analysis

Difficulty: Medium

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

15. One of the major uses of residual analysis is to test some of the assumptions that are underlying the regression.

Response: See section 12.4 Residual Analysis

Difficulty: Medium

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

16. The standard error of the estimate, denoted s_e, is the square root of the sum of the squares of the vertical distances between the actual y values and the predicted values of ŷ.

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Medium

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

17. The proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x) is called the coefficient of correlation.

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

18. The coefficient of determination is the proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x).

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

19. In a simple regression the coefficient of correlation is the square root of the coefficient of determination.

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

20. In the simple regression model, ŷ = 21 − 5x, if the coefficient of determination is 0.81, we can say that the coefficient of correlation between y and x is 0.90.

Response: See section 12.6 Coefficient of Determination

Difficulty: Hard

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

21. The range of admissible values for the coefficient of determination is −1 to +1.

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

22. A t-test is used to determine whether the coefficients of the regression model are significantly different from zero.

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Medium

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

23. To determine whether the overall regression model is significant, an F-test is used.

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Medium

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

24. An F-value to test the overall significance of a regression model is computed by dividing the sum of squares regression (SS_reg) by the sum of squares error (SS_err).

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Medium

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

25. The variability in the estimated slope is smaller when the x-values are more spread out.

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Hard

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

26. Given x, a 95% prediction interval for a single value of y is always wider than a 95% confidence interval for the average value of y.

Response: See section 12.8 Estimation

Difficulty: Medium

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

27. Prediction intervals get narrower as we extrapolate outside the range of the data.

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

28. A confidence interval based on a specific value of x will reflect the range for the average value of the dependent variable.

Ans: True

Response: See section 12.8 Estimation

Difficulty: Medium

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

29. A prediction interval based on a specific value of x will reflect an estimate of the dependent variable for one person or thing from the population.

Ans: True

Response: See section 12.8 Estimation

Difficulty: Medium

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

30. Regression methods can be pursued to estimate trends that are linear in time.

Response: See section 12.9, Using regression to develop a forecasting trend line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of the trend line to forecast outcomes for time periods in the future, using alternate coding for time periods if necessary.

31. Regression output from Minitab software directly displays the regression equation.

Response: See section 12.10, Interpreting the output

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

32. Regression output from Excel software directly shows the regression equation.

Response: See section 12.10, Interpreting the output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

33. Regression output from Minitab software includes an ANOVA table.

Response: See section 12.10, Interpreting the output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

34. Regression output from Excel software includes an ANOVA table.

Response: See section 12.10, Interpreting the output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

Multiple Choice

35. According to the following graphic, X and Y have _________.

a) strong negative correlation

b) virtually no correlation

c) strong positive correlation

d) moderate negative correlation

e) weak negative correlation

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

36. According to the following graphic, X and Y have _________.

a) strong negative correlation

b) virtually no correlation

c) strong positive correlation

d) moderate negative correlation

e) weak negative correlation

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

37. From the following scatter plot, we can say that between y and x there is _______.

a) perfect positive correlation

b) virtually no correlation

c) positive correlation

d) negative correlation

e) perfect negative correlation

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

38. From the following scatter plot, we can say that between y and x there is _______.

a) perfect positive correlation

b) virtually no correlation

c) positive correlation

d) negative correlation

e) perfect negative correlation

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

39. From the following scatter plot, we can say that between y and x there is _______.

a) perfect positive correlation

b) virtually no correlation

c) positive correlation

d) negative correlation

e) perfect negative correlation

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

40. The numerical value of the coefficient of correlation must be _______.

a) between -1 and +1

b) between -1 and 0

c) between 0 and 1

d) equal to SSE/(n-2)

e) between 0 and -1

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

41. If there is perfect negative correlation between two sets of numbers, then _______.

a) r = 0

b) r = -1

c) r = +1

d) SSE=1

e) MSE = 1

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

42. If there is positive correlation between two sets of numbers, then _______.

a) r = 0

b) r < 0

c) r > 0

d) SSE=1

e) MSE = 1

Response: See section 12.1 Correlation

Difficulty: Easy

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

43. A quality manager is developing a regression model to predict the total number of defects as a function of the day of the week that the item is produced. Production runs are done 10 hours a day, 7 days a week. The explanatory variable is ______.

a) day of week

b) production run

c) percentage of defects

d) number of defects

e) number of production runs

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

44. A quality manager is developing a regression model to predict the total number of defects as a function of the day of the week that the item is produced. Production runs are done 10 hours a day, 7 days a week. The dependent variable is ______.

a) day of week

b) production run

c) percentage of defects

d) number of defects

e) number of production runs

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

45. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the ______.

a) batch size

b) unit variable cost

c) fixed cost

d) total cost

e) total variable cost

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Medium

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

46. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The slope of the accountant’s model is ______.

a) batch size

b) unit variable cost

c) fixed cost

d) total cost

e) total variable cost

Response: See section 12.2 Introduction to Simple Regression Analysis

Difficulty: Easy

Learning Objective: 12.2: Explain what regression analysis is and the concepts of independent and dependent variable.

47. In the regression equation, ŷ = 49.56 + 0.97x, the slope is _______.

a) 0.97

b) 49.56

c) 1.00

d) 0.00

e) -0.97

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line

48. In the regression equation, ŷ = 54.78 + 1.45x, the intercept is _______.

a) 1.45

b) -1.45

c) 54.78

d) -54.78

e) 0.00

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

49. For a certain data set the regression equation is ŷ = 29 - 5x. The correlation coefficient between y and x in this data set _______.

a) must be 0

b) is negative

c) must be 1

d) is positive

e) must be >1

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line

50. For a certain data set the regression equation is ŷ = 37 + 13x. The correlation coefficient between y and x in this data set _______.

a) must be 0

b) is negative

c) must be 1

d) is positive

e) must be 3

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line

51. The coefficient of correlation in a simple regression analysis is = - 0.6. The coefficient of determination for this regression would be _______.

a) 0.6

b) - 0.6 or + 0.6

c) 0.13

d) - 0.36

e) 0.36

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line

52. The following data is to be used to construct a regression model:

The value of the intercept is ________.

a) 16.49

b) 1.19

c) 1.43

d) 0.75

e) 1.30

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

. The following data is to be used to construct a regression model:

The value of the slope is ____________.

a) 16.49

b) 1.19

c) 1.43

d) 0.75

e) 1.30

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Medium

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

54. The following data is to be used to construct a regression model:

The regression equation is _______________.

a) ŷ = 16.49 + 1.43x

b) ŷ = 1.19 + 0.91x

c) ŷ = 1.19 + 0.75x

d) ŷ = 0.75 + 0.18x

e) ŷ = 0.91 + 4.06x

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Hard

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

55. Consider the following scatter plot and regression line. At x = 50, the residual (error term) is _______.

a) positive

b) zero

c) negative

d) imaginary

e) unknown

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

56. For the following scatter plot and regression line, at x = 34 the residual is _______.

a) positive

b) zero

c) negative

d) imaginary

e) unknown

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

57. One of the assumptions made in simple regression is that ______________.

a) the error terms are normally distributed

b) the error terms have unequal variances

c) the model is nonlinear

d) the error terms are dependent

e) the error terms are all equal

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

58. One of the assumptions made in simple regression is that ______________.

a) the error terms are exponentially distributed

b) the error terms have unequal variances

c) the model is linear

d) the error terms are dependent

e) the model is nonlinear

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

59. The assumptions underlying simple regression analysis include ______________.

a) the error terms are exponentially distributed

b) the error terms have unequal variances

c) the model is nonlinear

d) the error terms are dependent

e) the error terms are independent

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

60. The assumption of constant error variance in regression analysis is called _______.

a) heteroscedasticity

b) homoscedasticity

c) residuals

d) linearity

e) nonnormality

Response: See section 12.4 Residual Analysis

Difficulty: Medium

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

61. The following residuals plot indicates _______________.

a) a nonlinear relation

b) a nonconstant error variance

c) the simple regression assumptions are met

d) the sample is biased

e) the sample is random

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

62. The following residuals plot indicates _______________.

a) a nonlinear relation

b) a nonconstant error variance

c) the simple regression assumptions are met

d) the sample is biased

e) a random sample

Response: See section 12.4 Residual Analysis

Difficulty: Easy

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

63. The total of the squared residuals is called the _______.

a) coefficient of determination

b) sum of squares of error

c) standard error of the estimate

d) R-squared

e) coefficient of correlation

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Easy

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

64. A standard deviation of the error of the regression model is called the _______.

a) coefficient of determination

b) sum of squares of error

c) standard error of the estimate

d) R-squared

e) coefficient of correlation

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Easy

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

65. A simple regression model developed for 12 pairs of data resulted in a sum of squares of error, SSE = 246. The standard error of the estimate is _______.

a) 24.6

b) 4.96

c) 20.5

d) 4.53

e) 12.3

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Medium

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

66. A simple regression model developed for ten pairs of data resulted in a sum of squares of error, SSE = 125. The standard error of the estimate is _______.

a) 12.5

b) 3.5

c) 15.6

d) 3.95

e) 25

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Medium

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

67. In regression analysis, R-squared is also called the _______.

a) residual

b) coefficient of determination

c) coefficient of correlation

d) standard error of the estimate

e) sum of squares of regression

Response: See section 12.6 Coefficient of Determination

Difficulty: Easy

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

68. The numerical value of the coefficient of determination must be _______.

a) between -1 and +1

b) between -1 and 0

c) between 0 and 1

d) equal to SSE/(n-2)

e) between -100 and +100

Response: See section 12.6 Coefficient of Determination

Difficulty: Easy

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

69. The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the _______.

a) sum of squares error

b) coefficient of correlation

c) coefficient of determination

d) covariance

e) regression sum of squares

Response: See section 12.6 Coefficient of Determination

Difficulty: Easy

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

70. If x and y in a regression model are totally unrelated, _______.

a) the correlation coefficient would be -1

b) the coefficient of determination would be 0

c) the coefficient of determination would be 1

d) the SSE would be 0

e) the MSE would be 0s

Response: See section 12.6 Coefficient of Determination

Difficulty: Easy

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

71. In a regression analysis if SST = 200 and SSR = 200, r ² = _________.

a) 0.25

b) 0.75

c) 0.00

d) 1.00

e) -1.00

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

72. If the coefficient of determination was 0.49, then the correlation coefficient would be _______.

a) 0.7

b) -0.7

c) 0.49

d) 0.7 or -0.7

e) -0.49

Ans: d

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

73. In a regression analysis if SST = 150 and SSR = 100, r ² = _________.

a) 0.82

b) 1.22

c) 1.50

d) 0.67

e) -1.00

Response: See section 12.6 Coefficient of Determination

Difficulty: Medium

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

74. A researcher has developed a regression model from fourteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two‑ tailed test and  = 0.01. The critical table t value is _______.

a) 2.650

b) 3.012

c) 3.055

d) 2.718

e) 2.168

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Easy

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

75. A researcher has developed a regression model from fifteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two‑tailed test and  = 0.10. The critical table t value is _______.

a) 1.771

b) 1.350

c) 1.761

d) 2.145

e) 2.068

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Easy

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

76. In the regression equation, ŷ = 2.164 + 1.3657x, and n = 6, the mean of x is 8.667, SS_xx= 89.333 and S_e= 3.44. A 95% confidence interval for the average of y when x=8 is _________

a) (9.13, 17.05)

b) (2.75, 23.43)

c) (10.31, 15.86)

d) (3.56, 22.62)

e) (12.09, 14.09)

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

77. In the regression equation, ŷ=2.164+1.3657x and n=6, the mean of x is 8.667, SS_xx=89.333 and S_e=3.44. A 95% prediction interval for y when x=8 is _________

a) (9.13, 17.05)

b) (2.75, 23.43)

c) (10.31, 15.86)

d) (3.56, 22.62)

e) (12.09, 14.09)

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

78. In the regression equation, ŷ=5.23+2.74x and n=24, the mean of x is 12.56, SS_xx=55.87 and S_e=10.71. A 90% prediction interval for y when x=11 is _________

a) (2.74, 5.23)

b) (35.37, 70.74)

c) (16.21, 54.53)

d) (12.56, 55.87)

e) (30.00, 40.74)

Ans: c

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

79. In the regression equation, ŷ=5.23+2.74x and n=24, the mean of x is 12.56, SS_xx=55.87 and S_e=10.71. A 90% confidence interval for y when x=11 is _________

a) (2.74, 5.23)

b) (35.37, 70.74)

c) (16.21, 54.53)

d) (12.56, 55.87)

e) (30.00, 40.74)

Ans: e

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

80, When determining interval estimates for specific x₀, the closer x₀ is to the _______, the more narrow ___________ become(s).

a) standard deviation, the confidence interval

b) mean of y; both the prediction and confidence intervals

c) mean of x; both the prediction and confidence intervals

d) standard deviation, mean of y

e) mean of x; mean of y

Ans: c

Response: See section 12.8 Estimation

Difficulty: Hard

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.

81. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 15,000 miles, the predicted cost is ____________.

a) 2,090

b) 17,000

c) 8,400

d) 8,300

e) 6,300

Response: See section 12.9 Using Regression to Develop a Forecasting Trend Line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

82. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 30,000 miles, the predicted cost is _____________.

a) 10,400

b) 14,600

c) 2,000

d) 32,000

e) 10,250

Response: See section 12.9 Using Regression to Develop a Forecasting Trend Line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

83. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed: ŷ = 2,000 + 0.42x. If a car is driven 20,000 miles, the predicted cost is ____________.

a) 10,400

b) 20,000

c) 2,840

d) 6,200

e) 6,750

Response: See section 12.9 Using Regression to Develop a Forecasting Trend Line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

84. A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: ŷ = $400 + 120x. If a trip took 4 days, the predicted cost of the trip is _____________.

a) 480

b) 880

c) 524

d) 2080

e) 1080

Response: See section 12.9 Using Regression to Develop a Forecasting Trend Line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

85. A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: ŷ = $400 + 120x. If a trip took 3 days, the predicted cost of the trip is _____________.

a) 760

b) 360

c) 523

d) 1560

e) 1080

Response: See section 12.9 Using Regression to Develop a Forecasting Trend Line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of a trend line to forecast outcomes for time periods in the future, using alternative coding for time periods if necessary.

86. The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2005-2010 is Sales= -265575+132.571*year. Using the trend line, the forecast sales for the year 2012 is ________

a) $1,157.85

b) $1,157,850

c) $132,571

d) $2,673,304

e) $1,000,327

Response: See section 12.9, Using regression to develop a forecasting trend line

Difficulty: Easy

Learning Objective: 12.9 Determine the equation of the trend line to forecast outcomes for time periods in the future, using alternate coding for time periods if necessary.

87. The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2005-2010 is Sales= -265575+132.571*year. The equation of the trend line when using 1 to 6 for 2005-2010 is ________

a) -265575+132.571x

b) 132,571x

c) 97.284+132.571x

d) -263571+98x

e) 2004+37.2x

Response: See section 12.9, Using regression to develop a forecasting trend line

Difficulty: Medium

Learning Objective: 12.9 Determine the equation of the trend line to forecast outcomes for time periods in the future, using alternate coding for time periods if necessary.

88. The equation of the trend line for the data based on sales (in $1000) of a local restaurant over the years 2005-2010 is Sales= -265575+132.571*year. The equation of the trend line when using 5 to 10 for 2005-2010 is ________

a) -433+132.571x

b) 132,571x

c) -97.284+132.571x

d) -263571+433x

e) 2004+37.2x

Response: See section 12.9, Using regression to develop a forecasting trend line

Difficulty: Medium

Learning Objective: 12.9 Determine the equation of the trend line to forecast outcomes for time periods in the future, using alternate coding for time periods if necessary.

89. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Louis's regression model is ________________.

a) ŷ = -0.358 + 3.996x

b) ŷ = 0.358 + 3.996x

c) ŷ = -3.996 + 0.358x

d) ŷ = 3.996 - 0.358x

e) ŷ = 3.996 + 0.358x

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

90. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

The correlation coefficient between Louis's variables is ________________.

a) -0.73

b) 0.73

c) 0.28

d) -0.28

e) 0.00

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

91. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Louis's sample size (n) is ________________.

a) 13

b) 14

c) 12

d) 24

e) 1

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

92. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

Using  = 0.05, Louis should ________________.

a) increase the sample size

b) suspend judgment

c) not reject H0: 1 = 0

d) reject H0: 1 = 0

e) not reject H0: 0 = 0

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

93. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in $100's). Regression analysis of the data yielded the following tables.

For a lot size of 10,000 handsets, Louis' model predicts total cost will be _____.

a) $4,031.80

b) $757.60

c) $3,960.20

d) $354.01

e) $1873.077

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

94. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Abby's regression model is __________.

a) ŷ = 39.15 + 2.79x

b) ŷ = 39.15 - 1.79x

c) ŷ = 1.79 + 39.15x

d) ŷ = -1.79 + 39.15x

e) ŷ = 39.15 + 1.79x

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

95. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

The correlation coefficient between the two variables in this regression is __________.

a) 0.682478

b) -0.83

c) 0.83

d) -0.68

e) 1.0008

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

96. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Abby's sample size (n) is __________.

a) 8

b) 10

c) 11

d) 20

e) 12

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

97. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

Using  = 0.05, Abby should ________________.

a) reject H0: 1 = 0

b) not reject H0: 1 = 0

c) increase the sample size

d) suspend judgment

e) reject H0: 0 = 0

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

98. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby's dependent variable is monthly household expenditures on groceries (in $'s), and her independent variable is annual household income (in $1,000's). Regression analysis of the data yielded the following tables.

For a household with $50,000 annual income, Abby's model predicts monthly grocery expenditures of ________________.

a) $150

b) $50

c) $1,959

d) $129

e) $1288

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

99. Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie’s dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables.

Annie’s regression model can be written as: __________.

a) ŷ = 7.950352 - 0.48455x

b) ŷ = -0.48455 + 7.950352x

c) ŷ = -0.14156 + 0.105195x

d) ŷ = 0.105195 - 0.14156x

e) ŷ = 0.105195 + 0.14156x

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

100. Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie’s dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables.

The numerical value of the correlation coefficient between the historic city tour sales and the size of city population is __________.

a) 0.969785

b) 0.940483

c) 0.224675

d) -0.14156

e) 1.000000

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

101. Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie’s dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables.

Annie’s sample size is __________.

a) 2

b) 4

c) 6

d) 8

e) 10

Response: See section 12.10 Interpreting the Output

Difficulty: Easy

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

102. Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie’s dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables.

Using  = 0.05, Annie should ________________.

a) increase the sample size

b) not reject H0: 1 = 0

c) reject H0: 1 = 0

d) suspend judgment

e) reject H0: 0 = 0

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

103. Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie’s dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables.

For a city with a population of 500,000, Annie’s model predicts annual sales of ________________.

a) $70,780

b) $5,259

c) $170,780

d) $52,597

e) $152,597

Response: See section 12.10 Interpreting the Output

Difficulty: Medium

Learning Objective: 12.10 Use a computer to develop a regression analysis, and interpret the output that is associated with it.

104. If a scatter plot of variables X and Y shows a trend that can be summarized to a large degree by a straight line with slope 0.8 and y-intercept 0.2 (i.e., Y = 0.2 + 0.8X), then the correlation coefficient between X and Y is ______.

a) 0.8, and there is a causal relation between X and Y (either X causes Y or Y causes X)

b) 0.2, and there is a causal relation between X and Y (either X causes Y or Y causes X)

c) 0.8, but there is no causal relation between X and Y

d) 0.2, but there is no causal relation between X and Y

e) 0.8, and there may be a causal relation between X and Y, but not necessarily

Response: See section 12.1 Correlation

Difficulty: Medium

AACSB: Reflective thinking

Bloom’s level: Knowledge

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

105. Suppose you compute the correlation coefficient between two variables, X and Y, and obtain a value of 0.55. Then you realize that all the values for both variables have been corrupted in a way that their actual sign has been changed (positive values were turned into negative values and vice versa; only the signs have been changed). Then the actual, corrected value of the correlation coefficient ______.

a) is −0.55

b) remains unchanged

c) changes but there is not enough information to determine the correct value

d) is 0

e) is 0.50

Response: See section 12.1 Correlation

Difficulty: Medium

AACSB: Reflective thinking

Bloom’s level: Knowledge

Learning Objective: 12.1: Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.

106. In the regression equation, ŷ = 54.78 + 1.45x, the x-intercept is _______.

a) 1.45

b) −1.45

c) 54.78

d) −54.78

e) −37.8

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Hard

AACSB: Reflective thinking

Bloom’s level: Knowledge

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

107. A regression line minimizes the sum of the squared error values. This means that the regression line minimizes the sum of ______ from each point in the scatter point to the regression line.

a) the squares of the distances

b) the squares of the horizontal distances (differences in the x-coordinates)

c) the squares of the vertical distances (differences in the x-coordinates)

d) the squares of the horizontal distances (differences in the y-coordinates)

e) the squares of the vertical distances (differences in the y-coordinates)

Response: See section 12.3 Determining the Equation of the Regression Line

Difficulty: Hard

AACSB: Reflective thinking

Bloom’s level: Knowledge

Learning Objective: 12.3: Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.

108. Which of the following assertions is true about the regression line?

a) The regression line is also called the least cubes line and is found minimizing the sum of the cubes of the residuals.

b) It is found by minimizing the sum of the residuals squared, but—even though it would be unnecessarily complicated—it could also be found minimizing the sum of the residuals cubed.

c) Depending on the data, some regression lines could have only positive residuals.

d) Depending on the data, some regression lines could have all residuals equal to zero.

e) Depending on the data, some regression lines could have only negative residuals.

Ans.: d

Response: See section 12.4 Residual Analysis

Difficulty: Hard

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

109. Suppose for a given data set the regression equation is: ŷ = 54.78 + 1.45x, and the point (0.00, 24.78) is in the data set. The residual for this point is _______.

a) 24.78

b) −24.78

c) 0.00

d) 30.00

e) −30.00

Ans.: e

Response: See section 12.4 Residual Analysis

Difficulty: Medium

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.4: Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.

110. A simple regression model resulted in a sum of squares of error of 125 (i.e., SSE = 125), and the standard error is 3.95. This model is for ______ pairs of data.

a) 8

b) 9

c) 10

d) 11

e) 12

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Medium

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

111. A simple regression model for 10 pair of data resulted in a standard error of 3.95 (i.e., S_e = 3.95), and the. The sum of squares of error (SSE) is ______.

a) 187.23

b) 171.63

c) 156.03

d) 140.42

e) 124.82

Response: See section 12.5 Standard Error of the Estimate

Difficulty: Medium

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.5: Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.

112. Suppose that in a regression analysis, SST = 140, SSE = 35, and SS_yy = 23.32. Then the corresponding coefficient of determination r² = ______.

a) 0.25

b) 0.50

c) either 0.25 or −0.25, but there is not enough information to determine its sign

d) either 0.50 or −0.50, but there is not enough information to determine its sign

e) −0.50

Ans.: e

Response: See section 12.6 Coefficient of Determination

Difficulty: Hard

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.6 Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.

113. A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, S_xx = 89.333, and S_e = 3.44. The researcher wants to test if the slope is significantly positive, and he chooses a significance level of 0.05. The observed t value is ______.

a) 3.752

b) 3.852

c) 3.972

d) 3.985

e) 3.995

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Medium
AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

114. A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, SS_xx = 89.333, and S_e = 3.44. The researcher wants to test if the slope is significantly positive, and he chooses a significance level of 0.05. The critical t value is ______.

a) 2.776

b) 2.132

c) 2.015

d) 1.943

e) 1.782

Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model

Difficulty: Medium
AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.7 Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.

115. A researcher has developed the regression equation ŷ = 2.164 + 1.3657x, where n = 6, the mean of x is 8.667, SS_xx = 89.333, and S_e = 3.44. The 90% confidence interval for y when x = 1 is ______.

a) (−2.14, 9.2)

b) (−2.54, 10.6)

c) (−3.04, 9.92)

d) (−3.14, 10.2)

e) (−3.24, 10.4)

Response: See section 12.8 Estimation

Difficulty: Hard

AACSB: Reflective thinking

Bloom’s level: Application

Learning Objective: 12.8 Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.