File: Ch13, Chapter 13: Basic Multiple Regression Analysis
True/False
 Regression analysis with one dependent variable and two or more independent variables is called multiple regression.
Ans: True
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 The model y = b 0+ b 1x1+ b 2x2 + e is a secondorder regression model.
Ans: False
Response: See section 13.1 The Multiple Regression Model
Difficulty: Medium
 The model y = b 0+ b 1x1+ b 2x2 + b 3x3 + e is a firstorder regression model.
Ans: True
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 In the multiple regression model y = b 0+ b 1x1+ b 2x2 + b 3x3 + e, the b coefficients of the x variables are called partial regression coefficients.
Ans: True
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 In the model y = b 0+ b 1x1+ b 2x2 + b 3x3 + e, y is the independent variable.
Ans: False
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 In a multiple regression model, the partial regression coefficient of an independent variable represents the increase in the y variable when that independent variable is increased by one unit if the values of all other independent variables are held constant.
Ans: True
Response: See section 13.1 The Multiple Regression Model
Difficulty: Medium
 In the estimated multiple regression model y = b0+ b1x1+ b 2 x2 if the values of x_{1} and x2 are both increased by one unit, the value of y will increase by (b1+ b 2) units.
Ans: False
Response: See section 13.1 The Multiple Regression Model
Difficulty: Hard
 In the model y = b 0+ b 1x1+ b 2x2 + b 3x3 + e, e is a constant.
Ans: False
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 In the estimated multiple regression model y = b0+ b1x1+ b 2 x2 if the value of x_{1} is increased by 2 and the value of x2 is increased by 3 simultaneously, the value of y will increase by (2b1+ 3b 2) units.
Ans: False
Response: See section 13.1 The Multiple Regression Model
Difficulty: Hard
 Multiple ttests are used to determine whether the overall regression model is significant.
Ans: False
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 The F test is used to determine whether the overall regression model is significant.
Ans: True
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 The F value that is used to test for the overall significance a multiple regression model is calculated by dividing the mean square regression (MS_{reg}) by the mean square error (MS_{err}).
Ans: True
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 The F value that is used to test for the overall significance a multiple regression model is calculated by dividing the sum of mean squares regression (SS_{reg}) by the sum of squares error (SS_{err}).
Ans: False
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 The mean square error (MS_{err}) is calculated by dividing the sum of squares error (SS_{err}) by the number of observations in the data set (N).
Ans: False
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Medium
 The mean square error (MS_{err}) is calculated by dividing the sum of squares error (SS_{err}) by the number of error degrees of freedom (df_{err}).
Ans: True
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N – k – 1).
Ans: True
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Medium
 In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N – k).
Ans: False
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Medium
 The standard error of the estimate of a multiple regression model is essentially the standard deviation of the residuals for the regression model.
Ans: True
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The standard error of the estimate of a multiple regression model is computed by taking the square root of the mean squares of error.
Ans: True
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Hard
 In a multiple regression model, the proportion of the variation of the dependent variable, y, accounted for the independent variables in the regression model is given by the coefficient of multiple correlation.
Ans: False
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
Multiple Choice
 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), production plant (Kingsland, and Yorktown), and production shift (day, and evening). The response variable in this model is ______.
 a) batch size
 b) production shift
 c) production plant
 d) total cost
 e) variable cost
Ans: d
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 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), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, “shift” is ______.
 a) a response variable
 b) an independent variable
 c) a quantitative variable
 d) a dependent variable
 e) a constant
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 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), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, “batch size” is ______.
 a) a response variable
 b) an indicator variable
 c) a dependent variable
 d) a qualitative variable
 e) an independent variable
Ans: e
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The response variable in this model is _____.
 a) family size
 b) expenditures on groceries
 c) household income
 d) suburban
 e) household neighborhood
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The “neighborhood” variable in this model is ______.
 a) an independent variable
 b) a response variable
 c) a quantitative variable
 d) a dependent variable
 e) a constant
Ans: a
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A market analyst is developing a regression model to predict monthly household expenditures on groceries as a function of family size, household income, and household neighborhood (urban, suburban, and rural). The “income” variable in this model is ____.
 a) an indicator variable
 b) a response variable
 c) a qualitative variable
 d) a dependent variable
 e) an independent variable
Ans: e
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The response variable in this model is ______.
 a) plant manager compensation
 b) plant capacity
 c) number of employees
 d) plant technology
 e) nuclear
Ans: a
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The “plant technology” variable in this model is ______.
 a) a response variable
 b) a dependent variable
 c) a quantitative variable
 d) an independent variable
 e) a constant
Ans: d
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A human resources analyst is developing a regression model to predict electricity production plant manager compensation as a function of production capacity of the plant, number of employees at the plant, and plant technology (coal, oil, and nuclear). The “plant technology” variable in this model is ______.
 a) a qualitative variable
 b) a dependent variable
 c) a response variable
 d) an indicator variable
 e) an independent variable
Ans: a
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The response variable in this model is _______.
 a) heated area
 b) number of bedrooms
 c) market value
 d) central heating
 e) residential houses
Ans: c
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The “central heating” variable in this model is _______.
 a) a response variable
 b) an independent variable
 c) a quantitative variable
 d) a dependent variable
 e) a constant
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The “central heating” variable in this model is _______.
 a) a response variable
 b) an indicator variable
 c) a dependent variable
 d) a qualitative variable
 e) an independent variable
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 The multiple regression formulas used to estimate the regression coefficients are designed to ________________.
 a) minimize the total sum of squares (SST)
 b) minimize the sum of squares of error (SSE)
 c) maximize the standard error of the estimate
 d) maximize the pvalue for the calculated F value
 e) minimize the mean error
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



The regression equation for this analysis is ____________.
 a) y = 616.6849 + 3.33833 x1+ 1.780075 x2
 b) y = 154.5535 – 1.43058 x1+ 5.30407 x2
 c) y = 616.6849 – 3.33833 x1 1.780075 x2
 d) y = 154.5535 + 2.333548 x1 + 0.335605 x2
 e) y = 616.6849 – 3.33833 x1+ 1.780075 x2
Ans: e
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



The sample size for this analysis is ____________.
 a) 19
 b) 17
 c) 34
 d) 15
 e) 18
Ans: e
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



Using a = 0.01 to test the null hypothesis H0: b 1 = b 2 = 0, the critical F value is ____.
 a) 68
 b) 6.36
 c) 8.40
 d) 6.11
 e) 3.36
Ans: b
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



Using a = 0.05 to test the null hypothesis H0: b1 = 0, the critical t value is ____.
 a) ± 1.753
 b) ± 2.110
 c) ± 2.131
 d) ± 1.740
 e) ± 2.500
Ans: c
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



These results indicate that ____________.
 a) none of the predictor variables are significant at the 5% level
 b) each predictor variable is significant at the 5% level
 c) x1is significant at the 5% level
 d) x2is significant at the 5% level
 e) the intercept is not significant at 5% level
Ans: d
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
616.6849 
154.5534 
3.990108 
0.000947 
x1 
3.33833 
2.333548 
1.43058 
0.170675 
x2 
1.780075 
0.335605 
5.30407 
5.83E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
121783 
60891.48 
14.76117 
0.000286 
Residual 
15 
61876.68 
4125.112 


Total 
17 
183659.6 



For x1= 60 and x2 = 200, the predicted value of y is ____________.
 a) 1,173.00
 b) 772.40
 c) 460.97
 d) 615.13
 e) 987.78
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



The regression equation for this analysis is ____________.
 a) y = 752.0833 + 11.87375 x1+ 1.908183 x2
 b) y = 752.0833 + 336.3158 x1+ 2.236241 x2
 c) y = 336.3158 + 5.32047 x1+ 0.662742 x2
 d) y = 2.236241 + 2.231711 x1 + 2.879226 x2
 e) y = 2.236241 + 2.231711 x1 2.879226 x2
Ans: a
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



The sample size for this analysis is ____________.
 a) 12
 b) 15
 c) 14
 d) 28
 e) 24
Ans: b
Response: See section 13.1 The Multiple Regression Model
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



Using a = 0.05 to test the null hypothesis H0: b1 = b2 = 0, the critical F value is ____.
 a) 74
 b) 3.89
 c) 4.75
 d) 4.60
 e) 2.74
Ans: b
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F

pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



Using a = 0.10 to test the null hypothesis H0: b2 = 0, the critical t value is ____.
 a) ±1.345
 b) ±1.356
 c) ±1.761
 d) ±2.782
 e) ±1.782
Ans: e
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



These results indicate that ____________.
 a) none of the predictor variables are significant at the 5% level
 b) each predictor variable is significant at the 5% level
 c) x1is the only predictor variable significant at the 5% level
 d) x2is the only predictor variable significant at the 5% level
 e) the intercept is not significant at the 5% level
Ans: b
Response: See section 13.2 Significance Tests of the Regression Model and its Coefficients
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
752.0833 
336.3158 
2.236241 
0.042132 
x1 
11.87375 
5.32047 
2.231711 
0.042493 
x2 
1.908183 
0.662742 
2.879226 
0.01213 
Source 
df 
SS 
MS 
F

pvalue 
Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 
Residual 
12 
181184.1 
15098.67 


Total 
14 
384877.4 



For x1= 60 and x2 = 200, the predicted value of y is ____________.
 a) 24
 b) 711.98
 c) 788.09
 d) 1,846.15
 e) 2,546.98
Ans: d
Response: See section 13.1 The Multiple Regression Model
Difficulty: Medium
 In regression analysis, outliers may be identified by examining the ________.
 a) coefficient of determination
 b) coefficient of correlation
 c) pvalues for the partial coefficients
 d) residuals
 e) Rsquared value
Ans: d
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The number of degrees of freedom for regression is __________.
 a) 1
 b) 4
 c) 34
 d) 30
 e) 35
Ans: b
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The number of degrees of freedom for error is __________.
 a) 1
 b) 4
 c) 34
 d) 30
 e) 35
Ans: d
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The MSR value is __________.
 a) 700.00
 b) 350.00
 c) 233.33
 d) 175.00
 e) 275.00
Ans: d
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The MSE value is __________.
 a) 8.57
 b) 8.82
 c) 10.00
 d) 75.00
 e) 20.00
Ans: c
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The observed F value is __________.
 a) 17.50
 b) 2.33
 c) 0.70
 d) 0.43
 e) 0.50
Ans: a
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The value of the standard error of the estimate se is __________.
 a) 13.23
 b) 3.16
 c) 17.32
 d) 26.46
 e) 10.00
Ans: b
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The R2 value is __________.
 a) 0.80
 b) 0.70
 c) 0.66
 d) 0.76
 e) 0.30
Ans: b
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis with n = 35 and four independent variables.
Source 
df 
SS 
MS 
F 
p 
Regression 

700 



Error 





Total 

1000 



The adjusted R2 value is __________.
 a) 0.80
 b) 0.70
 c) 0.66
 d) 0.76
 e) 0.30
Ans: c
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The sample size for the analysis is __________.
 a) 30
 b) 25
 c) 10
 d) 5
 e) 31
Ans: e
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The number of independent variables in the analysis is __________.
 a) 30
 b) 25
 c) 1
 d) 5
 e) 2
Ans: d
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The MSR value is __________.
 a) 20
 b) 400
 c) 2000
 d) 500
 e) 30
Ans: b
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The SSE value is __________.
 a) 20
 b) 400
 c) 2000
 d) 500
 e) 2500
Ans: d
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Easy
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The MSE value is __________.
 a) 20
 b) 400
 c) 2000
 d) 500
 e) 100
Ans: a
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The observed F value is __________.
 a) 20
 b) 400
 c) 2000
 d) 500
 e) 10
Ans: a
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The value of the standard error of the estimate se is __________.
 a) 20.00
 b) 44.72
 c) 4.47
 d) 22.36
 e) 12.47
Ans: c
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The R2 value is __________.
 a) 0.80
 b) 0.70
 c) 0.66
 d) 0.76
 e) 1.00
Ans: a
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The adjusted R2 value is __________.
 a) 0.80
 b) 0.70
 c) 0.66
 d) 0.86
 e) 0.76
Ans: e
Response: See section 13.3 Residuals, Standard Error of the Estimate, and R^{2}
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
624.5369 
78.49712 
7.956176 
6.88E06 
x1 
8.569122 
1.652255 
5.186319 
0.000301 
x2 
4.736515 
0.699194 
6.774248 
3.06E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
1660914 
830457.1 
58.31956 
1.4E06 
Residual 
11 
156637.5 
14239.77 


Total 
13 
1817552 



These results indicate that ____________.
 a) none of the predictor variables are significant at the 5% level
 b) each predictor variable is significant at the 5% level
 c) x1is the only predictor variable significant at the 5% level
 d) x2is the only predictor variable significant at the 5% level
 e) the intercept is not significant at 5% level
Ans: b
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
624.5369 
78.49712 
7.956176 
6.88E06 
x1 
8.569122 
1.652255 
5.186319 
0.000301 
x2 
4.736515 
0.699194 
6.774248 
3.06E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
1660914 
830457.1 
58.31956 
1.4E06 
Residual 
11 
156637.5 
14239.77 


Total 
13 
1817552 



For x1= 30 and x2 = 100, the predicted value of y is ____________.
 a) 77
 b) 1,173.00
 c) 1,355.26
 d) 615.13
 e) 6153.13
Ans: c
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
624.5369 
78.49712 
7.956176 
6.88E06 
x1 
8.569122 
1.652255 
5.186319 
0.000301 
x2 
4.736515 
0.699194 
6.774248 
3.06E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
1660914 
830457.1 
58.31956 
1.4E06 
Residual 
11 
156637.5 
14239.77 


Total 
13 
1817552 



The coefficient of multiple determination is ____________.
 a) 0592
 b) 0.9138
 c) 0.1149
 d) 0.9559
 e) 1.0000
Ans: b
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
624.5369 
78.49712 
7.956176 
6.88E06 
x1 
8.569122 
1.652255 
5.186319 
0.000301 
x2 
4.736515 
0.699194 
6.774248 
3.06E05 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
1660914 
830457.1 
58.31956 
1.4E06 
Residual 
11 
156637.5 
14239.77 


Total 
13 
1817552 



The adjusted R^{2} is ____________.
 a) 0.9138
 b) 0.9408
 c) 0.8981
 d) 0.8851
 e) 0.8891
Ans: c
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



The regression equation for this analysis is ____________.
 a) y = 302689 + 1153309 x1+ 1455998 x2
 b) y = 139.609 + 24.24619 x1+ 32.10171 x2
 c) y = 2548.989 + 22.25267 x1+ 17.44559 x2
 d) y = 0.05477 + 1.089586 x1 + 1.840105 x2
 e) y = 0.05477 + 1.089586 x1+ 1.840105 x2
Ans: b
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



The sample size for this analysis is ____________.
 a) 17
 b) 13
 c) 16
 d) 11
 e) 15
Ans: c
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Easy
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



Using a = 0.01 to test the null hypothesis H0: b 1 = b 2 = 0, the critical F value is ____.
 a) 99
 b) 5.70
 c) 1.96
 d) 4.84
 e) 6.70
Ans: e
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



Using a = 0.01 to test the null hypothesis H0: b2 = 0, the critical t value is ____.
 a) ± 1.174
 b) ± 2.093
 c) ± 2.131
 d) ± 4.012
 e) ± 3.012
Ans: e
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



These results indicate that ____________.
 a) none of the predictor variables are significant at the 5% level
 b) each predictor variable is significant at the 5% level
 c) x1is the only predictor variable significant at the 5% level
 d) x2is the only predictor variable significant at the 5% level
 e) all variables are significant at 5% level
Ans: a
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



For x1= 40 and x2 = 90, the predicted value of y is ____________.
 a) 77
 b) 1,173.00
 c) 1,355.26
 d) 3,719.39
 e) 1,565.75
Ans: d
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



The coefficient of multiple determination is ____________.
 a) 2079
 b) 0. 0860
 c) 0.5440
 d) 0.7921
 e) 0.5000
Ans: a
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium
 A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
139.609 
2548.989 
0.05477 
0.957154 
x1 
24.24619 
22.25267 
1.089586 
0.295682 
x2 
32.10171 
17.44559 
1.840105 
0.08869 
Source 
df 
SS 
MS 
F 
pvalue 
Regression 
2 
302689 
151344.5 
1.705942 
0.219838 
Residual 
13 
1153309 
88716.07 


Total 
15 
1455998 



The adjusted R^{2} is ____________.
 a) 0.2079
 b) 0.0860
 c) 0.5440
 d) 0.7921
 e) 1.0000
Ans: b
Response: See section 13.4 Interpreting Multiple Regression Computer Output
Difficulty: Medium