# Chapter 15 A real estate builder wishes to determine how house size

Document Type

Test Prep

Book Title

Basic Business Statistics 13th Edition

Authors

David M. Levine, Kathryn A. Szabat, Mark L. Berenson

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Multiple Regression Model Building 15-1

CHAPTER 15: MULTIPLE REGRESSION

MODEL BUILDING

1. A real estate builder wishes to determine how house size (House) is influenced by family income

(Income), family size (Size), and education of the head of household (School). House size is

measured in hundreds of square feet, income is measured in thousands of dollars, and education is

in years. The builder randomly selected 50 families and constructed the multiple regression

model. The business literature involving human capital shows that education influences an

individual’s annual income. Combined, these may influence family size. With this in mind, what

should the real estate builder be particularly concerned with when analyzing the multiple

regression model?

a) Randomness of error terms

b) Collinearity

c) Normality of residuals

d) Missing observations

2. A microeconomist wants to determine how corporate sales are influenced by capital and wage

spending by companies. She proceeds to randomly select 26 large corporations and record

information in millions of dollars. A statistical analyst discovers that capital spending by

corporations has a significant inverse relationship with wage spending. What should the

microeconomist who developed this multiple regression model be particularly concerned with?

a) Randomness of error terms

b) Collinearity

c) Normality of residuals

d) Missing observations

3. In multiple regression, the __________ procedure permits variables to enter and leave the model

at different stages of its development.

a) forward selection

b) residual analysis

c) backward elimination

d) stepwise regression

15-2 Multiple Regression Model Building

4. A regression diagnostic tool used to study the possible effects of collinearity is

a) the slope.

b) the Y-intercept.

c) the VIF.

d) the standard error of the estimate.

5. Which of the following is used to find a "best" model?

a) Odds ratio

b) Mallow's Cp

c) Standard error of the estimate

d) SST

6. The Variance Inflationary Factor (VIF) measures the

a) correlation of the X variables with the Y variable.

b) correlation of the X variables with each other.

c) contribution of each X variable with the Y variable after all other X variables are included

in the model.

d) standard deviation of the slope.

7. The p

C statistic is used

a) to determine if there is a problem of collinearity.

b) if the variances of the error terms are all the same in a regression model.

c) to choose the best model.

d) to determine if there is an irregular component in a time series.

Multiple Regression Model Building 15-3

SCENARIO 15-1

A certain type of rare gem serves as a status symbol for many of its owners. In theory, for low prices,

the demand increases and it decreases as the price of the gem increases. However, experts

hypothesize that when the gem is valued at very high prices, the demand increases with price due to

the status owners believe they gain in obtaining the gem. Thus, the model proposed to best explain

the demand for the gem by its price is the quadratic model:

Y=

β

0+

β

1X+

β

2X2+

ε

where Y = demand (in thousands) and X = retail price per carat.

This model was fit to data collected for a sample of 12 rare gems of this type. A portion of the

computer analysis obtained from Microsoft Excel is shown below:

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.994

R Square 0.988

Standard Error 12.42

Observations 12

ANOVA

df SS MS F Signif F

Regression 2 115145 57573 373 0.0001

Residual 9 1388 154

Total 11 116533

Coeff StdError t Stat P-value

Intercept 286.42 9.66 29.64 0.0001

Price – 0.31 0.06 – 5.14 0.0006

Price Sq 0.000067 0.00007 0.95 0.3647

8. Referring to Scenario 15-1, what is the value of the test statistic for testing whether there is an

upward curvature in the response curve relating the demand (Y) and the price (X)?

a) -5.14

b) 0.95

c) 373

d) None of the above.

15-4 Multiple Regression Model Building

9. Referring to Scenario 15-1, what is the p-value associated with the test statistic for testing

whether there is an upward curvature in the response curve relating the demand (Y) and the price

(X)?

a) 0.0001

b) 0.0006

c) 0.3647

d) None of the above.

10. Referring to Scenario 15-1, what is the correct interpretation of the coefficient of multiple

determination?

a) 98.8% of the total variation in demand can be explained by the linear relationship

between demand and price.

b) 98.8% of the total variation in demand can be explained by the quadratic relationship

between demand and price.

c) 98.8% of the total variation in demand can be explained by the addition of the square

term in price.

d) 98.8% of the total variation in demand can be explained by just the square term in price.

11. Referring to Scenario 15-1, does there appear to be significant upward curvature in the response

curve relating the demand (Y) and the price (X) at 10% level of significance?

a) Yes, since the p-value for the test is less than 0.10.

b) No, since the value of

β

2 is near 0.

c) No, since the p-value for the test is greater than 0.10.

d) Yes, since the value of

β

2 is positive.

12. True or False: Referring to Scenario 15-1, a more parsimonious simple linear model is likely to

be statistically superior to the fitted curvilinear for predicting sale price (Y).

Multiple Regression Model Building 15-5

SCENARIO 15-2

In Hawaii, condemnation proceedings are under way to enable private citizens to own the property

that their homes are built on. Until recently, only estates were permitted to own land, and

homeowners leased the land from the estate. In order to comply with the new law, a large Hawaiian

estate wants to use regression analysis to estimate the fair market value of the land. The following

model was fit to data collected for n = 20 properties, 10 of which are located near a cove.

Model 1:

Y=

β

0+

β

1X1+

β

2X2+

β

3X1X2+

β

4X1

2+

β

5X1

2X2+

ε

where Y = Sale price of property in thousands of dollars

X1 = Size of property in thousands of square feet

X2 = 1 if property located near cove, 0 if not

Using the data collected for the 20 properties, the following partial output obtained from Microsoft

Excel is shown:

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.985

R Square 0.970

Standard Error 9.5

Observations 20

ANOVA

df SS MS F Signif F

Regression 5 28324 5664 62.2 0.0001

Residual 14 1279 91

Total 19 29063

Coeff StdError t Stat P-value

Intercept – 32.1 35.7 – 0.90 0.3834

Size 12.2 5.9 2.05 0.0594

Cove – 104.3 53.5 – 1.95 0.0715

Size*Cove 17.0 8.5 1.99 0.0661

SizeSq – 0.3 0.2 – 1.28 0.2204

SizeSq*Cove – 0.3 0.3 – 1.13 0.2749

13. Referring to Scenario 15-2, is the overall model statistically adequate at a 0.05 level of

significance for predicting sale price (Y)?

a) No, since some of the t tests for the individual variables are not significant.

b) No, since the standard deviation of the model is fairly large.

c) Yes, since none of the

β

-estimates are equal to 0.

d) Yes, since the p-value for the test is smaller than 0.05.

15-6 Multiple Regression Model Building

14. Referring to Scenario 15-2, given a quadratic relationship between sale price (Y) and property

size (X1), what null hypothesis would you test to determine whether the curves differ from cove

and non-cove properties?

a)

H

0:

β

2=

β

3=

β

5=0

b)

H

0:

β

4=

β

5=0

c)

H

0:

β

3=

β

5=0

d)

H

0:

β

2=0

15. Referring to Scenario 15-2, given a quadratic relationship between sale price (Y) and property

size (X1), what test should be used to test whether the curves differ from cove and non-cove

properties?

a) F test for the entire regression model.

b) t test on each of the coefficients in the entire regression model.

c) Partial F test on the subset of the appropriate coefficients.

d) t test on each of the subsets of the appropriate coefficients.

16. If a group of independent variables are not significant individually but are significant as a group

at a specified level of significance, this is most likely due to

a) autocorrelation.

b) the presence of dummy variables.

c) the absence of dummy variables.

d) collinearity.

Multiple Regression Model Building 15-7

17. As a project for his business statistics class, a student examined the factors that determined

parking meter rates throughout the campus area. Data were collected for the price per hour of

parking, blocks to the quadrangle, and one of the three jurisdictions: on campus, in downtown

and off campus, or outside of downtown and off campus. The population regression model

hypothesized is 011 22 33iiii

YXXX

ββ β β

ε

=+ + + +

where

Y is the meter price

X1 is the number of blocks to the quad

X2 is a dummy variable that takes the value 1 if the meter is located in downtown and off campus

and the value 0 otherwise

X3 is a dummy variable that takes the value 1 if the meter is located outside of downtown and off

campus, and the value 0 otherwise

Suppose that whether the meter is located on campus is an important explanatory factor. Why

should the variable that depicts this attribute not be included in the model?

a) Its inclusion will introduce autocorrelation.

b) Its inclusion will introduce collinearity.

c) Its inclusion will inflate the standard errors of the estimated coefficients.

d) Both (b) and (c).

18. True or False: The Variance Inflationary Factor (VIF) measures the correlation of the X variables

with the Y variable.

19. True or False: Collinearity is present when there is a high degree of correlation between

independent variables.

20. True or False: Collinearity is present when there is a high degree of correlation between the

dependent variable and any of the independent variables.

15-8 Multiple Regression Model Building

21. True or False: A high value of R2 significantly above 0 in multiple regression accompanied by

insignificant t-values on all parameter estimates very often indicates a high correlation between

independent variables in the model.

22. True or False: One of the consequences of collinearity in multiple regression is inflated standard

errors in some or all of the estimated slope coefficients.

23. True or False: One of the consequences of collinearity in multiple regression is biased estimates

on the slope coefficients.

24. True or False: Collinearity is present if the dependent variable is linearly related to one of the

explanatory variables.

25. True or False: Collinearity will result in excessively low standard errors of the parameter

estimates reported in the regression output.

26. True or False: The parameter estimates are biased when collinearity is present in a multiple

regression equation.

Multiple Regression Model Building 15-9

27. True or False: Two simple regression models were used to predict a single dependent variable.

Both models were highly significant, but when the two independent variables were placed in the

same multiple regression model for the dependent variable, R2 did not increase substantially and

the parameter estimates for the model were not significantly different from 0. This is probably an

example of collinearity.

28. True or False: So that we can fit curves as well as lines by regression, we often use mathematical

manipulations for converting one variable into a different form. These manipulations are called

dummy variables.

SCENARIO 15-3

A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of

14 people suffering from extreme muscle constriction. She gave each a vial containing a dose (X) of

the drug and recorded the time to relief (Y) measured in seconds for each. She fit a curvilinear model

to this data. The results obtained by Microsoft Excel follow

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.747

R Square 0.558

Adjusted R Square 0.478

Standard Error 863.1

Observations 14

ANOVA

df SS MS F Signif F

Regression 2 10344797 5172399 6.94 0.0110

Residual 11 8193929 744903

Total 13 18538726

Coeff StdError t Stat P-value

Intercept 1283.0 352.0 3.65 0.0040

Dose 25.228 8.631 2.92 0.0140

DoseSq 0.8604 0.3722 2.31 0.0410

15-10 Multiple Regression Model Building

29. Referring to Scenario 15-3, the prediction of time to relief for a person receiving a dose of 10

units of the drug is ________.

30. Referring to Scenario 15-3, suppose the chemist decides to use an F test to determine if there is a

significant curvilinear relationship between time and dose. The p-value of the test is ________.

31. Referring to Scenario 15-3, suppose the chemist decides to use an F test to determine if there is a

significant curvilinear relationship between time and dose. The value of the test statistic is

________.

32. True or False: Referring to Scenario 15-3, suppose the chemist decides to use an F test to

determine if there is a significant curvilinear relationship between time and dose. If she chooses

to use a level of significance of 0.05, she would decide that there is a significant curvilinear

relationship.

33. True or False: Referring to Scenario 15-3, suppose the chemist decides to use an F test to

determine if there is a significant curvilinear relationship between time and dose. If she chooses

to use a level of significance of 0.01 she would decide that there is a significant curvilinear

relationship.

Multiple Regression Model Building 15-11

34. Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine if there is a

significant difference between a linear model and a curvilinear model that includes a linear term.

The p-value of the test statistic for the contribution of the curvilinear term is ________.

35. Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine if the linear

term is significant. The value of the test statistic is ______.

36. Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine if the linear

term is significant. The p-value of the test is ______.

37. True or False: Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine

if there is a significant difference between a linear model and a curvilinear model that includes a

linear term. If she used a level of significance of 0.05, she would decide that the linear model is

sufficient.

38. True or False: Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine

if there is a significant difference between a linear model and a curvilinear model that includes a

linear term. If she used a level of significance of 0.01, she would decide that the linear model is

sufficient.

15-12 Multiple Regression Model Building

39. True or False: Referring to Scenario 15-3, suppose the chemist decides to use a t test to determine

if the linear term is significant. Using a level of significance of 0.05, she would decide that the

linear term is significant.

40. In multiple regression, the __________ procedure permits variables to enter and leave the model

at different stages of its development.

41. A regression diagnostic tool used to study the possible effects of collinearity is ______.

42. The _______ (larger/smaller) the value of the Variance Inflationary Factor, the higher is the

collinearity of the X variables.

43. The logarithm transformation can be used

a) to overcome violations to the autocorrelation assumption.

b) to test for possible violations to the autocorrelation assumption.

c) to overcome violations to the homoscedasticity assumption.

d) to test for possible violations to the homoscedasticity assumption.

Multiple Regression Model Building 15-13

44. The logarithm transformation can be used

a) to overcome violations to the autocorrelation assumption.

b) to test for possible violations to the autocorrelation assumption.

c) to change a nonlinear model into a linear model.

d) to change a linear independent variable into a nonlinear independent variable.

45. Which of the following will NOT change a nonlinear model into a linear model?

a) Quadratic regression model

b) Logarithmic transformation

c) Square-root transformation

d) Variance inflationary factor

46. An independent variable Xj is considered highly correlated with the other independent variables if

a) 5

j

VIF <

b) 5

j

VIF >

c)

j

i

VIF VIF< for ij≠

d)

j

i

VIF VIF> for ij≠

47. True or False: The goals of model building are to find a good model with the fewest independent

variables that is easier to interpret and has lower probability of collinearity.

15-14 Multiple Regression Model Building

48. Using the best-subsets approach to model building, models are being considered when their

a) p

Ck>

b) p

Ck≤

c)

()

1

p

Ck>+

d)

()

1

p

Ck≤+

49. True or False: In data mining where huge data sets are being explored to discover relationships

among a large number of variables, the best-subsets approach is more practical than the stepwise

regression approach.

50. True or False: The stepwise regression approach takes into consideration all possible models.

51. True or False: In stepwise regression, an independent variable is not allowed to be removed from

the model once it has entered into the model.

52. True or False: Using the Cp statistic in model building, all models with

()

1

p

Ck≤+

are equally

good.

Multiple Regression Model Building 15-15

SCENARIO 15-4

The superintendent of a school district wanted to predict the percentage of students passing a sixth-

grade proficiency test. She obtained the data on percentage of students passing the proficiency test

(% Passing), daily mean of the percentage of students attending class (% Attendance), mean teacher

salary in dollars (Salaries), and instructional spending per pupil in dollars (Spending) of 47 schools in

the state.

Let Y = % Passing as the dependent variable, 1

X= % Attendance, 2

X= Salaries and 3

X= Spending.

The coefficient of multiple determination ( 2

j

R

) of each of the 3 predictors with all the other

remaining predictors are, respectively, 0.0338, 0.4669, and 0.4743.

The output from the best-subset regressions is given below:

Adjusted

Model Variables Cp k R Square R Square Std. Error

1 X1 3.05 2 0.6024 0.5936 10.5787

2 X1X2 3.66 3 0.6145 0.5970 10.5350

3 X1X2X3 4.00 4 0.6288 0.6029 10.4570

4 X1X3 2.00 3 0.6288 0.6119 10.3375

5 X2 67.35 2 0.0474 0.0262 16.3755

6 X2X3 64.30 3 0.0910 0.0497 16.1768

7 X3 62.33 2 0.0907 0.0705 15.9984

Following is the residual plot for % Attendance:

% Attendance Residual Plot

-40

-30

-20

-10

0

10

20

88 89 90 91 92 93 94 95 96 97 98

% Attendance

Residuals

15-16 Multiple Regression Model Building

Following is the output of several multiple regression models:

Model (I):

Coefficients Standard

Error

t Stat P-value Lower 95% Upper 95%

Intercept -753.4225 101.1149 -7.4511 2.88E-09 -957.3401 -549.5050

% Attendance 8.5014 1.0771 7.8929 6.73E-10 6.3292 10.6735

Salary 6.85E-07 0.0006 0.0011 0.9991 -0.0013 0.0013

Spending 0.0060 0.0046 1.2879 0.2047 -0.0034 0.0153

Model (II):

Coefficients Standard Error t Stat P-value

Intercept -753.4086 99.1451 -7.5991 0.0000

% Attendance 8.5014 1.0645 7.9862 0.0000

Spending 0.0060 0.0034 1.7676 0.0840

Model (III):

df SS MS F Significance F

Regression 2 8162.9429 4081.4714 39.8708 0.0000

Residual 44 4504.1635 102.3674

Total 46 12667.1064

Coefficients Standard

Error

t Stat P-value

Intercept 6672.8367 3267.7349 2.0420 0.0472

% Attendance -150.5694 69.9519 -2.1525 0.0369

% Attendance

Squared

0.8532 0.3743 2.2792 0.0276

53. Referring to Scenario 15-4, what are, respectively, the values of the variance inflationary factor of

the 3 predictors?

54. True or False: Referring to Scenario 15-4, there is reason to suspect collinearity between some

pairs of predictors.

Multiple Regression Model Building 15-17

55. Referring to Scenario 15-4, which of the following predictors should first be dropped to remove

collinearity?

a) 1

X

b) 2

X

c) 3

X

d) None of the above

56. Referring to Scenario 15-4, which of the following models should be taken into consideration

using the Mallows’ p

C statistic?

a) 13

,XX

b) 123

,,XXX

c) both of the above

d) None of the above

57. Referring to Scenario 15-4, the “best” model using a 5% level of significance among those

chosen by the p

C statistic is

a) 13

,XX

b) 123

,,XXX

c) either of the above

d) None of the above

58. Referring to Scenario 15-4, the “best” model chosen using the adjusted R-square statistic is

a) 13

,XX

b) 123

,,XXX

c) either of the above

d) None of the above

15-18 Multiple Regression Model Building

59. Referring to Scenario 15-4, the better model using a 5% level of significance derived from the

“best” model above is

a) 1

X

b) 3

X

c) 13

,XX

d) 123

,,XXX

60. True or False: Referring to Scenario 15-4, the residual plot suggests that a nonlinear model on %

attendance may be a better model.

61. Referring to Scenario 15-4, what is the value of the test statistic to determine whether the

quadratic effect of daily average of the percentage of students attending class on percentage of

students passing the proficiency test is significant at a 5% level of significance?

62. Referring to Scenario 15-4, what is the p-value of the test statistic to determine whether the

quadratic effect of daily average of the percentage of students attending class on percentage of

students passing the proficiency test is significant at a 5% level of significance?

63. True or False: Referring to Scenario 15-4, the null hypothesis should be rejected when testing

whether the quadratic effect of daily average of the percentage of students attending class on

percentage of students passing the proficiency test is significant at a 5% level of significance.

Multiple Regression Model Building 15-19

64. True or False: Referring to Scenario 15-4, the quadratic effect of daily average of the percentage

of students attending class on percentage of students passing the proficiency test is not significant

at a 5% level of significance.

SCENARIO 15-5

What are the factors that determine the acceleration time (in sec.) from 0 to 60 miles per hour of a

car? Data on the following variables for 171 different vehicle models were collected:

Accel Time: Acceleration time in sec.

Cargo Vol: Cargo volume in cu. ft.

HP: Horsepower

MPG: Miles per gallon

SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0

Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0

The coefficient of multiple determination ( 2

j

R

) for the regression model using each of the 5 variables

j

X

as the dependent variable and all other X variables as independent variables are, respectively,

0.7461, 0.5676, 0.6764, 0.8582, 0.6632.

65. Referring to Scenario 15-5, what is the value of the variance inflationary factor of Cargo Vol?

66. Referring to Scenario 15-5, what is the value of the variance inflationary factor of HP?

67. Referring to Scenario 15-5, what is the value of the variance inflationary factor of MPG?

15-20 Multiple Regression Model Building

68. Referring to Scenario 15-5, what is the value of the variance inflationary factor of SUV?

69. Referring to Scenario 15-5, what is the value of the variance inflationary factor of Sedan?

70. True or False: Referring to Scenario 15-5, there is reason to suspect collinearity between some

pairs of predictors based on the values of the variance inflationary factor.

SCENARIO 15-6

Given below are results from the regression analysis on 40 observations where the dependent variable

is the number of weeks a worker is unemployed due to a layoff (Y) and the independent variables are

the age of the worker (X1), the number of years of education received (X2), the number of years at the

previous job (X3), a dummy variable for marital status (X4: 1 = married, 0 = otherwise), a dummy

variable for head of household (X5: 1 = yes, 0 = no) and a dummy variable for management position

(X6: 1 = yes, 0 = no).

The coefficient of multiple determination ( 2

j

R

) for the regression model using each of the 6 variables

j

X

as the dependent variable and all other X variables as independent variables are, respectively,

0.2628, 0.1240, 0.2404, 0.3510, 0.3342 and 0.0993.

The partial results from best-subset regression are given below:

Model R Square Adj. R Square Std. Error

X1X5X6 0.4568 0.4116 18.3534

X1X2X5X6 0.4697 0.4091 18.3919

X1X3X5X6 0.4691 0.4084 18.4023

X1X2X3X5X6 0.4877 0.4123 18.3416

X1X2X3X4X5X6 0.4949 0.4030

18.4861

71. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Age?

Multiple Regression Model Building 15-21

72. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Edu?

73. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Job Yr?

74. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Married?

75. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Head?

76. Referring to Scenario 15-6, what is the value of the variance inflationary factor of Manager?

77. True or False: Referring to Scenario 15-6, there is reason to suspect collinearity between some

pairs of predictors based on the values of the variance inflationary factor.

15-22 Multiple Regression Model Building

78. True or False: Referring to Scenario 15-6, the variable X1 should be dropped to remove

collinearity?

79. True or False: Referring to Scenario 15-6, the variable X2 should be dropped to remove

collinearity?

80. True or False: Referring to Scenario 15-6, the variable X3 should be dropped to remove

collinearity?

81. True or False: Referring to Scenario 15-6, the variable X4 should be dropped to remove

collinearity?

82. True or False: Referring to Scenario 15-6, the variable X5 should be dropped to remove

collinearity?

83. True or False: Referring to Scenario 15-6, the variable X6 should be dropped to remove

collinearity?

Multiple Regression Model Building 15-23

84. Referring to Scenario 15-6, what is the value of the Mallow’s Cp statistic for the model that

includes X1, X5 and X6?

85. Referring to Scenario 15-6, what is the value of the Mallow’s Cp statistic for the model that

includes X1, X2, X5 and X6?

86. Referring to Scenario 15-6, what is the value of the Mallow’s Cp statistic for the model that

includes X1, X3, X5 and X6?

87. Referring to Scenario 15-6, what is the value of the Mallow’s Cp statistic for the model that

includes X1, X2, X3, X5 and X6?

88. Referring to Scenario 15-6, what is the value of the Mallow’s Cp statistic for the model that

includes all the six independent variables?

89. True or False: Referring to Scenario 15-6, the model that includes X1, X5 and X6 should be among

the appropriate models using the Mallow’s Cp statistic.

15-24 Multiple Regression Model Building

90. True or False: Referring to Scenario 15-6, the model that includes X1, X2, X5 and X6 should be

among the appropriate models using the Mallow’s Cp statistic.

91. True or False: Referring to Scenario 15-6, the model that includes X1, X3, X5 and X6 should be

among the appropriate models using the Mallow’s Cp statistic.

92. True or False: Referring to Scenario 15-6, the model that includes X1, X2, X3, X5 and X6 should be

among the appropriate models using the Mallow’s Cp statistic.

93. True or False: Referring to Scenario 15-6, the model that includes all the six independent

variables should be among the appropriate models using the Mallow’s Cp statistic.

94. True or False: Referring to Scenario 15-6, the model that includes all six independent variables

should be selected using the adjusted r2 statistic.

95. True or False: Referring to Scenario 15-6, the model that includes X1, X2, X3, X5 and X6 should be

selected using the adjusted r2 statistic.

Multiple Regression Model Building 15-25

96. True or False: Referring to Scenario 15-6, the model that includes X1, X5 and X6 should be

selected using the adjusted r2 statistic.

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