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
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.

Trusted by Thousands of
Students

Here are what students say about us.

Copyright ©2022 All rights reserved. | CoursePaper is not sponsored or endorsed by any college or university.