Unformatted text preview: Business Statistics 59. Multicollinearity is severe if the largest variance inflation factor (VIF) is greater than
_____.
A. 100
B. 5
C. 10
D. 1 60. In a multiple regression model we can conclude that multicollinearity exists if the average
variance inflation factor
A. 100
B. 5
C. 10
D. 1 is substantially greater than _____. 61. Multicollinearity affects the ability of the ____ statistic in assessing the importance of an
independent variable.
A. C
B. F
C. t
D. VIP 62. Significant _________ may exist when the overall Fstatistic is significant and the
individual t statistics for all independent variables is insignificant.
A. autocorrelation
B. independence
C. Multicollinearity
D. outliers 11460 Chapter 01  An Introduction to Business Statistics 63. Inclusion of redundant independent variables in the regression model increases the
problem of __________.
A. autocorrelation
B. independence
C. Multicollinearity
D. outliers 64. ___________ is an iterative variable selection procedure that allows an independent
variable to be added to a multiple regression model in one iteration and deleted during the
next iteration?
A. Logistic regression
B. Stepwise regression
C. Quadratic regression
D. Backward regression 65. Stepwise regression uses a series of _____ tests during each iteration in order to determine
which independent variables should be brought into the regression model.
A. C
B. F
C. t
D. VIF 66. When using a multiple linear regression model, data transformations on the dependent
variable may be used in order to correct a violation of a(n) __________ of the multiple linear
regression model.
A. assumption
B. independent variables
C. autocorrelation
D. normality 11461 Chapter 01  An Introduction to Business Statistics 67. The _____________ statistic is used to test for autocorrelation among the residuals.
A. Cook's distance
B. C
C. DurbinWatson
D. R2 68. When the assumption of __________  residuals (error terms) is violated, the DurbinWatson statistic is used to test to determine if there is significant _____________ among the
residuals.
A. normality, probability
B. independent, probability
C. independent, autocorrelation
D. normality, autocorrelation 69. If the absolute value of the ______________ is greater than t.005, then there is strong
evidence that the observation is an outlier with respect to its y value.
A. Cook's distance
B. C statistic
C. F
D. studentized deleted residual 70. In a regression model a(n) ______ is an observation that is well separated from the rest of
the data with respect to its y value and/or its x values.
A. Outlier
B. Influential
C. Independent
D. Dependent 71. In converting the residual to a studentized (standardized) residual for a given observation,
the residual for the observation is divided by the residual's __________.
A. sample size
B. t value
C. standard error
D. square 11462 Chapter 01  An Introduction to Business Statistics 72. If successive values of the residuals are close together, then there is a(n) ___________
autocorrelation, and the value of the DurbinWatson statistic is _________.
A. negative, large
B. positive, small
C. negative, small
D. positive, large 73. If a multiple regression model has a(n) _____ statistic substantially greater than k + 1,
then it can be shown that this model has substantial bias and is undesirable.
A. C
B. F
C. Cook's distance
D. DurbinWatson 74. In general, a multiple regression model is considered to be desirable if the value of the C
statistic is small and the value of C is less than _____.
A. k
B. k  1
C. k + 1
D. n  k 11463 Chapter 01  An Introduction to Business Statistics
Essay Questions 75. Below is a partial multiple regression computer output based on a quadratic regression
model. Determine the number of observations in the sample, explained variation and the MSE. 76. Below is a partial multiple regression computer output based on a quadratic regression
model. Calculate R2. 11464 Chapter 01  An Introduction to Business Statistics 77. Below is a partial multiple regression computer output based on a quadratic regression
model. Test the overall usefulness of the model at α = .05. Calculate the F statistic and make your
decision. 78. Below is a partial multiple regression computer output based on a quadratic regression
model. Assuming a quadratic model was used, write the least squares prediction equation. 11465 Chapter 01  An Introduction to Business Statistics 79. Below is a partial multiple regression computer output based on a quadratic regression
model. Test the usefulness of the variable X2 in the model at = .05. 80. Below is a partial multiple regression computer output based on a quadratic regression
model to predict student enrollment at a local university. The dependent variable is the annual
enrollment given in thousands of students, the independent variable X is the increase in tuition
stated in thousands of dollars per year, and X2 is the square of tuition increase given in
squared thousands of dollars per year.
Interp...
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This document was uploaded on 01/20/2014.
 Winter '14

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