Question 1

Accepted Answer

A) True 
 B)False

Question 2

SCENARIO 15-6  
-True or False: Referring to Scenario 15-6, the variable   
should be dropped to remove
collinearity?

Accepted Answer

A) True 
 B)False

Question 3

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?

Accepted Answer

A)  Randomness of error terms 
B)  Collinearity 
C)  Normality of residuals 
D)  Missing observations 
A)  Randomness of error terms 
B)  Collinearity 
C)  Normality of residuals 
D)  Missing observations

Question 4

Accepted Answer

A) True 
 B)False

Question 5

SCENARIO 15-1  
-Referring to Scenario 15-1, what is the correct interpretation of the coefficient of multiple
Determination?

Accepted Answer

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. 
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.

Question 6

Accepted Answer

A) True 
 B)False

Question 7

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

Accepted Answer

A) True 
 B)False

Question 8

Accepted Answer

A) True 
 B)False

Question 9

SCENARIO 15-1  
-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)?

Accepted Answer

A)  0.0001 
B)  0.0006 
C)  0.3647 
D)  None of the above. 
A)  0.0001 
B)  0.0006 
C)  0.3647 
D)  None of the above.

Question 10

SCENARIO 15-6  
-True or False: Referring to Scenario 15-6, the variable   
should be dropped to remove
collinearity?

Accepted Answer

A) True 
 B)False

Question 11

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.

Accepted Answer

A) True 
 B)False

Question 12

SCENARIO 15-2  
-

Accepted Answer

Question 13

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

Accepted Answer

A) True 
 B)False

Question 14

The Cp statistic is used

Accepted Answer

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. 
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.

Question 15

The logarithm transformation can be used

Accepted Answer

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. 
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.

Question 16

SCENARIO 15-4   
15-16 Multiple Regression Model Building  
-

Accepted Answer

Question 17

Accepted Answer

A) True 
 B)False

Question 18

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.

Accepted Answer

A) True 
 B)False

Question 19

SCENARIO 15-1  
-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).

Accepted Answer

A) True 
 B)False

Question 20

SCENARIO 15-6  
-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.

Accepted Answer

A) True 
 B)False

SCENARIO 15-6 -True or False: Referring to Scenario 15-6, the variable should be dropped to remove collinearity?

SCENARIO 15-1 -Referring to Scenario 15-1, what is the correct interpretation of the coefficient of multiple Determination?

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

SCENARIO 15-1 -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)?

SCENARIO 15-6 -True or False: Referring to Scenario 15-6, the variable should be dropped to remove collinearity?

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.

SCENARIO 15-2 -

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

The Cp statistic is used

The logarithm transformation can be used

SCENARIO 15-4 15-16 Multiple Regression Model Building -

SCENARIO 15-1 -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).

SCENARIO 15-6 -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.

Defining and Collecting Data

Organizing and Visualizing

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

Conthe Tinuonormausl Disdis Tributionstribution and Other

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Analysis of Variance

Chi-Square and Nonparametric

Simple Linear Regression

Introduction to Multiple

Time-Series Forecasting

Business Analytics

A Roadmap for Analyzing Data

Statistical Applications in Quality Management

Decision Making

Filters

Exam 15: Multiple Regression

SCENARIO 15-6 -True or False: Referring to Scenario 15-6, the variable should be dropped to remove collinearity?

SCENARIO 15-1 -Referring to Scenario 15-1, what is the correct interpretation of the coefficient of multiple Determination?

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

SCENARIO 15-1 -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)?

SCENARIO 15-6 -True or False: Referring to Scenario 15-6, the variable should be dropped to remove collinearity?

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.

SCENARIO 15-2 -

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

The Cp statistic is used

The logarithm transformation can be used

SCENARIO 15-4 15-16 Multiple Regression Model Building -

SCENARIO 15-1 -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).

SCENARIO 15-6 -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.

Defining and Collecting Data

Organizing and Visualizing

Numerical Descriptive Measures

Basic Probability

Discrete Probability Distributions

Conthe Tinuonormausl Disdis Tributionstribution and Other

Sampling Distributions

Confidence Interval Estimation

Fundamentals of Hypothesis Testing: One-Sample Tests

Two-Sample Tests

Analysis of Variance

Chi-Square and Nonparametric

Simple Linear Regression

Introduction to Multiple

Time-Series Forecasting

Business Analytics

A Roadmap for Analyzing Data

Statistical Applications in Quality Management

Decision Making

Filters