Question 1

The appropriate hypothesis test for an ANOVA test is:

Accepted Answer

A) 
$H _ { 0 } : \text { all } B \neq 0 , H _ { \alpha } : \text { at least one } B = 0$ 
B) 
$H _ { 0 } \text { : all } B = 0 , H _ { \alpha } \text { : at least one } B \neq 0$ 
C) 
$H _ { 0 } \text { : at least on } B \neq 0 , H _ { a } : \text { all } B = 0$ 
D) 
$H _ { 0 } \text { : at least one } B = 0 , H _ { \alpha } \text { : all } B \neq 0$ 
A) 
$H _ { 0 } : \text { all } B \neq 0 , H _ { \alpha } : \text { at least one } B = 0$ 
B) 
$H _ { 0 } \text { : all } B = 0 , H _ { \alpha } \text { : at least one } B \neq 0$ 
C) 
$H _ { 0 } \text { : at least on } B \neq 0 , H _ { a } : \text { all } B = 0$ 
D) 
$H _ { 0 } \text { : at least one } B = 0 , H _ { \alpha } \text { : all } B \neq 0$ B

Question 2

In multiple regressions,a large value of the test statistic F indicates that most of the variation in Y is unexplained by the regression equation and that the model is useless.A small value of F indicates that most of the variation in Y is explained by the regression equation and that the model is useful.

Accepted Answer

A) True 
 B)False  False

Question 3

The error term represents the vertical distance from any point to the

Accepted Answer

A)  estimated regression line 
B)  population regression line 
C)  value of the Y's 
D)  mean value of the X's 
A)  estimated regression line 
B)  population regression line 
C)  value of the Y's 
D)  mean value of the X's B

Question 4

Heteroscedasticity means that the variability of Y values is larger for some X values
than for others.

Accepted Answer

A) True 
 B)False

Question 5

A multiple regression model involves 40 observations and 4 explanatory variables produces SST = 1000 and SSR = 804.The value of MSE is 5.6.

Accepted Answer

A) True 
 B)False

Question 6

Suppose you forecast the values of all of the independent variables and insert them into a multiple regression equation and obtain a point prediction for the dependent variable.You could then use the standard error of the estimate to obtain an approximate

Accepted Answer

A)  confidence interval 
B)  prediction interval 
C)  hypothesis test 
D)  independence test 
A)  confidence interval 
B)  prediction interval 
C)  hypothesis test 
D)  independence test

Question 7

The can be used to test for autocorrelation.

Accepted Answer

A)  regression coefficient 
B)  correlation coefficient 
C)  Durbin-Watson statistic 
D)  F-test or t-test 
A)  regression coefficient 
B)  correlation coefficient 
C)  Durbin-Watson statistic 
D)  F-test or t-test

Question 8

In time series data,errors are often not probabilistically independent.

Accepted Answer

A) True 
 B)False

Question 9

Time series data often exhibits which of the following characteristics?

Accepted Answer

A)  homoscedasticity 
B)  heteroscedasticity 
C)  autocorrelation 
D)  multicollinearity 
A)  homoscedasticity 
B)  heteroscedasticity 
C)  autocorrelation 
D)  multicollinearity

Question 10

Which of the following is true regarding regression error,e

Accepted Answer

A)  it is the same as a residual 
B)  it can be calculated from the observed data 
C)  it cannot be calculated from the observed data 
D)  it is unbiased 
A)  it is the same as a residual 
B)  it can be calculated from the observed data 
C)  it cannot be calculated from the observed data 
D)  it is unbiased

Question 11

A researcher can check whether the errors are normally distributed by using:

Accepted Answer

A)  a t-test or an F-test 
B)  the Durbin-Watson statistic 
C)  a frequency distribution or the value of the regression coefficient 
D)  a histogram or a Q-Q plot 
A)  a t-test or an F-test 
B)  the Durbin-Watson statistic 
C)  a frequency distribution or the value of the regression coefficient 
D)  a histogram or a Q-Q plot

Question 12

If you can determine that the outlier is not really a member of the relevant population,then it is appropriate and probably best to:

Accepted Answer

A)  average it 
B)  reduce it 
C)  delete it 
D)  leave it 
A)  average it 
B)  reduce it 
C)  delete it 
D)  leave it

Question 13

Which of the following would be considered a definition of an outlier?

Accepted Answer

A)  An extreme value for one or more variables 
B)  A value whose residual is abnormally large in magnitude 
C)  Values for individual explanatory variables that fall outside the general pattern of the other observations 
D)  All of these options 
A)  An extreme value for one or more variables 
B)  A value whose residual is abnormally large in magnitude 
C)  Values for individual explanatory variables that fall outside the general pattern of the other observations 
D)  All of these options

Question 14

Which of the following definitions best describes parsimony?

Accepted Answer

A)  Explaining the most with the least 
B)  Explaining the least with the most 
C)  Being able to explain all of the change in the response variable 
D)  Being able to predict the value of the response variable far into the future 
A)  Explaining the most with the least 
B)  Explaining the least with the most 
C)  Being able to explain all of the change in the response variable 
D)  Being able to predict the value of the response variable far into the future

Question 15

Forward regression:

Accepted Answer

A)  begins with all potential explanatory variables in the equation and deletes them one at a time until further deletion would do more harm than good. 
B)  adds and deletes variables until an optimal equation is achieved. 
C)  begins with no explanatory variables in the equation and successively adds one at a time until no remaining variables make a significant contribution. 
D)  randomly selects the optimal number of explanatory variables to be used 
A)  begins with all potential explanatory variables in the equation and deletes them one at a time until further deletion would do more harm than good. 
B)  adds and deletes variables until an optimal equation is achieved. 
C)  begins with no explanatory variables in the equation and successively adds one at a time until no remaining variables make a significant contribution. 
D)  randomly selects the optimal number of explanatory variables to be used

Question 16

In a multiple regression problem involving 30 observations and four explanatory variables,SST = 800 and SSE = 240.The value of the F-statistic for testing the significance of this model is 14.583.

Accepted Answer

A) True 
 B)False

Question 17

Which of the following is not one of the assumptions of regression?

Accepted Answer

A)  There is a population regression line 
B)  The response variable is not normally distributed 
C)  The response variable is normally distributed 
D)  The errors are probabilistically independent
The manager of a commuter rail transportation system was recently asked by his governing board to predict the demand for rides in the large city served by the transportation network.The system manager has collected data on variables thought to be related to the number of weekly riders on the city's rail system.The table shown below contains these data.
\(\begin{array}{cccccc}
\text { Year } & \text { Weekly Riders } & \text { Price perRide } & \text { Population } & \text { Income } & \text { Parking Rate } \
1 & 1200 & \$ 0.15 & 1800 & \$ 2,900 & \$ 0.50 \
2 & 1190 & \$ 0.15 & 1790 & \$ 3,100 & \$ 0.50 \
3 & 1195 & \$ 0.15 & 1780 & \$ 3,200 & \$ 0.60 \
4 & 1100 & \$ 0.25 & 1778 & \$ 3,250 & \$ 0.60 \
5 & 1105 & \$ 0.25 & 1750 & \$ 3,275 & \$ 0.60 \
6 & 1115 & \$ 0.25 & 1740 & \$ 3,290 & \$ 0.70 \
7 & 1130 & \$ 0.25 & 1725 & \$ 4,100 & \$ 0.75 \
8 & 1095 & \$ 0.30 & 1725 & \$ 4,300 & \$ 0.75 \
9 & 1090 & \$ 0.30 & 1720 & \$ 4,400 & \$ 0.75 \
10 & 1087 & \$ 0.30 & 1705 & \$ 4,600 & \$ 0.80\
11 & 1080 & \$ 0.30 & 1710 & \$ 4,815 & \$ 0.80 \
12 & 1020 & \$ 0.40 & 1700 & \$ 5,285 & \$ 0.80 \
13 & 1010 & \$ 0.40 & 1695 & \$ 5,665 & \$ 0.85 \
14 & 1010 & \$ 0.40 & 1695 & \$ 5,800 & \$ 1.00 \
15 & 1005 & \$ 0.40 & 1690 & \$ 5,900 & \$ 1.05 \
16 & 995 & \$ 0.40 & 1630 & \$ 5,915 & \$ 1.05 \
17 & 930 & \$ 0.75 & 1640 & \$ 6,325 & \$ 1.05 \
18 & 915 & \$ 0.75 & 1635 & \$ 6,500 & \$ 1.10 \
19 & 920 & \$ 0.75 & 1630 & \$ 6,612 & \$ 1.25 \
20 & 940 & \$ 0.75 & 1620 & \$ 6,883 & \$ 1.30\
21 & 950 & \$ 0.75 & 1615 & \$ 7,005 & \$ 1.50 \
22 & 910 & \$ 1.00 & 1605 & \$ 7,234 & \$ 1.55 \
23 & 930 & \$ 1.00 & 1590 & \$ 7,500 & \$ 1.65 \
24 & 933 & \$ 1.00 & 1595 & \$ 7,600 & \$ 1.75 \
25 & 940 & \$ 1.00 & 1590 & \$ 7,800 & \$ 1.75 \
26 & 948 & \$ 1.00 & 1600 & \$ 8,000 & \$ 1.90 \
27 & 955 & \$ 1,00 & 1610 & \$ 8,100 & \$ 2.00\
\end{array}\)
The variables "weekly riders" and "population" are measured in thousands,and the variables "price per ride","income",and "parking rate" are measured in dollars. 
A)  There is a population regression line 
B)  The response variable is not normally distributed 
C)  The response variable is normally distributed 
D)  The errors are probabilistically independent
The manager of a commuter rail transportation system was recently asked by his governing board to predict the demand for rides in the large city served by the transportation network.The system manager has collected data on variables thought to be related to the number of weekly riders on the city's rail system.The table shown below contains these data.
\(\begin{array}{cccccc}
\text { Year } & \text { Weekly Riders } & \text { Price perRide } & \text { Population } & \text { Income } & \text { Parking Rate } \
1 & 1200 & \$ 0.15 & 1800 & \$ 2,900 & \$ 0.50 \
2 & 1190 & \$ 0.15 & 1790 & \$ 3,100 & \$ 0.50 \
3 & 1195 & \$ 0.15 & 1780 & \$ 3,200 & \$ 0.60 \
4 & 1100 & \$ 0.25 & 1778 & \$ 3,250 & \$ 0.60 \
5 & 1105 & \$ 0.25 & 1750 & \$ 3,275 & \$ 0.60 \
6 & 1115 & \$ 0.25 & 1740 & \$ 3,290 & \$ 0.70 \
7 & 1130 & \$ 0.25 & 1725 & \$ 4,100 & \$ 0.75 \
8 & 1095 & \$ 0.30 & 1725 & \$ 4,300 & \$ 0.75 \
9 & 1090 & \$ 0.30 & 1720 & \$ 4,400 & \$ 0.75 \
10 & 1087 & \$ 0.30 & 1705 & \$ 4,600 & \$ 0.80\
11 & 1080 & \$ 0.30 & 1710 & \$ 4,815 & \$ 0.80 \
12 & 1020 & \$ 0.40 & 1700 & \$ 5,285 & \$ 0.80 \
13 & 1010 & \$ 0.40 & 1695 & \$ 5,665 & \$ 0.85 \
14 & 1010 & \$ 0.40 & 1695 & \$ 5,800 & \$ 1.00 \
15 & 1005 & \$ 0.40 & 1690 & \$ 5,900 & \$ 1.05 \
16 & 995 & \$ 0.40 & 1630 & \$ 5,915 & \$ 1.05 \
17 & 930 & \$ 0.75 & 1640 & \$ 6,325 & \$ 1.05 \
18 & 915 & \$ 0.75 & 1635 & \$ 6,500 & \$ 1.10 \
19 & 920 & \$ 0.75 & 1630 & \$ 6,612 & \$ 1.25 \
20 & 940 & \$ 0.75 & 1620 & \$ 6,883 & \$ 1.30\
21 & 950 & \$ 0.75 & 1615 & \$ 7,005 & \$ 1.50 \
22 & 910 & \$ 1.00 & 1605 & \$ 7,234 & \$ 1.55 \
23 & 930 & \$ 1.00 & 1590 & \$ 7,500 & \$ 1.65 \
24 & 933 & \$ 1.00 & 1595 & \$ 7,600 & \$ 1.75 \
25 & 940 & \$ 1.00 & 1590 & \$ 7,800 & \$ 1.75 \
26 & 948 & \$ 1.00 & 1600 & \$ 8,000 & \$ 1.90 \
27 & 955 & \$ 1,00 & 1610 & \$ 8,100 & \$ 2.00\
\end{array}\)
The variables "weekly riders" and "population" are measured in thousands,and the variables "price per ride","income",and "parking rate" are measured in dollars.

Question 18

Another term for constant error variance is:

Accepted Answer

A)  homoscedasticity 
B)  heteroscedasticity 
C)  autocorrelation 
D)  multicollinearity 
A)  homoscedasticity 
B)  heteroscedasticity 
C)  autocorrelation 
D)  multicollinearity

Question 19

In regression analysis,the total variation in the dependent variable Y,measured by $\sum \left( y _ { i } - \bar { y } \right) ^ { 2 }$ 
and referred to as SST,can be decomposed into two parts: the explained variation,measured by SSR,and the unexplained variation,measured by SSE.

Accepted Answer

A) True 
 B)False

Question 20

The ANOVA table splits the total variation into two parts.They are the

Accepted Answer

A)  acceptable and unacceptable variation 
B)  adequate and inadequate variation 
C)  resolved and unresolved variation 
D)  explained and unexplained variation 
A)  acceptable and unacceptable variation 
B)  adequate and inadequate variation 
C)  resolved and unresolved variation 
D)  explained and unexplained variation

The appropriate hypothesis test for an ANOVA test is:

The error term represents the vertical distance from any point to the

Heteroscedasticity means that the variability of Y values is larger for some X values than for others.

A multiple regression model involves 40 observations and 4 explanatory variables produces SST = 1000 and SSR = 804.The value of MSE is 5.6.

Suppose you forecast the values of all of the independent variables and insert them into a multiple regression equation and obtain a point prediction for the dependent variable.You could then use the standard error of the estimate to obtain an approximate

The can be used to test for autocorrelation.

In time series data,errors are often not probabilistically independent.

Time series data often exhibits which of the following characteristics?

Which of the following is true regarding regression error,e

A researcher can check whether the errors are normally distributed by using:

If you can determine that the outlier is not really a member of the relevant population,then it is appropriate and probably best to:

Which of the following would be considered a definition of an outlier?

Which of the following definitions best describes parsimony?

Forward regression:

In a multiple regression problem involving 30 observations and four explanatory variables,SST = 800 and SSE = 240.The value of the F-statistic for testing the significance of this model is 14.583.

Which of the following is not one of the assumptions of regression?

Another term for constant error variance is:

In regression analysis,the total variation in the dependent variable Y,measured by $\sum \left( y _ { i } - \bar { y } \right) ^ { 2 }$ and referred to as SST,can be decomposed into two parts: the explained variation,measured by SSR,and the unexplained variation,measured by SSE.

The ANOVA table splits the total variation into two parts.They are the

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

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Exam 11: Regression Analysis: Statistical Inference

The appropriate hypothesis test for an ANOVA test is:

The error term represents the vertical distance from any point to the

Heteroscedasticity means that the variability of Y values is larger for some X values than for others.

A multiple regression model involves 40 observations and 4 explanatory variables produces SST = 1000 and SSR = 804.The value of MSE is 5.6.

Suppose you forecast the values of all of the independent variables and insert them into a multiple regression equation and obtain a point prediction for the dependent variable.You could then use the standard error of the estimate to obtain an approximate

The can be used to test for autocorrelation.

In time series data,errors are often not probabilistically independent.

Time series data often exhibits which of the following characteristics?

Which of the following is true regarding regression error,e

A researcher can check whether the errors are normally distributed by using:

If you can determine that the outlier is not really a member of the relevant population,then it is appropriate and probably best to:

Which of the following would be considered a definition of an outlier?

Which of the following definitions best describes parsimony?

Forward regression:

In a multiple regression problem involving 30 observations and four explanatory variables,SST = 800 and SSE = 240.The value of the F-statistic for testing the significance of this model is 14.583.

Which of the following is not one of the assumptions of regression?

Another term for constant error variance is:

The ANOVA table splits the total variation into two parts.They are the

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters