Question 1

Which of the following is an example of a nonlinear regression model?

Accepted Answer

A)  a quadratic regression equation 
B)  a logarithmic regression equation 
C)  constant elasticity equation 
D)  the learning curve model 
E)  all of these choices 
A)  a quadratic regression equation 
B)  a logarithmic regression equation 
C)  constant elasticity equation 
D)  the learning curve model 
E)  all of these choices E

Question 2

In linear regression, the fitted value is:

Accepted Answer

A)  the predicted value of the dependent variable 
B)  the predicted value of the independent value 
C)  the predicted value of the slope 
D)  the predicted value of the intercept 
E)  none of these choices 
A)  the predicted value of the dependent variable 
B)  the predicted value of the independent value 
C)  the predicted value of the slope 
D)  the predicted value of the intercept 
E)  none of these choices A

Question 3

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable   on the response variable Y depends on the value of another explanatory variable   .

Accepted Answer

A) True 
 B)False  True

Question 4

In regression analysis, if there are several explanatory variables, it is called:

Accepted Answer

A)  simple regression 
B)  multiple regression 
C)  compound regression 
D)  composite regression 
A)  simple regression 
B)  multiple regression 
C)  compound regression 
D)  composite regression

Question 5

In regression analysis, which of the following causal relationships are possible?

Accepted Answer

A)  X causes Y to vary. 
B)  Y causes X to vary. 
C)  Other variables cause both X and Y to vary. 
D)  All of these options are possible. 
A)  X causes Y to vary. 
B)  Y causes X to vary. 
C)  Other variables cause both X and Y to vary. 
D)  All of these options are possible.

Question 6

The standard error of the estimate (   ) is essentially the

Accepted Answer

A)  mean of the residuals 
B)  standard deviation of the residuals 
C)  mean of the explanatory variable 
D)  standard deviation of the explanatory variable 
A)  mean of the residuals 
B)  standard deviation of the residuals 
C)  mean of the explanatory variable 
D)  standard deviation of the explanatory variable

Question 7

In every regression study there is a single variable that we are trying to explain or predict. This is called the response variable or dependent variable.

Accepted Answer

A) True 
 B)False

Question 8

In a simple linear regression analysis, the following sums of squares are produced:   The proportion of the variation in Y that is explained by the variation in X is:

Accepted Answer

A)  20% 
B)  80% 
C)  25% 
D)  50% 
E)  none of these choices 
A)  20% 
B)  80% 
C)  25% 
D)  50% 
E)  none of these choices

Question 9

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

Accepted Answer

A) True 
 B)False

Question 10

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

Accepted Answer

A)  the dependent variable will remain constant 
B)  the dependent variable will be allowed to vary 
C)  all of the other independent variables remain constant 
D)  all of the other independent variables be allowed to vary 
A)  the dependent variable will remain constant 
B)  the dependent variable will be allowed to vary 
C)  all of the other independent variables remain constant 
D)  all of the other independent variables be allowed to vary

Question 11

The R2 can only increase when extra explanatory variables are added to a multiple regression model.

Accepted Answer

A) True 
 B)False

Question 12

In regression analysis, the variables used to help explain or predict the response variable are called the:

Accepted Answer

A)  independent variables 
B)  dependent variables 
C)  regression variables 
D)  statistical variables 
A)  independent variables 
B)  dependent variables 
C)  regression variables 
D)  statistical variables

Question 13

In simple linear regression, the divisor of the standard error of estimate   is n - 1, simply because there is only one explanatory variable of interest.

Accepted Answer

A) True 
 B)False

Question 14

A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line:   = 84 +7X. This implies that if there is no advertising, then the predicted amount of sales (in dollars) is $84,000.

Accepted Answer

A) True 
 B)False

Question 15

Approximately what percentage of the observed Y values are within one standard error of the estimate (   ) of the corresponding fitted Y values?

Accepted Answer

A)  67% 
B)  95% 
C)  99% 
D)  It is not possible to determine this. 
A)  67% 
B)  95% 
C)  99% 
D)  It is not possible to determine this.

Question 16

Outliers are observations that:

Accepted Answer

A)  lie outside the sample 
B)  render the study useless 
C)  lie outside the typical pattern of points on a scatterplot 
D)  disrupt the entire linear trend 
A)  lie outside the sample 
B)  render the study useless 
C)  lie outside the typical pattern of points on a scatterplot 
D)  disrupt the entire linear trend

Question 17

In linear regression, a dummy variable is used:

Accepted Answer

A)  to represent residual variables 
B)  to represent missing data in each sample 
C)  to include hypothetical data in the regression equation 
D)  to include categorical variables in the regression equation 
E)  when "dumb" responses are included in the data 
A)  to represent residual variables 
B)  to represent missing data in each sample 
C)  to include hypothetical data in the regression equation 
D)  to include categorical variables in the regression equation 
E)  when "dumb" responses are included in the data

Question 18

A correlation value of zero indicates.

Accepted Answer

A)  a strong linear relationship 
B)  a weak linear relationship 
C)  no linear relationship 
D)  a perfect linear relationship 
A)  a strong linear relationship 
B)  a weak linear relationship 
C)  no linear relationship 
D)  a perfect linear relationship

Question 19

The station manager of a local television station is interested in predicting the amount of television (in hours) that people will watch in the viewing area. The explanatory variables are:   age (in years),   education (highest level obtained, in years) and   family size (number of family members in household). The multiple regression output is shown below:  
-(A) Use the information above to estimate the linear regression model.
(B) Interpret each of the estimated regression coefficients of the regression model in (A).
(C) Identify and interpret the coefficient of determination (   ) for the model in (A).
(D) Identify and interpret the standard error of the estimate   for the model in (A).

Accepted Answer

(A) @#IMG-DLM& (B) This model shows that the numbe

Question 20

The adjusted R2 adjusts R2 for:

Accepted Answer

A)  non-linearity 
B)  outliers 
C)  low correlation 
D)  the number of explanatory variables in a multiple regression model 
A)  non-linearity 
B)  outliers 
C)  low correlation 
D)  the number of explanatory variables in a multiple regression model

Which of the following is an example of a nonlinear regression model?

In linear regression, the fitted value is:

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable on the response variable Y depends on the value of another explanatory variable .

In regression analysis, if there are several explanatory variables, it is called:

In regression analysis, which of the following causal relationships are possible?

The standard error of the estimate ( ) is essentially the

In every regression study there is a single variable that we are trying to explain or predict. This is called the response variable or dependent variable.

In a simple linear regression analysis, the following sums of squares are produced: The proportion of the variation in Y that is explained by the variation in X is:

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

The R² can only increase when extra explanatory variables are added to a multiple regression model.

In regression analysis, the variables used to help explain or predict the response variable are called the:

In simple linear regression, the divisor of the standard error of estimate is n - 1, simply because there is only one explanatory variable of interest.

A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line: = 84 +7X. This implies that if there is no advertising, then the predicted amount of sales (in dollars) is $84,000.

Approximately what percentage of the observed Y values are within one standard error of the estimate ( ) of the corresponding fitted Y values?

Outliers are observations that:

In linear regression, a dummy variable is used:

A correlation value of zero indicates.

The adjusted R² adjusts R² for:

Introduction to Business Analytics

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: Statistical Inference

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

Exam 10: Regression Analysis: Estimating Relationships

Which of the following is an example of a nonlinear regression model?

In linear regression, the fitted value is:

We should include an interaction variable in a regression model if we believe that the effect of one explanatory variable on the response variable Y depends on the value of another explanatory variable .

In regression analysis, if there are several explanatory variables, it is called:

In regression analysis, which of the following causal relationships are possible?

The standard error of the estimate ( ) is essentially the

In every regression study there is a single variable that we are trying to explain or predict. This is called the response variable or dependent variable.

In a simple linear regression analysis, the following sums of squares are produced: The proportion of the variation in Y that is explained by the variation in X is:

The coefficients for logarithmically transformed explanatory variables should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable.

An important condition when interpreting the coefficient for a particular independent variable X in a multiple regression equation is that:

The R2 can only increase when extra explanatory variables are added to a multiple regression model.

In regression analysis, the variables used to help explain or predict the response variable are called the:

In simple linear regression, the divisor of the standard error of estimate is n - 1, simply because there is only one explanatory variable of interest.

A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line: = 84 +7X. This implies that if there is no advertising, then the predicted amount of sales (in dollars) is $84,000.

Approximately what percentage of the observed Y values are within one standard error of the estimate ( ) of the corresponding fitted Y values?

Outliers are observations that:

In linear regression, a dummy variable is used:

A correlation value of zero indicates.

The adjusted R2 adjusts R2 for:

Introduction to Business Analytics

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: Statistical Inference

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

The R² can only increase when extra explanatory variables are added to a multiple regression model.

The adjusted R² adjusts R² for: