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 options 
A) A quadratic regression equation 
B) A logarithmic regression equation 
C) Constant elasticity equation 
D) The learning curve model 
E) All of these options

Question 2

(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 adjusted   for the model in (A).

Accepted Answer

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

Question 3

In multiple regression,the coefficients reflect the expected change in:

Accepted Answer

A) Y when the associated X value increases by one unit 
B) X when the associated Y value increases by one unit 
C) Y when the associated X value decreases by one unit 
D) X when the associated Y value decreases by one unit 
A) Y when the associated X value increases by one unit 
B) X when the associated Y value increases by one unit 
C) Y when the associated X value decreases by one unit 
D) X when the associated Y value decreases by one unit

Question 4

Regression analysis asks:

Accepted Answer

A) if there are differences between distinct populations 
B) if the sample is representative of the population 
C) how a single variable depends on other relevant variables 
D) how several variables depend on each other 
A) if there are differences between distinct populations 
B) if the sample is representative of the population 
C) how a single variable depends on other relevant variables 
D) how several variables depend on each other

Question 5

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

Accepted Answer

A) True 
 B)False

Question 6

(A)Estimate a simple linear regression model with Service Interval (X)and Maintenance Cost (Y).Interpret the slope coefficient of the least squares line as well as the computed value of   .
(B)Do you think this model proves the agency's point about maintenance? Explain your answer.
(C)Obtain a residual plot vs.Service Interval.Does this affect your opinion of the validity of the model in (A)?
(D)Obtain a scatterplot of Maintenance Cost vs.Service Interval.Does this affect your opinion of the validity of the model in (A)?
(E)Use what you have learned about transformations to fit an alternative model to the one in (A).
(F)Interpret the model you developed in (E).Does it help you assess the agency's claim? What should the agency conclude about the relationship between service interval and maintenance costs?

Accepted Answer

(A) @#IMG-DLM& (A)As the service interval increase

Question 7

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

Accepted Answer

A) True 
 B)False

Question 8

Correlation is a summary measure that indicates:

Accepted Answer

A) a curved relationship among the variables 
B) the rate of change in Y for a one unit change in X 
C) the strength of the linear relationship between pairs of variables 
D) the magnitude of difference between two variables 
A) a curved relationship among the variables 
B) the rate of change in Y for a one unit change in X 
C) the strength of the linear relationship between pairs of variables 
D) the magnitude of difference between two variables

Question 9

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 10

In linear regression,the fitted value is the:

Accepted Answer

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

Question 11

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

Accepted Answer

A) True 
 B)False

Question 12

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 13

(A)Estimate a simple linear regression model involving shipping cost and package weight.Interpret the slope coefficient of the least squares line as well as the computed value of   .
(B)Add another explanatory variable - distance shipped - to the regression to (A).Estimate and interpret this expanded model.How does the   value for this multiple regression model compare to that of the simple regression model estimated in (A)? Explain any difference between the two   values.Compute and interpret the adjusted   value for the revised model.
(C)Suppose that one of the managers of this express delivery service company is trying to decide whether to add an interaction term involving the package weight   and the distance shipped   in the multiple regression model developed previously.Why would the manager want to add such a term to the regression equation?
(D)Estimate the revised model using the interaction term suggested in (C).
(E)Interpret each of the estimated coefficients in your revised model in (D).In particular,how do you interpret the coefficient for the interaction term in the revised model?
(F)Does this revised model in (D)fit the given data better than the original multiple regression model in (B)? Explain why or why not.

Accepted Answer

(A) @#IMG-DLM& As the package weight increases by

Question 14

(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)Would any of the variables in this model be considered a dummy variable? Explain your answer.
(D)Identify and interpret the coefficient of determination (   )and the standard error of the estimate (se)for the model in (A).
(E)Use the estimated model in (A)to predict the sales price of a 2500 square feet,15-year old house that has 5 rooms and an attached garage.

Accepted Answer

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

Question 15

In a simple linear regression problem,if the percentage of variation explained   is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

Accepted Answer

A) True 
 B)False

Question 16

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 17

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

Accepted Answer

A) True 
 B)False

Question 18

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 the above 
A) 20% 
B) 80% 
C) 25% 
D) 50% 
E) None of the above

Question 19

The least squares line is the line that minimizes the sum of the residuals.

Accepted Answer

A) True 
 B)False

Question 20

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line:   = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

Accepted Answer

A) True 
 B)False

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

In multiple regression,the coefficients reflect the expected change in:

Regression analysis asks:

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

Correlation is a summary measure that indicates:

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.

In linear regression,the fitted value is the:

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

In linear regression,a dummy variable is used:

In a simple linear regression problem,if the percentage of variation explained is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

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

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

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 least squares line is the line that minimizes the sum of the residuals.

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

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: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

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Exam 10: Regression Analysis: Estimating Relationships

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

In multiple regression,the coefficients reflect the expected change in:

Regression analysis asks:

If the regression equation includes anything other than a constant plus the sum of products of constants and variables,the model will not be linear

In a simple regression with a single explanatory variable,the multiple R is the same as the standard correlation between the Y variable and the explanatory X variable.

Correlation is a summary measure that indicates:

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.

In linear regression,the fitted value is the:

The primary purpose of a nonlinear transformation is to "straighten out" the data on a scatterplot

In linear regression,a dummy variable is used:

In a simple linear regression problem,if the percentage of variation explained is 0.95,this means that 95% of the variation in the explanatory variable X can be explained by regression.

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

Correlation is used to determine the strength of the linear relationship between an explanatory variable X and response variable Y.

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 least squares line is the line that minimizes the sum of the residuals.

A regression analysis between sales (in $1000)and advertising (in $100)resulted in the following least squares line: = 84 +7X.This implies that if advertising is $800,then the predicted amount of sales (in dollars)is $140,000.

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: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters