Question 1

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 2

In regression analysis,the ANOVA table analyzes:

Accepted Answer

A) the variation of the response variable Y 
B) the variation of the explanatory variable X 
C) the total variation of all variables 
D) All of these options 
A) the variation of the response variable Y 
B) the variation of the explanatory variable X 
C) the total variation of all variables 
D) All of these options

Question 3

(A)Summarize the findings of the stepwise regression method using this cutoff value.
(B)When the cutoff value was increased to 0.10,the output below was the result.The table at top left represents the change when the disposable income variable is added to the model and the table at top right represents the average price variable being added.The regression model with both added variables is shown in the bottom table.Summarize the results for this model.
   (C)Which model would you recommend using? Why?

Accepted Answer

(A)When a cutoff value of 0.05 is used,t

Question 4

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

Accepted Answer

A) True 
 B)False

Question 5

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

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 t-value for testing   is calculated using which of the following equations:

Accepted Answer

A) n - k - 1 
B)    
C)    
D)    
A) n - k - 1 
B)    
C)    
D)

Question 8

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

Question 9

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 10

(A)Estimate the regression model.How well does this model fit the given data?
(B)Is there a linear relationship between the two explanatory variables and the dependent variable at the 5% significance level? Explain how you arrived at your answer.
(C)Use the estimated regression model to predict the annual maintenance expense of a truck that is driven 14,000 miles per year and is 5 years old.
(D)Find a 95% prediction interval for the maintenance expense determined in (C).Use a t-multiple = 2.
(E)Find a 95% confidence interval for the maintenance expense for all trucks sharing the characteristics provided in Question 96.Use a t-multiple = 2.
(F)How do you explain the differences between the widths of the intervals in (D)and (E)?

Accepted Answer

(A) @#IMG-DLM& = -680.70 + 0.080 @#IMG-DLM& + 44.238 @#IMG-DLM& ; this

Question 11

In regression analysis,extrapolation is performed when you:

Accepted Answer

A) attempt to predict beyond the limits of the sample 
B) have to estimate some of the explanatory variable values 
C) have to use a lag variable as an explanatory variable in the model 
D) don't have observations for every period in the sample 
A) attempt to predict beyond the limits of the sample 
B) have to estimate some of the explanatory variable values 
C) have to use a lag variable as an explanatory variable in the model 
D) don't have observations for every period in the sample

Question 12

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 13

In multiple regression with k explanatory variables,the t-tests of the individual coefficients allows us to determine whether   (for i = 1,2,….,k),which tells us whether a linear relationship exists between   and Y.

Accepted Answer

A) True 
 B)False

Question 14

Do you see any problems evident in the plot below of residuals versus fitted values from a multiple regression analysis? Explain your answer.

Accepted Answer

There are a few points with la

Question 15

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

Accepted Answer

A) "Wrong" values for the coefficients for the left and right foot size 
B) Large p-values for the coefficients for the left and right foot size 
C) Small t-values for the coefficients for the left and right foot size 
D) All of these options 
A) "Wrong" values for the coefficients for the left and right foot size 
B) Large p-values for the coefficients for the left and right foot size 
C) Small t-values for the coefficients for the left and right foot size 
D) All of these options

Question 16

If exact multicollinearlity exists,that means that there is redundancy in the data.

Accepted Answer

A) True 
 B)False

Question 17

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

Accepted Answer

A) True 
 B)False

Question 18

In a simple linear regression model,testing whether the slope   of the population regression line could be zero is the same as testing whether or not the linear relationship between the response variable Y and the explanatory variable X is significant.

Accepted Answer

A) True 
 B)False

Question 19

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

Accepted Answer

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

Question 20

(A)Determine the least-squares multiple regression equation.
(B)Interpret the Y- intercept of the regression equation.
(C)Interpret the partial regression coefficients.
(D)What is the estimated number of new visitors to a club if the size of the ad is 6 column-inches and a $100 discount is offered?
(E)Determine the approximate 95% prediction interval for the number of new visitors to a given club when the ad is 5 column-inches and the discount is $80.
(F)What is the value for the percentage of variation explained,and exactly what does it indicate?
(G)At the 0.05 level,is the overall regression equation in (A)significant?
(H)Use the 0.05 level in concluding whether each partial regression coefficient differs significantly from zero.
(I Interpret the results of the preceding tests in (H)and (I)in the context of the two explanatory variables described in the problem.
(J)Construct a 95% confidence interval for each partial regression coefficient in the population regression equation.

Accepted Answer

(A)Visitors = 10.687 + 2.1569 @#IMG-DLM& Ad Size +

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

In regression analysis,the ANOVA table analyzes:

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

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 t-value for testing is calculated using which of the following equations:

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

Which of the following is true regarding regression error,e

In regression analysis,extrapolation is performed when you:

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

In multiple regression with k explanatory variables,the t-tests of the individual coefficients allows us to determine whether (for i = 1,2,….,k),which tells us whether a linear relationship exists between and Y.

Do you see any problems evident in the plot below of residuals versus fitted values from a multiple regression analysis? Explain your answer.

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

If exact multicollinearlity exists,that means that there is redundancy in the data.

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

In a simple linear regression model,testing whether the slope of the population regression line could be zero is the same as testing whether or not the linear relationship between the response variable Y and the explanatory variable X is significant.

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters

Exam 11: Regression Analysis: Statistical Inference

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

In regression analysis,the ANOVA table analyzes:

Suppose that one equation has 3 explanatory variables and an F-ratio of 49.Another equation has 5 explanatory variables and an F-ratio of 38.The first equation will always be considered a better model.

A confidence interval constructed around a point prediction from a regression model is called a prediction interval,because the actual point being estimated is not a population parameter

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 t-value for testing is calculated using which of the following equations:

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

Which of the following is true regarding regression error,e

In regression analysis,extrapolation is performed when you:

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

In multiple regression with k explanatory variables,the t-tests of the individual coefficients allows us to determine whether (for i = 1,2,….,k),which tells us whether a linear relationship exists between and Y.

Do you see any problems evident in the plot below of residuals versus fitted values from a multiple regression analysis? Explain your answer.

Suppose you run a regression of a person's height on his/her right and left foot sizes,and you suspect that there may be multicollinearity between the foot sizes.What types of problems might you see if your suspicions are true?

If exact multicollinearlity exists,that means that there is redundancy in the data.

In a multiple regression analysis involving 4 explanatory variables and 40 data points,the degrees of freedom associated with the sum of squared errors,SSE,is 35.

In a simple linear regression model,testing whether the slope of the population regression line could be zero is the same as testing whether or not the linear relationship between the response variable Y and the explanatory variable X is significant.

A scatterplot that exhibits a "fan" shape (the variation of Y increases as X increases)is an example of:

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters