Question 1

If the significance level for the F-test is high enough, there is a relationship between the dependent and independent variables.

Accepted Answer

A) True 
 B)False

Question 2

Which of the following statements provides the best guidance for model building?

Accepted Answer

A) If the value of r2 increases as more variables are added to the model, the variables should remain in the model, regardless of the magnitude of increase. 
B) If the value of the adjusted r2 increases as more variables are added to the model, the variables should remain in the model. 
C) If the value of r2 increases as more variables are added to the model, the variables should not remain in the model, regardless of the magnitude of the increase. 
D) If the value of the adjusted r2 increases as more variables are added to the model, the variables should not remain in the model. 
A) If the value of r2 increases as more variables are added to the model, the variables should remain in the model, regardless of the magnitude of increase. 
B) If the value of the adjusted r2 increases as more variables are added to the model, the variables should remain in the model. 
C) If the value of r2 increases as more variables are added to the model, the variables should not remain in the model, regardless of the magnitude of the increase. 
D) If the value of the adjusted r2 increases as more variables are added to the model, the variables should not remain in the model.

Question 3

The number of dummy variables must equal 1 less than the number of categories of the qualitative variable.

Accepted Answer

A) True 
 B)False

Question 4

Which of the following represents the underlying linear model for hypothesis testing?

Accepted Answer

A) Y = b0 + b1 X + ε 
B) Y = b0 + b1 X 
C) Y = β0 + β1 X + ε 
D) Y = β0 + β1 X 
A) Y = b0 + b1 X + ε 
B) Y = b0 + b1 X 
C) Y = β0 + β1 X + ε 
D) Y = β0 + β1 X

Question 5

In regression, a binary variable is also called an indicator variable.

Accepted Answer

A) True 
 B)False

Question 6

In regression, a dependent variable is sometimes called a predictor variable.

Accepted Answer

A) True 
 B)False

Question 7

What is the difference between simple linear regression models and multiple regression models?

Accepted Answer

Multiple regression

Question 8

Table 4.1
SUMMARY OUTPUT
   ANOVA
    
-Consider the output shown in Table 4.1.Which of the predictors is not significant?

Accepted Answer

A) Intercept 
B) X1 
C) X2 
D) X3 
A) Intercept 
B) X1 
C) X2 
D) X3

Question 9

Which of these represents the sample intercept?

Accepted Answer

A) b0 
B) β1 
C) β0 
D) b1 
A) b0 
B) β1 
C) β0 
D) b1

Question 10

An air conditioning and heating repair firm conducted a study to determine if the average outside temperature could be used to predict the cost of an electric bill for homes during the winter months in Houston, Texas.
Y = 227.19 - 1.45X, where Y = monthly cost, X = average outside air temperature
(a)If the temperature averaged 48 degrees during December, what is the forecasted cost of December's electric bill?
(b)If the temperature averaged 38 degrees during January, what is the forecasted cost of January's electric bill?

Accepted Answer

(a)$227.19 - $1.45(4

Question 11

Which of these represents a predicted value?

Accepted Answer

A) b0 
B) Ŷ 
C) b1 
D) X1 
A) b0 
B) Ŷ 
C) b1 
D) X1

Question 12

The null hypothesis in the F-test is that there is a linear relationship between the X and Y variables.

Accepted Answer

A) True 
 B)False

Question 13

The coefficient of determination resulting from a particular regression analysis was 0.85.What was the slope of the regression line?

Accepted Answer

A) 0.85 
B) -0.85 
C) 0.922 
D) There is insufficient information to answer the question. 
A) 0.85 
B) -0.85 
C) 0.922 
D) There is insufficient information to answer the question.

Question 14

Table 4.1
SUMMARY OUTPUT
   ANOVA
    
-Consider the output shown in Table 4.1.The Mean Square value is missing.What is its actual value?

Accepted Answer

A) 31683 
B) 10561 
C) 3520 
D) 660 
A) 31683 
B) 10561 
C) 3520 
D) 660

Question 15

Dummy variables for regression analysis can take on a value of either -1 or +1.

Accepted Answer

A) True 
 B)False

Question 16

List the four assumptions of the regression model.

Accepted Answer

(1)The errors are independent.

Question 17

Which of the following statements is not true about regression models?

Accepted Answer

A) Estimates of the slope are found from sample data. 
B) The regression line minimizes the sum of the squared errors. 
C) The error is found by subtracting the actual data value from the predicted data value. 
D) The dependent variable is the explanatory variable. 
A) Estimates of the slope are found from sample data. 
B) The regression line minimizes the sum of the squared errors. 
C) The error is found by subtracting the actual data value from the predicted data value. 
D) The dependent variable is the explanatory variable.

Question 18

As more variables are added to the model, what happens to the r2 value?

Accepted Answer

It usually

Question 19

An electronics company is looking to develop a regression model to predict the number of units sold for a special running watch.Data is provided below:
   (a)Define the dummy variable(s)for the regression model.
(b)What is the correlation between the Holiday categorical variable and sales?

Accepted Answer

(a)X₃= 1 if it is a h

Question 20

Which of these statements is best?

Accepted Answer

A) The SST measures the total variability in the dependent variable about the regression line. 
B) The SSE measures the total variability in the independent variable about the regression line. 
C) The SSR indicates how much of the total variability in the dependent variable is explained by the regression model. 
D) The coefficient of determination takes on values between -1 and + 1. 
A) The SST measures the total variability in the dependent variable about the regression line. 
B) The SSE measures the total variability in the independent variable about the regression line. 
C) The SSR indicates how much of the total variability in the dependent variable is explained by the regression model. 
D) The coefficient of determination takes on values between -1 and + 1.

Exam 4: Regression Models

If the significance level for the F-test is high enough, there is a relationship between the dependent and independent variables.

Which of the following statements provides the best guidance for model building?

The number of dummy variables must equal 1 less than the number of categories of the qualitative variable.

Which of the following represents the underlying linear model for hypothesis testing?

In regression, a binary variable is also called an indicator variable.

In regression, a dependent variable is sometimes called a predictor variable.

What is the difference between simple linear regression models and multiple regression models?

Table 4.1 SUMMARY OUTPUT ANOVA -Consider the output shown in Table 4.1.Which of the predictors is not significant?

Which of these represents the sample intercept?

Which of these represents a predicted value?

The null hypothesis in the F-test is that there is a linear relationship between the X and Y variables.

The coefficient of determination resulting from a particular regression analysis was 0.85.What was the slope of the regression line?

Table 4.1 SUMMARY OUTPUT ANOVA -Consider the output shown in Table 4.1.The Mean Square value is missing.What is its actual value?

Dummy variables for regression analysis can take on a value of either -1 or +1.

List the four assumptions of the regression model.

Which of the following statements is not true about regression models?

As more variables are added to the model, what happens to the r2 value?

An electronics company is looking to develop a regression model to predict the number of units sold for a special running watch.Data is provided below: (a)Define the dummy variable(s)for the regression model. (b)What is the correlation between the Holiday categorical variable and sales?

Which of these statements is best?

Introduction to Quantitative Analysis

Probability Concepts and Applications

Decision Analysis

Forecasting

Inventory Control Models

Linear Programming Models: Graphical and Computer Methods

Linear Programming Applications

Transportation, Assignment, and Network Models

Integer Programming, Goal Programming, and Nonlinear Programming

Project Management

Waiting Lines and Queuing Theory Models

Simulation Modeling

Markov Analysis

Statistical Quality Control

Analytic Hierarchy Process

Dynamic Programming

Decision Theory and the Normal Distribution

Game Theory

Mathematical Tools: Determinants and Matrices

Calculus-Based Optimization

Linear Programming: The Simplex Method

Transportation, Assignment, and Network Algorithms

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As more variables are added to the model, what happens to the r² value?