Question 1

What is the purpose of regression analysis?

Accepted Answer

Regression analysis is a tool

Question 2

Parsimony is where ________.

Accepted Answer

A) the model is kept as simple as possible 
B) having as many variables as possible for accuracy 
C) multiple models answer questions 
D) none of the above 
A) the model is kept as simple as possible 
B) having as many variables as possible for accuracy 
C) multiple models answer questions 
D) none of the above

Question 3

Stepwise regression carried out by deleting variables currently in the model and adding extra variables if none meet the criteria for removal is called ________.

Accepted Answer

A) forward selection 
B) parsimony 
C) general stepwise 
D) backward elimination 
A) forward selection 
B) parsimony 
C) general stepwise 
D) backward elimination

Question 4

In a simple regression model Y = ß0 + ß1X + ε,ß0 represents the ________.

Accepted Answer

A) slope of the regression line 
B) intercept 
C) error term 
D) mean value of X 
A) slope of the regression line 
B) intercept 
C) error term 
D) mean value of X

Question 5

Specific risk associated with a single stock is ________.

Accepted Answer

A) variation in stock price explained by the market 
B) variation in stock price due to factors such as earnings potential 
C) characterized by a measure called beta 
D) measured by the slope of the regression line 
A) variation in stock price explained by the market 
B) variation in stock price due to factors such as earnings potential 
C) characterized by a measure called beta 
D) measured by the slope of the regression line

Question 6

The best-fitting regression line minimizes the sum of the squares of the observed errors.

Accepted Answer

A) True 
 B)False

Question 7

A regression model displays multicollinearity when ________.

Accepted Answer

A) the correlation coefficient for dependent variables is 0.7 
B) the variance inflation factor is equal to -1. 
C) the correlation coefficient for independent variables is 1 
D) the variance inflation factor is less than 3 
A) the correlation coefficient for dependent variables is 0.7 
B) the variance inflation factor is equal to -1. 
C) the correlation coefficient for independent variables is 1 
D) the variance inflation factor is less than 3

Question 8

The best-subsets regression method ________.

Accepted Answer

A) evaluates best subsets of models for a fixed number of dependent variables 
B) adds one variable at a time to the multiple regression model and tests for significance 
C) evaluates all possible regression models for a set of independent variables 
D) removes one variable at a time from the regression model 
A) evaluates best subsets of models for a fixed number of dependent variables 
B) adds one variable at a time to the multiple regression model and tests for significance 
C) evaluates all possible regression models for a set of independent variables 
D) removes one variable at a time from the regression model

Question 9

When the variance of the mean value is smaller than the variance of individual values,________.

Accepted Answer

A) the p-value is equal to the Significance F 
B) the data set contains an outlier 
C) the Adjusted R Square is equal to 1 
D) the prediction interval is wider than the confidence interval 
A) the p-value is equal to the Significance F 
B) the data set contains an outlier 
C) the Adjusted R Square is equal to 1 
D) the prediction interval is wider than the confidence interval

Question 10

For simple linear regression,________ is the regression statistic and it is also known as the sample correlation coefficient.

Accepted Answer

A) standard error 
B) adjusted R2 
C) R2 
D) multiple R 
A) standard error 
B) adjusted R2 
C) R2 
D) multiple R

Question 11

For the least-squares equation   = 3,698 + 2,538X,Y represents house prices and X represents number of rooms.Which of the following statements is true?

Accepted Answer

A) Only 36.98% of the variation in house prices can be explained by the number of rooms in the house. 
B) For every additional room that is added in a house,house prices increase by $2,538. 
C) As the number of rooms in the house increase,prices fall by $3,698. 
D) 25.38% of the house price is attributed to the number of rooms. 
A) Only 36.98% of the variation in house prices can be explained by the number of rooms in the house. 
B) For every additional room that is added in a house,house prices increase by $2,538. 
C) As the number of rooms in the house increase,prices fall by $3,698. 
D) 25.38% of the house price is attributed to the number of rooms.

Question 12

Confidence intervals provide information about the unknown values of the true regression coefficients,accounting for sampling error.

Accepted Answer

A) True 
 B)False

Question 13

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

Accepted Answer

A) Y = β0 + β1X2 + ε 
B) Y = β0 + β1X + β2P + β3C + ε 
C) Y = β0 + β1X + ε 
D) Y = β0 + β1X + (β2)2P + ε 
A) Y = β0 + β1X2 + ε 
B) Y = β0 + β1X + β2P + β3C + ε 
C) Y = β0 + β1X + ε 
D) Y = β0 + β1X + (β2)2P + ε

Question 14

For simple linear regression,the p-value associated with the test for the slope coefficient ________.

Accepted Answer

A) is usually a value greater than 1 
B) will be equal to the Significance F value 
C) measures the fit of the regression line to the data 
D) explains the variation of the dependent variable around the mean 
A) is usually a value greater than 1 
B) will be equal to the Significance F value 
C) measures the fit of the regression line to the data 
D) explains the variation of the dependent variable around the mean

Question 15

What does the principle of parsimony suggest?

Accepted Answer

A) A simple model with fewer independent variables may not produce an effective result. 
B) The fewest number of explanatory variables that explain the independent variable need to be included in the model. 
C) Good regression models are often based on sound technical analysis. 
D) To avoid the problem of multicollinearity,the number of independent variables need to be sufficiently high. 
A) A simple model with fewer independent variables may not produce an effective result. 
B) The fewest number of explanatory variables that explain the independent variable need to be included in the model. 
C) Good regression models are often based on sound technical analysis. 
D) To avoid the problem of multicollinearity,the number of independent variables need to be sufficiently high.

Question 16

ANOVA is applied to regression to ________.

Accepted Answer

A) check for correlation between the error terms 
B) check if the differences between sample means is due to error 
C) determine the value of the intercept term 
D) test for the significance of regression 
A) check for correlation between the error terms 
B) check if the differences between sample means is due to error 
C) determine the value of the intercept term 
D) test for the significance of regression

Question 17

When analyzing systematic risk of a stock,if the slope of the regression line is greater than 1,________.

Accepted Answer

A) the stock return exceeds market return 
B) the market return and stock return move equally 
C) the stock return increases at a slower rate than market 
D) the stock return and market return are uncorrelated 
A) the stock return exceeds market return 
B) the market return and stock return move equally 
C) the stock return increases at a slower rate than market 
D) the stock return and market return are uncorrelated

Question 18

Systematic risk associated with a single stock is defined as the variation in the stock price explained by ________.

Accepted Answer

A) market movements 
B) the firm's earnings potential 
C) acquisition strategies 
D) net losses 
A) market movements 
B) the firm's earnings potential 
C) acquisition strategies 
D) net losses

Question 19

If the residuals for a regression model exhibit a parabolic shape,this would violate the assumption of ________.

Accepted Answer

A) homoscedasticity 
B) linearity 
C) autocorrelation 
D) normality of errors 
A) homoscedasticity 
B) linearity 
C) autocorrelation 
D) normality of errors

Question 20

Categorical variables are included in regression analysis by ________.

Accepted Answer

A) removing the error term from the equation 
B) applying the principle of parsimony 
C) using dummy variables 
D) using an intercept term 
A) removing the error term from the equation 
B) applying the principle of parsimony 
C) using dummy variables 
D) using an intercept term

What is the purpose of regression analysis?

Parsimony is where ________.

Stepwise regression carried out by deleting variables currently in the model and adding extra variables if none meet the criteria for removal is called ________.

In a simple regression model Y = ß₀+ ß₁X + ε,ß₀represents the ________.

Specific risk associated with a single stock is ________.

The best-fitting regression line minimizes the sum of the squares of the observed errors.

A regression model displays multicollinearity when ________.

The best-subsets regression method ________.

When the variance of the mean value is smaller than the variance of individual values,________.

For simple linear regression,________ is the regression statistic and it is also known as the sample correlation coefficient.

For the least-squares equation = 3,698 + 2,538X,Y represents house prices and X represents number of rooms.Which of the following statements is true?

Confidence intervals provide information about the unknown values of the true regression coefficients,accounting for sampling error.

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

For simple linear regression,the p-value associated with the test for the slope coefficient ________.

What does the principle of parsimony suggest?

ANOVA is applied to regression to ________.

When analyzing systematic risk of a stock,if the slope of the regression line is greater than 1,________.

Systematic risk associated with a single stock is defined as the variation in the stock price explained by ________.

If the residuals for a regression model exhibit a parabolic shape,this would violate the assumption of ________.

Categorical variables are included in regression analysis by ________.

Data and Business Decisions

Descriptive Statistics and Data Analysis

Probability Concepts and Distributions

Sampling and Estimation

Hypothesis Testing and Statistical Inference

Forecasting

Introduction to Statistical Quality Control

Building and Using Decision Models

Decision Models With Uncertainty and Risk

Decisions, uncertainty, and Risk

Queues and Process Simulation Modeling

Linear Optimization

Integer, nonlinear, and Advanced Optimization Methods

Filters

Exam 6: Regression Analysis

What is the purpose of regression analysis?

Parsimony is where ________.

Stepwise regression carried out by deleting variables currently in the model and adding extra variables if none meet the criteria for removal is called ________.

In a simple regression model Y = ß0 + ß1X + ε,ß0 represents the ________.

Specific risk associated with a single stock is ________.

The best-fitting regression line minimizes the sum of the squares of the observed errors.

A regression model displays multicollinearity when ________.

The best-subsets regression method ________.

When the variance of the mean value is smaller than the variance of individual values,________.

For simple linear regression,________ is the regression statistic and it is also known as the sample correlation coefficient.

For the least-squares equation = 3,698 + 2,538X,Y represents house prices and X represents number of rooms.Which of the following statements is true?

Confidence intervals provide information about the unknown values of the true regression coefficients,accounting for sampling error.

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

For simple linear regression,the p-value associated with the test for the slope coefficient ________.

What does the principle of parsimony suggest?

ANOVA is applied to regression to ________.

When analyzing systematic risk of a stock,if the slope of the regression line is greater than 1,________.

Systematic risk associated with a single stock is defined as the variation in the stock price explained by ________.

If the residuals for a regression model exhibit a parabolic shape,this would violate the assumption of ________.

Categorical variables are included in regression analysis by ________.

Data and Business Decisions

Descriptive Statistics and Data Analysis

Probability Concepts and Distributions

Sampling and Estimation

Hypothesis Testing and Statistical Inference

Forecasting

Introduction to Statistical Quality Control

Building and Using Decision Models

Decision Models With Uncertainty and Risk

Decisions, uncertainty, and Risk

Queues and Process Simulation Modeling

Linear Optimization

Integer, nonlinear, and Advanced Optimization Methods

Filters

In a simple regression model Y = ß₀+ ß₁X + ε,ß₀represents the ________.