Question 1

The specific risk associated with a stock is measured by ________.

Accepted Answer

A) the slope of the regression line 
B) the intercept of the regression equation 
C) the standard error of the estimate 
D) the sum of the squares of errors 
A) the slope of the regression line 
B) the intercept of the regression equation 
C) the standard error of the estimate 
D) the sum of the squares of errors C

Question 2

R2 is called the coefficient of determination.

Accepted Answer

A) True 
 B)False  True

Question 3

If the stock return increases at a slower rate than the market return,the slope of the regression line is ________.

Accepted Answer

A) greater than one 
B) equal to one 
C) lesser than one 
D) equal to zero 
A) greater than one 
B) equal to one 
C) lesser than one 
D) equal to zero C

Question 4

A high R Square value means that there is ________.

Accepted Answer

A) a strong relationship between the independent and dependent variables 
B) a weak relationship between the independent and dependent variables 
C) no relationship between the independent and dependent variables 
D) a curvilinear relationship between the independent and dependent variables 
A) a strong relationship between the independent and dependent variables 
B) a weak relationship between the independent and dependent variables 
C) no relationship between the independent and dependent variables 
D) a curvilinear relationship between the independent and dependent variables

Question 5

Best-subsets regression evaluates models using a statistic called ________.

Accepted Answer

A) Durbin-Watson 
B) degrees of freedom 
C) Bonferroni criterion 
D) sum of squares 
A) Durbin-Watson 
B) degrees of freedom 
C) Bonferroni criterion 
D) sum of squares

Question 6

In a simple regression model Y = ß0 + ß1X + ε,ε 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 7

Since regression analysis involves numerical data,it is not possible to use categorical data to build a regression model.

Accepted Answer

A) True 
 B)False

Question 8

What is the difference between a simple linear regression model and a multiple linear regression model?

Accepted Answer

A regression model that involv

Question 9

The best way to measure multicollinearity is using the ________.

Accepted Answer

A) factor of sums 
B) variance inflation factor 
C) standard deviation 
D) correlation matrix 
A) factor of sums 
B) variance inflation factor 
C) standard deviation 
D) correlation matrix

Question 10

If increasing the number of independent variables increases R2,why isn't it advised to simply increase the number of independent variables in a model to explain the variability in the dependent variable?

Accepted Answer

Good modelers try to have as simple a mo

Question 11

The assumption of homoscedasticity means that ________.

Accepted Answer

A) variation about the regression line is constant for all values of the independent variable 
B) the residuals should appear to be randomly scattered about zero,with no apparent pattern 
C) the errors for each individual value of X are normally distributed,with a mean of 0 
D) the residuals should be scattered in a parabolic shape about zero 
A) variation about the regression line is constant for all values of the independent variable 
B) the residuals should appear to be randomly scattered about zero,with no apparent pattern 
C) the errors for each individual value of X are normally distributed,with a mean of 0 
D) the residuals should be scattered in a parabolic shape about zero

Question 12

The Durbin-Watson statistic for a data set gives a value of 0.This means that ________.

Accepted Answer

A) the errors for each value of X are normally distributed 
B) the data exhibits autocorrelation 
C) a curvilinear regression model should be used for the data 
D) the data is heteroscedastic 
A) the errors for each value of X are normally distributed 
B) the data exhibits autocorrelation 
C) a curvilinear regression model should be used for the data 
D) the data is heteroscedastic

Question 13

A confidence interval for the independent variable X would specify ________.

Accepted Answer

A) the uncertainty about the mean value of the dependent variable 
B) the possible values of X that are uncorrelated with the dependent variable 
C) all the possible values of X 
D) the probability distribution for the various values of X 
A) the uncertainty about the mean value of the dependent variable 
B) the possible values of X that are uncorrelated with the dependent variable 
C) all the possible values of X 
D) the probability distribution for the various values of X

Question 14

If a variable is removed from the regression model when the t-statistic is greater than 1,________.

Accepted Answer

A) the standard error will decrease 
B) the p-value will decrease 
C) the Adjusted R2 square will decrease 
D) the Multiple R will decrease 
A) the standard error will decrease 
B) the p-value will decrease 
C) the Adjusted R2 square will decrease 
D) the Multiple R will decrease

Question 15

The variance inflation factor for each variable in a multiple regression analysis is listed below:   Given these VIFs what can you say about multicollinearity in a model that includes all three of these variables?

Accepted Answer

Conservative guidelines suggest that a m

Question 16

A prediction interval for the independent variable X would specify ________.

Accepted Answer

A) all the possible values of the dependent variable Y 
B) the probability distribution for the various values of X 
C) the uncertainty in the dependent variable for a single value of X 
D) all the possible values of X 
A) all the possible values of the dependent variable Y 
B) the probability distribution for the various values of X 
C) the uncertainty in the dependent variable for a single value of X 
D) all the possible values of X

Question 17

Models having a Bonferroni Criterion (Cp)less than 1 ________.

Accepted Answer

A) have a substantial bias. 
B) are good models to consider 
C) need to be recalculated. 
D) are underspecified models. 
A) have a substantial bias. 
B) are good models to consider 
C) need to be recalculated. 
D) are underspecified models.

Question 18

An independent variable should be removed to improve the regression model if ________.

Accepted Answer

A) the correlation coefficient is -1 
B) the R2 value is 1 
C) the t-statistic is lesser than 1 
D) the p-value is greater than 0.05 
A) the correlation coefficient is -1 
B) the R2 value is 1 
C) the t-statistic is lesser than 1 
D) the p-value is greater than 0.05

Question 19

________ indicates the strength of association between the dependent and independent variables in multiple regression.

Accepted Answer

A) Variance inflation factor 
B) R square 
C) Standard error 
D) ANOVA 
A) Variance inflation factor 
B) R square 
C) Standard error 
D) ANOVA

Question 20

A curvilinear regression model ________.

Accepted Answer

A) explains a linear relationship between variables 
B) is used when the residual plot shows a linear relationship 
C) is a model that is curvilinear in the parameters 
D) shows a curvilinear relationship between the variables 
A) explains a linear relationship between variables 
B) is used when the residual plot shows a linear relationship 
C) is a model that is curvilinear in the parameters 
D) shows a curvilinear relationship between the variables

The specific risk associated with a stock is measured by ________.

R² is called the coefficient of determination.

If the stock return increases at a slower rate than the market return,the slope of the regression line is ________.

A high R Square value means that there is ________.

Best-subsets regression evaluates models using a statistic called ________.

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

Since regression analysis involves numerical data,it is not possible to use categorical data to build a regression model.

What is the difference between a simple linear regression model and a multiple linear regression model?

The best way to measure multicollinearity is using the ________.

If increasing the number of independent variables increases R²,why isn't it advised to simply increase the number of independent variables in a model to explain the variability in the dependent variable?

The assumption of homoscedasticity means that ________.

The Durbin-Watson statistic for a data set gives a value of 0.This means that ________.

A confidence interval for the independent variable X would specify ________.

If a variable is removed from the regression model when the t-statistic is greater than 1,________.

The variance inflation factor for each variable in a multiple regression analysis is listed below: Given these VIFs what can you say about multicollinearity in a model that includes all three of these variables?

A prediction interval for the independent variable X would specify ________.

Models having a Bonferroni Criterion (Cp)less than 1 ________.

An independent variable should be removed to improve the regression model if ________.

________ indicates the strength of association between the dependent and independent variables in multiple regression.

A curvilinear regression model ________.

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

The specific risk associated with a stock is measured by ________.

R2 is called the coefficient of determination.

If the stock return increases at a slower rate than the market return,the slope of the regression line is ________.

A high R Square value means that there is ________.

Best-subsets regression evaluates models using a statistic called ________.

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

Since regression analysis involves numerical data,it is not possible to use categorical data to build a regression model.

What is the difference between a simple linear regression model and a multiple linear regression model?

The best way to measure multicollinearity is using the ________.

If increasing the number of independent variables increases R2,why isn't it advised to simply increase the number of independent variables in a model to explain the variability in the dependent variable?

The assumption of homoscedasticity means that ________.

The Durbin-Watson statistic for a data set gives a value of 0.This means that ________.

A confidence interval for the independent variable X would specify ________.

If a variable is removed from the regression model when the t-statistic is greater than 1,________.

The variance inflation factor for each variable in a multiple regression analysis is listed below: Given these VIFs what can you say about multicollinearity in a model that includes all three of these variables?

A prediction interval for the independent variable X would specify ________.

Models having a Bonferroni Criterion (Cp)less than 1 ________.

An independent variable should be removed to improve the regression model if ________.

________ indicates the strength of association between the dependent and independent variables in multiple regression.

A curvilinear regression model ________.

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

R² is called the coefficient of determination.

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

If increasing the number of independent variables increases R²,why isn't it advised to simply increase the number of independent variables in a model to explain the variability in the dependent variable?