[2008] Topic 9: Multiple Regression: Estimation and Hypothesis Testing 相关习

aianjie

 

AIM 2: Define and interpret the partial slope coefficient.

1、Which of the following statements regarding the results of a regression analysis is FALSE? The:

A) slope coefficient in a multiple regression is the value of the dependent variable for a given value of the independent variable.

B) slope coefficient in a multiple regression is the change in the dependent variable for a one-unit change in the independent variable, holding all other variables constant.

C) intercept is the value that the dependent variable takes on if all the independent variables had a value of zero.

D) slope coefficients in the multiple regression are referred to as partial betas.

aianjie

 

The correct answer is A

The slope coefficient is the change in the dependent variable for a one-unit change in the independent variable.

aianjie

 

2、When interpreting the results of a multiple regression analysis, which of the following terms represents the value of the dependent variable when the independent variables are all equal to zero?

A) Slope coefficient.

B) p-value.

C) t-value.

D) Intercept term.

aianjie

 

The correct answer is D

The intercept term is the value of the dependent variable when the independent variables are set to zero.

aianjie

 

AIM 3: List the assumptions of the multiple linear regression model.

1、Which of the following statements least accurately describes one of the fundamental multiple regression assumptions?

A) The error term is normally distributed.

B) The variance of the error terms is not constant (i.e., the errors are heteroskedastic).

C) The independent variables are not random.

D) There is no exact linear relationship between any two or more independent variables.

aianjie

 

The correct answer is B

The variance of the error term IS assumed to be constant, resulting in errors that are homoskedastic.

aianjie

 

2、One of the underlying assumptions of a multiple regression is that the variance of the residuals is constant for various levels of the independent variables. This quality is referred to as:

A) a normal distribution.

B) homoskedasticity.

C) a linear relationship.

D) serial correlation.

aianjie

 

The correct answer is B

Homoskedasticity refers to the basic assumption of a multiple regression model that the variance of the error terms is constant.

aianjie

 

AIM 4: Explain the concept of multicollinearity and implications it has on modeling.

1、Which of the following statements regarding multicollinearity is FALSE?

A) Multicollinearity may be a problem even if the Multicollinearity is not perfect.

B) Multicollinearity makes it difficult to determine the contribution to explanation of the dependent variable of an individual explanatory variable.

C) Multicollinearity may be present in any regression model.

D) If the t-statistics for the individual independent variables are insignificant, yet he F-statistic is significant, this indicates the presence of Multicollinearity.

aianjie

 

The correct answer is C

Multicollinearity is not an issue in simple regression.