Question 1

Which of the following statements is true of confidence intervals?

Accepted Answer

A) Confidence intervals in a CLM are also referred to as point estimates. 
B) Confidence intervals in a CLM provide a range of likely values for the population parameter. 
C) Confidence intervals in a CLM do not depend on the degrees of freedom of a distribution. 
D) Confidence intervals in a CLM can be truly estimated when heteroskedasticity is present. 
A) Confidence intervals in a CLM are also referred to as point estimates. 
B) Confidence intervals in a CLM provide a range of likely values for the population parameter. 
C) Confidence intervals in a CLM do not depend on the degrees of freedom of a distribution. 
D) Confidence intervals in a CLM can be truly estimated when heteroskedasticity is present.

Question 2

Which of the following correctly identifies a reason why some authors prefer to report the standard errors rather than the t statistic?

Accepted Answer

A) Having standard errors makes it easier to compute confidence intervals. 
B) Standard errors are always positive. 
C) The F statistic can be reported just by looking at the standard errors. 
D) Standard errors can be used directly to test multiple linear regressions. 
A) Having standard errors makes it easier to compute confidence intervals. 
B) Standard errors are always positive. 
C) The F statistic can be reported just by looking at the standard errors. 
D) Standard errors can be used directly to test multiple linear regressions.

Question 3

Which of the following 95% confidence intervals for the estimate of the population treatment effect is the most convincing evidence that a policy intervention was indeed effective?

Accepted Answer

A) (-0.5, 0.9). 
B) (-0.1, 0.5). 
C) (0.5, 0.75).​ 
D) (-0.2, 0.3). 
A) (-0.5, 0.9). 
B) (-0.1, 0.5). 
C) (0.5, 0.75).​ 
D) (-0.2, 0.3).

Question 4

The general t statistic can be written as:

Accepted Answer

A) t =   
B) t =   
C) t =   
D) t =   
A) t =   
B) t =   
C) t =   
D) t =

Question 5

A normal variable is standardized by:

Accepted Answer

A) subtracting off its mean from it and multiplying by its standard deviation. 
B) adding its mean to it and multiplying by its standard deviation. 
C) subtracting off its mean from it and dividing by its standard deviation. 
D) adding its mean to it and dividing by its standard deviation. 
A) subtracting off its mean from it and multiplying by its standard deviation. 
B) adding its mean to it and multiplying by its standard deviation. 
C) subtracting off its mean from it and dividing by its standard deviation. 
D) adding its mean to it and dividing by its standard deviation.

Question 6

If   and   are estimated values of regression coefficients associated with two explanatory variables in a regression equation, then the standard error (   ) = standard error (   ) - standard error (   ).

Accepted Answer

A) True 
 B)False

Question 7

Which of the following is true of standard error?

Accepted Answer

A) It can take negative values. 
B) ​It is an estimate of the standard deviation. 
C) ​It is the square root of the variance. 
D) It complicates the computation of confidence intervals. 
A) It can take negative values. 
B) ​It is an estimate of the standard deviation. 
C) ​It is the square root of the variance. 
D) It complicates the computation of confidence intervals.

Question 8

Standard errors must always be positive.

Accepted Answer

A) True 
 B)False

Which of the following statements is true of confidence intervals?

Which of the following correctly identifies a reason why some authors prefer to report the standard errors rather than the t statistic?

Which of the following 95% confidence intervals for the estimate of the population treatment effect is the most convincing evidence that a policy intervention was indeed effective?

The general t statistic can be written as:

A normal variable is standardized by:

If and are estimated values of regression coefficients associated with two explanatory variables in a regression equation, then the standard error ( ) = standard error ( ) - standard error ( ).

Which of the following is true of standard error?

Standard errors must always be positive.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 4: Multiple Regression Analysis Inference

Which of the following statements is true of confidence intervals?

Which of the following correctly identifies a reason why some authors prefer to report the standard errors rather than the t statistic?

Which of the following 95% confidence intervals for the estimate of the population treatment effect is the most convincing evidence that a policy intervention was indeed effective?

The general t statistic can be written as:

A normal variable is standardized by:

If and are estimated values of regression coefficients associated with two explanatory variables in a regression equation, then the standard error ( ) = standard error ( ) - standard error ( ).

Which of the following is true of standard error?

Standard errors must always be positive.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters