Question 1

Why should we not include irrelevant variables in our regression analysis?

Accepted Answer

A)  Your R-squared will become too high 
B)  Because of data limitations 
C)  It is bad academic fashion not to base your variables on sound theory 
D)  We increase the risk of producing false significant results 
A)  Your R-squared will become too high 
B)  Because of data limitations 
C)  It is bad academic fashion not to base your variables on sound theory 
D)  We increase the risk of producing false significant results D

Question 2

What does heteroskedasticity mean?

Accepted Answer

A)  The variance in the residuals is the same regardless of their predicted values 
B)  There is variance in the residuals 
C)  We are unable to produce residuals 
D)  The variance in the residuals differ depending on their predicted values 
A)  The variance in the residuals is the same regardless of their predicted values 
B)  There is variance in the residuals 
C)  We are unable to produce residuals 
D)  The variance in the residuals differ depending on their predicted values D

Question 3

What is the best way to find the exact top or bottom point of a squared effect?

Accepted Answer

A)  Through derivation using values from the two coefficients 
B)  Excluding the squared term and predicting 
C)  Including the squared term and predicting 
D)  Graphing the results and comparing the top/bottom point with the value on the X-axis 
A)  Through derivation using values from the two coefficients 
B)  Excluding the squared term and predicting 
C)  Including the squared term and predicting 
D)  Graphing the results and comparing the top/bottom point with the value on the X-axis A

Question 4

How can we deal with the breach of the assumption about linearity?

Accepted Answer

A)  Include a squared term 
B)  Include an interaction term 
C)  Use robust regression 
D)  Use the margins command 
A)  Include a squared term 
B)  Include an interaction term 
C)  Use robust regression 
D)  Use the margins command

Question 5

What are the two ways we can check for heteroskedasticity?

Accepted Answer

A)  We can examine a plot of predicted values vs the residuals 
B)  We can run the Hausman test 
C)  We can run the hettest command 
D)  We can compare the F-test of two models 
A)  We can examine a plot of predicted values vs the residuals 
B)  We can run the Hausman test 
C)  We can run the hettest command 
D)  We can compare the F-test of two models

Question 6

Which one is not a measure of influential (or potentially influential) observations?

Accepted Answer

A)  Leverage 
B)  Cook-Weisberg 
C)  DFBETA 
D)  Cook's distance 
A)  Leverage 
B)  Cook-Weisberg 
C)  DFBETA 
D)  Cook's distance

Question 7

Name another way of modelling nonlinearity

Accepted Answer

A)  Using the linktest command 
B)  Using interaction term 
C)  Using dummy variables 
D)  Using a bivariate regression model 
A)  Using the linktest command 
B)  Using interaction term 
C)  Using dummy variables 
D)  Using a bivariate regression model

Question 8

Which statistic(s) can help us detect multicollinearity?

Accepted Answer

A)  Variance inflation factor (VIF) 
B)  F-statistic 
C)  Durbin-Watson 
D)  Tolerance values (1/VIF) 
A)  Variance inflation factor (VIF) 
B)  F-statistic 
C)  Durbin-Watson 
D)  Tolerance values (1/VIF)

Question 9

Which one of these statements is not a Gauss-Markov assumption?

Accepted Answer

A)  The error term has a conditional mean of 0 
B)  Influential observations are absent 
C)  The error term has constant variance 
D)  The errors are uncorrelated 
A)  The error term has a conditional mean of 0 
B)  Influential observations are absent 
C)  The error term has constant variance 
D)  The errors are uncorrelated

Question 10

What does robust regression do?

Accepted Answer

A)  Performs an OLS regression with more trustworthy standard errors 
B)  It gives a weight to each unit based on their distance from the mean of Y 
C)  Performs three types of regression analysis and presents the mean results 
D)  It gives a weight to each unit based on their total influence on the model 
A)  Performs an OLS regression with more trustworthy standard errors 
B)  It gives a weight to each unit based on their distance from the mean of Y 
C)  Performs three types of regression analysis and presents the mean results 
D)  It gives a weight to each unit based on their total influence on the model

Why should we not include irrelevant variables in our regression analysis?

What does heteroskedasticity mean?

What is the best way to find the exact top or bottom point of a squared effect?

How can we deal with the breach of the assumption about linearity?

What are the two ways we can check for heteroskedasticity?

Which one is not a measure of influential (or potentially influential) observations?

Name another way of modelling nonlinearity

Which statistic(s) can help us detect multicollinearity?

Which one of these statements is not a Gauss-Markov assumption?

What does robust regression do?

Research and Statistics

Introduction to STATA

Simple Bivariate Regression

Multiple Regression

Dummy-Variable Regression

Interaction/Moderation Effects Using Regression

Logistic Regression

Survival Analysis

Multilevel Analysis

Panel Data Analysis

Time-Series Analysis

Exploratory Factor Analysis

Structural Equation Modelling and Confirmatory Factor Analysis

Advanced Statistical Techniques

Programming and Dynamic Reporting Using STATA

Filters

Exam 7: Linear Regression Assumptions and Diagnostics

Why should we not include irrelevant variables in our regression analysis?

What does heteroskedasticity mean?

What is the best way to find the exact top or bottom point of a squared effect?

How can we deal with the breach of the assumption about linearity?

What are the two ways we can check for heteroskedasticity?

Which one is not a measure of influential (or potentially influential) observations?

Name another way of modelling nonlinearity

Which statistic(s) can help us detect multicollinearity?

Which one of these statements is not a Gauss-Markov assumption?

What does robust regression do?

Research and Statistics

Introduction to STATA

Simple Bivariate Regression

Multiple Regression

Dummy-Variable Regression

Interaction/Moderation Effects Using Regression

Logistic Regression

Survival Analysis

Multilevel Analysis

Panel Data Analysis

Time-Series Analysis

Exploratory Factor Analysis

Structural Equation Modelling and Confirmatory Factor Analysis

Advanced Statistical Techniques

Programming and Dynamic Reporting Using STATA

Filters