Question 1

Outliers can affect the slope of regression coefficients.

Accepted Answer

A) True 
 B)False  True

Question 2

It is common to compare β coefficients across different models.

Accepted Answer

A) True 
 B)False  False

Question 3

If a nominal variable has five categories, an analyst would include up to four dummy variables in a regression model.

Accepted Answer

A) True 
 B)False  True

Question 4

The error term plot shows the relationship between the predicted dependent variable and the error term.

Accepted Answer

A) True 
 B)False

Question 5

The lack of a pattern in the error term plot that is distributed around (0,0) indicates that the net effect of all variables excluded from the model on the dependent variable is zero.

Accepted Answer

A) True 
 B)False

Question 6

A nomothetic mode of explanation isolates the most important factors.

Accepted Answer

A) True 
 B)False

Question 7

The regression coefficient of a dummy variable is interpreted as the effect of that variable on the dependent variable, controlled for all other variables in the model.

Accepted Answer

A) True 
 B)False

Question 8

The effect of omitting a relevant variable is to inflate the value of variables that are included.

Accepted Answer

A) True 
 B)False

Question 9

Full model specification means that all variables are measured that affect the dependent variable.

Accepted Answer

A) True 
 B)False

Question 10

Standardized coefficients enable analysts to draw inferences about the relative impact of different independent variables on the dependent variable.

Accepted Answer

A) True 
 B)False

Question 11

When two variables are strongly correlated with each other, they are also multicollinear.

Accepted Answer

A) True 
 B)False

Question 12

Each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effect of all of the other independent variables included in the regression.

Accepted Answer

A) True 
 B)False

Question 13

In multiple regression, the adjusted R2 controls for the number of dependent variables.

Accepted Answer

A) True 
 B)False

Question 14

The search for parsimonious explanations often leads analysts to first identify different categories of factors that most affect their dependent variable.

Accepted Answer

A) True 
 B)False

Question 15

It is okay to include irrelevant variables as long as they are significant.

Accepted Answer

A) True 
 B)False

Question 16

Curvelinearity is indicated by residuals that are linearly related to each other.

Accepted Answer

A) True 
 B)False

Question 17

The global F-test examines the overall effect of all independent variables jointly on the dependent variable.

Accepted Answer

A) True 
 B)False

Question 18

Multiple regression is one of the most widely used multivariate statistical techniques for analyzing three or more variables.

Accepted Answer

A) True 
 B)False

Question 19

Heteroscedasticity is addressed by transforming both the dependent and the independent variables.

Accepted Answer

A) True 
 B)False

Question 20

Heteroscedasticity occurs when one of the dependent variables is linearly related to the independent variable.

Accepted Answer

A) True 
 B)False

Outliers can affect the slope of regression coefficients.

It is common to compare β coefficients across different models.

If a nominal variable has five categories, an analyst would include up to four dummy variables in a regression model.

The error term plot shows the relationship between the predicted dependent variable and the error term.

The lack of a pattern in the error term plot that is distributed around (0,0) indicates that the net effect of all variables excluded from the model on the dependent variable is zero.

A nomothetic mode of explanation isolates the most important factors.

The regression coefficient of a dummy variable is interpreted as the effect of that variable on the dependent variable, controlled for all other variables in the model.

The effect of omitting a relevant variable is to inflate the value of variables that are included.

Full model specification means that all variables are measured that affect the dependent variable.

Standardized coefficients enable analysts to draw inferences about the relative impact of different independent variables on the dependent variable.

When two variables are strongly correlated with each other, they are also multicollinear.

Each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effect of all of the other independent variables included in the regression.

In multiple regression, the adjusted R² controls for the number of dependent variables.

The search for parsimonious explanations often leads analysts to first identify different categories of factors that most affect their dependent variable.

It is okay to include irrelevant variables as long as they are significant.

Curvelinearity is indicated by residuals that are linearly related to each other.

The global F-test examines the overall effect of all independent variables jointly on the dependent variable.

Multiple regression is one of the most widely used multivariate statistical techniques for analyzing three or more variables.

Heteroscedasticity is addressed by transforming both the dependent and the independent variables.

Heteroscedasticity occurs when one of the dependent variables is linearly related to the independent variable.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Introducing Inference: Estimation From Samples

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Simple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

Exam 15: Multiple Regression

Outliers can affect the slope of regression coefficients.

It is common to compare β coefficients across different models.

If a nominal variable has five categories, an analyst would include up to four dummy variables in a regression model.

The error term plot shows the relationship between the predicted dependent variable and the error term.

The lack of a pattern in the error term plot that is distributed around (0,0) indicates that the net effect of all variables excluded from the model on the dependent variable is zero.

A nomothetic mode of explanation isolates the most important factors.

The regression coefficient of a dummy variable is interpreted as the effect of that variable on the dependent variable, controlled for all other variables in the model.

The effect of omitting a relevant variable is to inflate the value of variables that are included.

Full model specification means that all variables are measured that affect the dependent variable.

Standardized coefficients enable analysts to draw inferences about the relative impact of different independent variables on the dependent variable.

When two variables are strongly correlated with each other, they are also multicollinear.

Each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effect of all of the other independent variables included in the regression.

In multiple regression, the adjusted R2 controls for the number of dependent variables.

The search for parsimonious explanations often leads analysts to first identify different categories of factors that most affect their dependent variable.

It is okay to include irrelevant variables as long as they are significant.

Curvelinearity is indicated by residuals that are linearly related to each other.

The global F-test examines the overall effect of all independent variables jointly on the dependent variable.

Multiple regression is one of the most widely used multivariate statistical techniques for analyzing three or more variables.

Heteroscedasticity is addressed by transforming both the dependent and the independent variables.

Heteroscedasticity occurs when one of the dependent variables is linearly related to the independent variable.

Why Statistics for Public Managers and Policy Analysts

Research Design

Conceptualization and Measurement

Measuring and Managing Performance: Present and Future

Data Collection

Central Tendency

Measures of Dispersion

Contingency Tables

Getting Results

Introducing Inference: Estimation From Samples

Hypothesis Testing With Chi-Square

The T-Test

Analysis of Variance Anova

Simple Regression

Logistic and Time Series Regression

Survey of Other Techniques

Filters

In multiple regression, the adjusted R² controls for the number of dependent variables.