Question 1

It is okay for independent variables not to be correlated with the dependent variables, as long as they are highly correlated with each other.

Accepted Answer

A) True 
 B)False

Question 2

Outliers are observations whose multiple regression residuals exceed three standard deviations.

Accepted Answer

A) True 
 B)False

Question 3

A dummy variable can have only two values.

Accepted Answer

A) True 
 B)False

Question 4

The error term accounts for all variables not specified in the model.

Accepted Answer

A) True 
 B)False

Question 5

Autocorrelation is common with time series data.

Accepted Answer

A) True 
 B)False

Question 6

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

Accepted Answer

A) True 
 B)False

Question 7

The assumption of full model specification is that variables not specified in the model are justifiably omitted only when their cumulative effect on the dependent variable is zero.

Accepted Answer

A) True 
 B)False

Question 8

Values of R2 adjusted below .20 are considered to suggest weak model fit, those between .20 and .40 indicate moderate fit, those above .40 indicate strong fit, and those above .65 indicate very strong model fit.

Accepted Answer

A) True 
 B)False

Question 9

Curvelinearity is addressed by transforming one of the independent variables.

Accepted Answer

A) True 
 B)False

It is okay for independent variables not to be correlated with the dependent variables, as long as they are highly correlated with each other.

Outliers are observations whose multiple regression residuals exceed three standard deviations.

A dummy variable can have only two values.

The error term accounts for all variables not specified in the model.

Autocorrelation is common with time series data.

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

The assumption of full model specification is that variables not specified in the model are justifiably omitted only when their cumulative effect on the dependent variable is zero.

Values of R² adjusted below .20 are considered to suggest weak model fit, those between .20 and .40 indicate moderate fit, those above .40 indicate strong fit, and those above .65 indicate very strong model fit.

Curvelinearity is addressed by transforming one of the independent variables.

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

It is okay for independent variables not to be correlated with the dependent variables, as long as they are highly correlated with each other.

Outliers are observations whose multiple regression residuals exceed three standard deviations.

A dummy variable can have only two values.

The error term accounts for all variables not specified in the model.

Autocorrelation is common with time series data.

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

The assumption of full model specification is that variables not specified in the model are justifiably omitted only when their cumulative effect on the dependent variable is zero.

Values of R2 adjusted below .20 are considered to suggest weak model fit, those between .20 and .40 indicate moderate fit, those above .40 indicate strong fit, and those above .65 indicate very strong model fit.

Curvelinearity is addressed by transforming one of the independent variables.

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

Values of R² adjusted below .20 are considered to suggest weak model fit, those between .20 and .40 indicate moderate fit, those above .40 indicate strong fit, and those above .65 indicate very strong model fit.