Question 1

Logistic regression deals with situations in which the dependent variable is dichotomous.

Accepted Answer

Logistic regression is designed to predict a binary outcome (i.e., a dependent variable with two possible values). It is used when the response variable or dependent variable is categorical, consisting of two categories, usually expressed as "success" or "failure" or "yes" or "no."

Question 2

Logistic regression is often used in political science.

Accepted Answer

Logistic regression is commonly used in political science to analyze and predict outcomes of elections or other political events based on various factors such as demographic characteristics, past voting behavior, and political ideology.

Question 3

The dichotomous nature of the dependent variable violates an assumption of multiple regression.

Accepted Answer

The dichotomous nature of the dependent variable violates the assumption of normality in multiple regression, which assumes that the dependent variable is normally distributed.

Question 4

Logistic regression fits a U-shaped curve to the data.

Accepted Answer

Logistic regression fits a sigmoidal curve to the data, not a U-shaped curve.

Question 5

Logistic regression is the same as multiple regression with dummy variables.

Accepted Answer

Logistic regression is used for binary or categorical outcome variables, predicting probabilities that an observation falls into one of the categories, while multiple regression is used for continuous outcome variables, predicting a continuous outcome based on multiple predictors. Dummy variables in multiple regression allow for the modeling of categorical predictors, not the transformation of the regression model into a logistic regression.

Question 6

The Nagelkerke R² is analogous to R² in multiple regression.

Accepted Answer

"Analogous" means "comparable in certain respects, typically in a way which makes clearer the nature of the things compared." Therefore, the statement is true as both the Nagelkerke and multiple regression models are comparable in certain respects.

Question 7

Classification tables should have 50% or fewer correctly predicted values.

Accepted Answer

Classification tables should have more than 50% correctly predicted values. A table with less than 50% accuracy would indicate that the model is performing worse than random guessing.

Question 8

The Hosmer and Lemeshow test compares the observed and predicted values and should be statistically significant.

Accepted Answer

The Hosmer and Lemeshow test is used to assess the goodness of fit of a logistic regression model. A statistically significant result (p < 0.05) actually indicates a poor fit between the observed and predicted values, whereas a non-significant result suggests a good fit.

Question 9

Wald χ² is the test statistic used in logistic regression for testing the statistical significance of logistic regression coefficients.

Accepted Answer

The Wald χ² test is used in logistic regression to test the statistical significance of logistic regression coefficients.

Question 10

Logistic regression can be used to predict the likelihood of an event occurring.

Accepted Answer

Logistic regression is a statistical method used to analyze and model the relationship between a categorical dependent variable and one or more independent variables. The output of logistic regression is a probability score that predicts the likelihood of an event occurring, making it suitable for binary classification problems.

Question 11

The odds ratio compares the probability of something occurring, as compared to it not occurring.

Accepted Answer

The odds ratio is a measure used to compare the likelihood of an event or outcome occurring in one group versus another. It calculates the odds of an event occurring in one group compared to the odds in another group. Therefore, it compares the probability of something occurring versus not occurring.

Question 12

The same principles that apply to multiple regression, such as full model specification, also apply to time series regression.

Accepted Answer

The same principles that apply to multiple regression, such as full model specification, also apply to time series regression.

Question 13

With time series data, the assumption of random distribution of error terms is usually violated.

Accepted Answer

Time series data often exhibit patterns and trends, which means that the error terms are not truly random. They may be correlated with each other or with past values of the series. As a result, traditional statistical models that assume random distribution of error terms may not be appropriate for time series analysis. Time series models such as ARIMA and VAR take into account the non-randomness of the error terms and are therefore more suitable for analyzing time series data.

Question 14

Autocorrelation is detected by examining the variance inflation factor.

Accepted Answer

Autocorrelation is detected by examining the residual plot or performing specific tests such as the Durbin-Watson test, not by examining the variance inflation factor. VIF is a measure of collinearity between variables in a regression model.

Question 15

Values of the Durbin-Watson statistic around 2 indicate autocorrelation.

Accepted Answer

Values of the Durbin-Watson statistic around 2 indicate little to no autocorrelation in the residuals of a regression analysis.

Question 16

The Durbin-Watson statistic has a range of values for which test statistics are inconclusive.

Accepted Answer

The Durbin-Watson statistic can range from 0 to 4 with values close to 2 indicating no autocorrelation in the data. However, values outside of the range 1 to 3 can indicate inconclusive results and further analysis is needed.

Question 17

Autocorrelation is a problem, but serial correlation is not.

Accepted Answer

Autocorrelation and serial correlation are interchangeable terms referring to the same phenomenon.

Question 18

It is better to correct for autocorrelation by taking first differences than by examining relationships in levels form.

Accepted Answer

Taking first differences removes any trends or patterns that may exhibit autocorrelation, making it a more effective method of correcting for autocorrelation than examining relationships in levels form.

Question 19

Policies and program are sometimes evaluated by including dummy variables in the model.

Accepted Answer

Dummy variables are often included in program evaluation models to account for differences in treatment groups or to control for certain variables that may affect the outcome.

Question 20

A step impact variable is similar to an increasing impact variable.

Accepted Answer

A step impact variable refers to a variable that changes its effect in a stepwise manner at certain points, while an increasing impact variable changes its effect gradually and continuously as its value increases.

Quiz 16: Logistic and Time Series Regression

Logistic regression deals with situations in which the dependent variable is dichotomous.

Logistic regression is often used in political science.

The dichotomous nature of the dependent variable violates an assumption of multiple regression.

Logistic regression fits a U-shaped curve to the data.

Logistic regression is the same as multiple regression with dummy variables.

The Nagelkerke R2 is analogous to R2 in multiple regression.

Classification tables should have 50% or fewer correctly predicted values.

The Hosmer and Lemeshow test compares the observed and predicted values and should be statistically significant.

Wald χ2 is the test statistic used in logistic regression for testing the statistical significance of logistic regression coefficients.

Logistic regression can be used to predict the likelihood of an event occurring.

The odds ratio compares the probability of something occurring, as compared to it not occurring.

The same principles that apply to multiple regression, such as full model specification, also apply to time series regression.

With time series data, the assumption of random distribution of error terms is usually violated.

Autocorrelation is detected by examining the variance inflation factor.

Values of the Durbin-Watson statistic around 2 indicate autocorrelation.

The Durbin-Watson statistic has a range of values for which test statistics are inconclusive.

Autocorrelation is a problem, but serial correlation is not.

It is better to correct for autocorrelation by taking first differences than by examining relationships in levels form.

Policies and program are sometimes evaluated by including dummy variables in the model.

A step impact variable is similar to an increasing impact variable.

The Nagelkerke R² is analogous to R² in multiple regression.

Wald χ² is the test statistic used in logistic regression for testing the statistical significance of logistic regression coefficients.