Deck 15: Multiple Regression

Question 1

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

Answer 1

True

Question 2

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

Answer 2

False

Question 3

A nomothetic mode of explanation isolates the most important factors.

Answer 3

True

Question 4

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

Question 5

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

Question 6

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.

Question 7

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.

Question 8

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

Question 9

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

Question 10

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.

Question 11

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

Question 12

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.

Question 13

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

Question 14

It is common to compare β coefficients across different models.

Question 15

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

Question 16

A dummy variable can have only two values.

Question 17

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

Question 18

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.

Question 19

Outliers can affect the slope of regression coefficients.

Question 20

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

Question 21

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

Question 22

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

Question 23

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

Question 24

Curvelinearity is addressed by transforming one of the independent variables.

Question 25

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

Question 26

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

Question 27

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

Question 28

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

Question 29

Autocorrelation is common with time series data.