Multiple Regression

In the multiple regression model, the adjusted R2

• Cannot be Negative

• will never be greater than the regression R2

• equals the square of the correlation coefficient r.

• cannot decrease when an additional explanatory variable is added.

If you had a two regressor regression model, then omitting one variable which is relevant

• will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is negative.

• will always bias the coefficient of the included variable upwards.

• will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is zero.

• makes the sum of the product between the included variable and the residuals different from 0.

The dummy variable trap is an example of

• imperfect multicollinearity

• something that is of theoretical interest only

• perfect multicollinearity

• something that does not happen to university or college students

Which one of these can cause a bias in Randomized control trials

• Attrition Bias

• Hawthorne Effect

• Compliance

• All of the above

In the Fixed Time Effects regression model, you should exclude one of the binary variables for the time periods when an intercept is present in the equation

• because the first time period must always excluded from your data set.

• because there are already too many coefficients to estimate.

• to avoid perfect multicolinearity

• to allow for some changes between time periods to take place.