Perfect multicollinearity:

implies, while a coefficient can estimated, it will be difficult to estimate precisely one or more of the partial effects using the data at hand.

means that you perfectly estimated the population regression function, such that all residuals equal zero.

violates one of the four Least Squares assumptions in the multiple regression model.

means that your estimated regression coefficient will not be statistically significantly different from zero.

QUESTION 4 Perfect multicollinearity: O implies, while a coefficient can estimated, it will be difficult to estimate precisely one or more of the partial effects using the data at hand. O means that you perfectly estimated the population regression function, such that all residuals equal zero. O violates one of the four Least Squares assumptions in the multiple regression model. means that your estimated regression coefficient will not be statistically significantly different from zero. QUESTION 5 The dummy variable trap is an example of: O something that does not happen to college students. O perfect multicollinearity. O imperfect multicollinearity. O a situation where all coefficients on binary regressors are not statistically significantly different from zero