Final answer:
Perfect multicollinearity is the condition when two or more predictor variables in a regression model have an exact linear relationship, making it impossible to uniquely estimate the regression coefficients.
Step-by-step explanation:
The condition called when two or more predictor variables have an exact linear relationship is known as Perfect multicollinearity.
In the context of statistics and regression analysis, multicollinearity refers to the phenomenon where one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.
In the case of perfect multicollinearity, the relationship is exact, indicating that the predictor variables are correlated to a point that the coefficients of the regression equation cannot be uniquely estimated.