Final answer:
The condition when two or more predictor variables in a regression model have an exact linear relationship is known as perfect multicollinearity. This situation undermines the model's ability to estimate the effect of each variable and can make standard errors infinitely large, meaning option D is correct.
Step-by-step explanation:
The condition when two or more predictor variables have an exact linear relationship is referred to as perfect multicollinearity. In the context of statistics and econometrics, multicollinearity refers to a situation in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy. When this correlation is perfect, the predictors are said to be perfectly multicollinear, which can cause issues in estimating the regression coefficients uniquely as the predictors are not statistically independent.
Within a regression model, perfect multicollinearity is problematic because it undermines the statistical significance of an independent variable. Generally, in regression analysis, we want to isolate the effect that each independent variable has on the dependent variable. If two variables convey the same information (perfect multicollinearity), it becomes impossible to discern which variable is affecting the dependent variable. Consequently, the standard errors of the regression coefficients become infinitely large, and certain coefficients cannot be estimated distinctly. The model cannot be used to make inferences or predictions about the data.
Therefore, the correct option that describes the condition when two or more predictor variables have an exact linear relationship is D. Perfect multicollinearity. It's crucial to detect and address multicollinearity to ensure that the regression model is well-specified and that its estimations are reliable.