Explanation of multicollinearity using the decomposition theorem of ordinary linear regression models
In a multiple linear regression model, the algebraic formula of the decomposition theorem explains the relationship between the univariate regression coefficient and partial regression coefficient using geometry. It was found that univariate regression coefficients are decomposed into their respective partial regression coefficients according to the parallelogram rule. Multicollinearity is analyzed with the help of the decomposition theorem. It was also shown that it is a sample phenomenon that the partial regression coefficients of important explanatory variables are not significant, but the sign expectation deviation cause may be the population structure between the explained variables and explanatory variables or may be the result of sample selection. At present, some methods of diagnostic multicollinearity only consider the correlation of explanatory variables, so these methods are basically unreliable, and handling multicollinearity is blind before the causes are not distinguished. The increase in the sample size can help identify the causes of multicollinearity, and the difference method can play an auxiliary role.