用主成分回归分析解决回归模型中复共线性问题

Using Factor and Regression Analysis to Solve Collinearity in Regression model

主成分回归分析是将回归模型中有严重复共线性的变量进行因子提取,得到正交的因子变量,然后对因子变量进行回归模型的建立.通过具体实例介绍复共线性的判别方法以及如何利用主成分回归分析方法建立较理想的回归模型,从而解决了由于复共线性而造成病态回归方程的问题.

Factor and Regression Analysis is to draw the new factor variables from collinearity variables in the regression equation. So we can obtain the orthogonal factor variables, then use new factor variables to regression analysis. Through the example we introduce different ways to judge collinearity and introduce how to use factor and regression analysis to establish a good regression equation.