岭回归(英文名：ridge regression, Tikhonov regularization)是一种专用于共线性数据分析的有偏估计回归方法，实质上是一种改良的最小二乘估计法，通过放弃最小二乘法的无偏性，以损失部分信息、降低精度为代价获得回归系数更为符合实际、更可靠的回归方法，对病态数据的拟合要强于最小二乘法。
总之，本文档是岭回归的R语言实现代码，主要用于解决当模型中出现多重共线性问题，尤其是当你所有的解释变量都很重要，又无法通过其他检验来删除时，岭回归是一个很好的解决办法。(Ridge regression (English Name: ridge regression, Tikhonov regularization) is a kind of special linear data analysis of biased estimation is actually the least squares regression method, an improved estimation method, the unbiased least square method to give up the part of the loss of information, reduce the accuracy of regression coefficient is more practical more reliable, the cost of pathological data was stronger than that of least squares fitting.
In short, this document is a ridge regression of the R language code, mainly used to solve the multicollinearity problems when the model, especially when you have all the explanatory variables are very important, and not by other inspection to delete, ridge regression is a good solution.)