In this review article we consider linear regression analysis from a geometric perspective, looking at standard methods and outputs in terms of the lengths of the relevant vectors and the angles between these vectors. We show that standard regression output can be written in terms of the lengths and angles between the various input vectors, such that this geometric information is sufficient in linear regression problems. This allows us to obtain a standard formula for multiple correlation and give a geometric interpretation to this. We examine how multicollinearity affects the total explanatory power of the data, and we examine a counter-intuitive phenomena called "enhancement" where the total information from the explanatory vectors is greater than the sum of the marginal parts.
翻译:在本审查文章中,我们从几何角度考虑线性回归分析,从相关矢量的长度和这些矢量之间的角度来审视标准方法和输出结果。我们显示,标准回归输出可以按各种输入矢量之间的长度和角度来写,这样,这种几何信息就足以解决线性回归问题。这使我们能够获得一个多重相关性的标准公式,并对此进行几何解释。我们研究多曲线性如何影响数据的解释性总能力,我们研究一种反直觉现象,即“增强”现象,即解释矢量的全部信息大于边缘部分的总和。