In statistics, linear regression is often used to model the linear relationship between a scalar response and one or a few variables. It is usually represented by the following. Y = A + BX (where Y represents the scalar response, A represents the Y-intercept and B represents the gradient/slope and X represents the variable. If there are more than one variable, you will then have X1, X2 etc) In the area of investments, linear regression could be used to predict housing prices or understand the performance of a particular investment strategy. I have written about the use of linear regression in several articles in my blog, particular pertaining to housing prices. There are many different kinds of theories used in quantitative finance which are representations of linear regression. Examples will be Capital Asset Pricing Model (CAPM) and Fama and French Three Factor models. The inherent need for us to be...