Regression Analysis

Standard Estimation Results In Least Squares

Multiple linear regression is a way to express the idea that a response variable, y, varies with a set of independent variables x1, x2 ,.., xm. The variability in y has two components: a systematic part and a random part. The systematic variation of y can be modeled as a function of x variables. The model relating y to x1, x2,…,xm is called the regression equation. The random part takes into account the fact that the model does not exactly describe the behavior of the response [6].

Multiple linear regression fits a response variable y to a function of independent variables and parameters. The general linear regression fits a response variable y to a function of linear variables and parameters. The general linear regression model has the form

Y = A0 + A1x1 + … + Amxm + e

where y is the response, or dependent, variable, A0, A1 ,…, Am are unknown parameters, x1, x2 ,…, xm are the independent variables, and e is the random er...