LINEAR REGRESSION

Shivali Srivastava
Institute for Signal and Information Processing
Mississippi State University, Mississippi State, MS 39762
email: shivali@isip.msstate.edu

ABSTRACT

The technique of regression, in particular linear regression, is the most popular statistical tool. There are several forms of regression such as linear, non-linear, simple, multiple, parametric and non-parametric.The major purpose of regression is to explore the dependence of one variable on other. One is called as response variable and other is known to be predictor variable. The goal is to forecast the values of the dependent variables on the basis of one or, more explanatory variables by setting up an appropriate probability model. The type of model depends on the types of variables involved.

The talk will contain the different models to represent linear regression such as mathematical model, statistical model and the probabilistic model. The emphasis will be given to least square criterion of regression. Quality of fit is an important issue in regression analysis. I will discuss the simple as well as multiple linear regression and give emphasis on the techniques of getting the best quality model.

Additional items of interest: