Abstract
This chapter focuses on multiple regression model, along with its applications. The multiple linear regression model is the extension of the simple linear regression model that allows more than one independent variable. Although the multiple regression model must be linear in the model parameters, it may be used to describe curvilinear relationships. This is accomplished primarily by polynomial regression, but other forms may be used. The procedures for estimating the coefficients are presented, along with the procedure for obtaining the error variance and the inferences about model parameter. A brief description of correlations that describe the strength of linear relationships involving several variables is also provided. A regression model has more independent variables than necessary for an adequate description of the data. This problem of an excessive number of independent variables can be solved by selecting a subset of independent variables to be used in the model.
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