ABSTRACT
This chapter provides the concepts of regression and correlation where two or more independent variables are used to estimate the dependent variable. It elaborates on the concepts of the multiple linear regression model. The chapter defines the multiple regression line and explains how it is computed. It explains the multiple coefficient of determination and correlation and describes the partial coefficient of determination. The chapter explains the role of computer packages in performing multiple-regression. It utilizes the t and F distributions to test for significance of relationships in multiple regression. The chapter describes the assumptions of multiple linear regression – serial or autocorrelation, heteroscedasticity, and multicollinearity and defines the multiple standard error of estimate. The advantage of multiple regression over simple regression analysis is in enhancing our ability to use more available information in estimating the dependent variable. The purpose of multiple regression equation is to predict and estimate the value of the dependent variable.
