Multiple regression is one of the most widely used model in research (1) to assess the relationship between a group of independent variables with one dependent variable, (2) to evaluate the relationship between one independent variable and an dependent variable controlling for covariates, and (3) to predict an outcome variable (dependent) using a group of predictors (independent). Although regression model can be grasped using the linear equations we learned in high school:

In this model,

*x*_{1, }*x2,**x3 ...**xn*are a group of n independent variables, and y is the dependent variable. The regression relationship between the first three independent variables and the dependent variable y can be described graphically as below:This graph directly tells us that the three

*x*'s independently affect*y*, with each of their effects being expressed using the b regression coefficients. These coefficients are similar to the coefficients used in structural equation modeling (or path model) when no latent factors are included (refer to my blog on structural equation modeling in this thread.)