Statistical models are used to explain the variability of measurements made on the variable of interest (also called outcome, response).
In Regression Analysis, a linear model is used to describe the variability of a dependent (response) variable based on an assumed linear relationship with several independent variables (also called predictors, covariates, regressors). In Analysis-of-variance (ANOVA), linear models are used for the comparison of several group means. The simplicity of linear models makes them widely applicable in different disciplines including economics, agricultural, biological, engineering and social sciences. These models usually have as a basic assumption that the error terms are independently and identically distributed.
Read more about the research subject: Linear Models and their Extensions