Linear regression models a relationship between two variables, a dependent variable (also known as the outcome variable) and an independent variable (also known as the explanatory variable) using data observed in the study. This relationship is modeled by fitting a linear equation to the two variables.  

When fitting a line to the given data, it is important to check the validity of the model. Before proceeding with analysis, all assumptions must be verified, and the fit of the model checked.

Multivariate regression analysis is an extention of the bivariate regression analysis mentioned above. In the case of a multivariate analysis, more than one independent variable is used to model the outcome.

Although Multivariate Linear Regression is the most common type of model used, it is not always the correct model. Thus, multivariate non-linear models must be used to fit the data. Multivariate Logistic Regression can be used when the outcome is binary.

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