Linear regression is the equation which provides of straight line that best describes the association between two continuous variables, x and y. However, it is often the case that the dependent variable y is influenced by more than one variable and not just a single x variable. Multivariate analysis is a statistical modeling technique wherein multiple x variables are analysed simultaneously for their effect on y, resulting in an additive model (via an equation) that explains the observation/s and corrects for confounding association/s using one dependent and several independent variables, assigning a gradient to each of these independent variables, and with all product terms of gradient and magnitude of the independent variables adding up to estimate 'y'. This paper outlines these various techniques and applications.