Regression analysis is the statistical tool that assists in assessing the relationship between two variables. The association between the two variables is quantified for example by placing X & Y in a line or on a plane in such a way that all the points are evenly distributed on a plane or lies in a line.

This tool is used to depict the relationship between a variable with one or more other variables. Different types of Regression analysis that can be used are as follows:

Simple Regression: It is the simplest form of Regression Analysis which has only two variables namely X & Y that comply with each other. It is applicable with both Linear & Nonlinear Regression analyses that has one variable.

Polynomial Regression: It is a nonlinear equation that includes any number of data or terms in an equation. They are not strictly nonlinear equations, however it can be a linear equation when one variable is Constant & compared with other variable. It is applied for interpolation & graphing purposes.

Calibration Model: It includes two variables where large numbers of known values are measured and as per the values an equation is drawn. It is later used to know the unknown values by inversing the predicted value.

Multiple Regressions: This model defines one variable as a function to multiple numbers of variables and used to include data to a linear equation with two or more variables.

Nonlinear Regression: It is a type of analysis which involves a nonlinear combination of the variables that depends on one or more independent variables.

Partial Least Squares: It is designed to develop statistical models where multiple independent variables are related to multiple dependent variables. It is useful for the prediction of many values. It is largely used by Chemical Engineers & Chemometricians.