### Introduction

Linear regression is a statistical method used for modeling a relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. When only one explanatory variable is involved, it is called simple linear regression. And in case of many explanatory variabes, the process is termed multiple linear regression.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

Regression models can be used to make predictions of kinetic data of a series of chemically similar compounds. The molecular structure of a chemical compound determines its properties. A set of numerical descriptors are calculated to encode information about each of the molecular structures. These descriptors are then used to build statistical models using linear regression to predict the kinetics or activity of interest. However, this method is inductive, meaning it depends on having a set of compounds with known kinetic parameters or activities.

### Example Cases

For examples of previously performed studies in which Linear Regression of Kinetic Data with Chemical Descriptors was the primary method used, see the following example cases:

Linear Regression of Kinetic Data with Chemical Descriptors was also used in the following examples: