Understanding Linear Regression

One of the most common statistical methods is linear regression. At its most basic, it’s used when you want to express the mathematical relationship between two variables or attributes. When you use it, you are making the assumption that there is a linear relationship between an outcome variable (sometimes also called the response variable, dependent variable, or label) and a predictor (sometimes also called an independent variable, explanatory variable, or feature); or between one variable and several other variables, in which case you’re modeling the relationship as having a linear structure.
Changes in one variable correlate linearly with changes in another variable. For example, it makes sense that the more products you sell, the more money you make.