Regression is a statistical relationship between two or more variables in which a change in the independent variable is associated with a difference in the dependent variable.
It’s important to note that not all variables are related to each other. For example, a person’s favorite color may not be related to revenue from a website. But if you look at a chart showing height and age, the change in one variable—height—is closely associated with the change in the other variable—age. This makes intuitive sense, as from birth, as you get older, you get taller. If you plot that data, you would see those green points on the graph up to some particular age where growth would taper off. The plot in the middle shows the clear linear relationship between age and height, which is indicated by the solid red line. You sometimes call that line a trendline, or a regression line, or the line of best fit. You see that the height is the dependent variable, and age is the independent variable.
You might ask, “Doesn’t height depend on other factors?” Of course, it does, but here we’re looking at the relationship between two variables, one independent and one dependent: age and height.
Next, let us take a look at the types of regression.