Statistical Tableau

Chapter 9. Polynomial Regression in Tableau

Linear regression can be a powerful tool in your toolkit, as you saw in the last chapter. However, sometimes it’s not the best tool for the job. In this chapter, you will be introduced to another regression model called polynomial regression. This model allows your predictions to move along with the data rather than being a rigid line. There are many advantages of this model, but you have to be careful not to overfit the model.

In this chapter, you will be introduced to polynomial regression, learn the pros and cons of the model, and learn about overfitting a model.

What Is Polynomial Regression?

As with linear regression, polynomial regression is a predictive model that has many use cases:

Physics

Polynomial regression is often used for curve fitting when the relationship between the independent and dependent variables appears to follow a curve or a nonlinear pattern. For example, in physics, it can be used to model the trajectory of a projectile.

Economics and finance

In economics and finance, polynomial regression can be used to analyze the relationship between variables like gross domestic product (GDP) and time or stock prices and time, where linear models might not capture the underlying trends accurately.

Medicine and biology

In medical research and biology, polynomial regression can be used to model growth curves, drug concentration–response relationships, or the relationship between age and various physiological parameters. ...