# Introduction to Regression Methods

### Regression – Simple, but SO effective

So, you want to be a data scientist, predicting stuff. It’s cool, right? Definitely. We can use data and statistical models to project into the future, predict outcomes in sports, almost anything.

If this is for you, then you will have come across the term *regression*.

This series of posts is going to cover what regression is, why it’s important, and some of the main methods used today.

Read on!

### What is regression?

Let’s start with a formal definition.

*Regression is a set of statistical methods that are used to estimate the relationship between an outcome (dependent) variable, and one or more explanatory (independent) variables*.

Typically, regression is what we use to predict, or to forecast an outcome. You can consider it a subset of machine learning, specifically a type of *Supervised Learning*, where we are training our model on known outcomes – input/output pairs.

### We’ll cover the following basic methods:

- Linear
- Logistic
- Polynomial
- Stepwise
- Ridge
- Lasso
- ElasticNet

### Ready? Let’s get started.

Regression methods are super helpful, and the best starting point in your machine learning education. We’re going to jump straight in with the most understandable **linear regression**. We’ll see you in the next one!