# What is Regression?

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Regression is a type of analysis that *estimates* the relationship between two or more variables. For example, the sales of a company have a link to the amount spent on advertising. One variable is called a **dependant variable**, and the others are **independent variables**. If both variables are independent, then there is no relationship, it’s just two sets of numbers with no influence on each other. In the above example, the sales are *dependant* on the amount of money spent on advertising, the advertising spend is an *independent variable*, and the sales number is a *dependant variable*.

There are different types of relationships; some are linear, which means as the dependant variable changes, the independent variable changes in a straight line.

There are also non-linear relationships, which are also called *polynomial*.

If the relationship is more binary, true/false, for instance, then we would use something called logistic regression.

In this chapter, we are going to cover Linear Regression and Polynomial Regression. Regression is a well-understood problem with some well-known algorithms to solve, so using a Neural Network might seem overkill. However, it’s an excellent way to introduce us to Neural Networks since we can model a regression analysis using a *single neuron*.

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