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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.

Figure 1. Example Linear Regression

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

Figure 2. Example Polynomial Regression

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

Figure 3. Example 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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