After simple linear regression (SLR) logical next learning step is multiple linear regression (MLR). To better explain what MLR is, lets start with reminder what SLR is.

## Simple linear regression

SLR is statistical method where predicted value depends linearly on one input value. That means that mathematically we can describe it by simple linear functions and visualize it with a line.

Formula for this would be:

This is just a reminder of what SLR is. But for more information on it you can look at my previous post on it.

## Multiple linear regression

Now that you are reminded what simple linear regression, we can move onto multiple linear regression. MLR is same thing but with more than one input variables. Here it is how it looks in mathematical equation.

## But what are all those variables?

Depending on where you look, all variables can have different names, but I’ll try to keep it simple with other commonly used terms.

y – value we want to predict/dependent variable/predicted value

Xi – features / independent variable / expanatory variable / observed variable

Ai – coefficient for feature

So simplified, we are predicting what value of y will be depending on features Xi and with coefficients Ai we are deciding how much each feature is affecting predicted value.

## More correct mathematical model

In translation, predicted value y is sum of all features multiplied with their coefficients, summed with base coefficient A0.

## Real world example:

This example won’t show implementation. Some implementation examples you can find in my Github repository that follows Udemy Machine learning A-Z course.

Imagine we want to predict salary of the employee should have. Input variables we have could be number or experience, number of years in company, position level, office location and many more. These are all variables someone’s salary might depend on. However, they would depend all in different significance. Number of years of experience would probably have higher impact than number of years in company. This is why we have those coefficients. They define weight(meaning) to each feature.

## Why is it called linear regression?

There are multiple features, but all coefficients and features in equation are linear. No variable has exponential higher than one. And that is why it is called linear. Otherwise we would have polynomial regression.

## Conclusion

Multiple linear regression is very similar to simple linear regression. And hopefully this post gives intuition on what it is and how it differs from SLR. To keep it simple, I didn’t go into any coding or underlying math. This, I will leave for other post. First, important is to understand what it is and hopefully you get that from this post.

## Other resources

https://www.investopedia.com/terms/m/mlr.asp

https://www.udemy.com/course/machinelearning

## Discussion (0)