What is Simple Linear Regression(SLR)?

Simple Linear Regression(SLR) is a statistical regression model that allows us to find a relationship between a Responsive variable( say y ) and one Explanatory variable ( say x ).

Because it uses only one Explanatory variable or Independent variable so it is being called SLR. If it gets more than one Explanatory variable or Independent variable, then it will be called Multiple Linear Regression.

In Machine Learning it comes under Supervised Machine Learning.

How do SLR works?

Please WATCH the below video to understand SLR in the easiest way. And Please download the PDF file to study.

Download the Below file(s) SLR-Theory-Tec4Tric to learn the Theory part & SLR-Math-Tec4Tric to learn How to solve an SLR Problem.

Read More about Machine Learning here – Machine Learning Blog

Machine Learning Tutorial by Sayan De – YouTube

What is a simple linear regression model?

Simple Linear Regression is a statistical regression model that allows us to find a relationship between a Responsive variable(say y) and one Explanatory variable (say x). So, the simple linear function would be y = ax + b, where “a” is the slope and “b” is the intercept.

How do you calculate simple linear regression?

It’s as simple as Y = a+bx, now we have to calculate a & b with some formula(s).

What is the difference between simple linear regression and multiple linear regression?

If the linear regression model uses only one Explanatory variable or Independent variable so it will be called Simple Linear Regression. If it gets more than one Explanatory variable or Independent variable, then it will be called Multiple Linear Regression.