Supervised Linear Regression

• Ordinary Least Squares regression (metodo dei minimi quadrati)
• Example
• How to use LinearRegression.js

Simple Linear Regression is finding the best relationship between the input variable x (independent variable) and the expected variable y (dependent variable).
The linear relationship between these two variables can be represented by a straight line called regression line.

The variable y is linearly dependent on the variable x. So, we can find a straight line which can best define the data.

This model could be applied to these following situations, for example estimating:

• Total sales based on money spent on advertising
• Salary based on years of experience
• Muscle strength by body mass
• Cholesterol level by physical exercise (in mins)

ecc…

OLS

To calculate the regression line you can use the Ordinary Least Squares regression (metodo dei minimi quadrati):

Example

Y(predict value)=a(0.135)+b(0.627)*X(UserInput)

LinearRegression.js

How to use

// inital datasets for training 
var x = [1, 2, 3]; // inputs
var y = [123, 4234, 435345]; // expected values
var test = new LinearRegression(x, y);
test.predict(6);

Authors

• Anas Araid - asdf1899