Articles on: Algorithms

# Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is used to find the best linear relationship between the input variables and the output variable.

The basic idea behind linear regression is that there is a linear relationship between the input variables (also known as independent variables or predictors) and the output variable (also known as the dependent variable or response). The goal of linear regression is to find the best linear relationship between the input variables and the output variable.

A linear regression model is represented by an equation of the form:

where y is the output variable, x1, x2, ..., xn are the input variables, b0, b1, b2, ..., bn are the coefficients of the model, also called weights. The goal of linear regression is to find the best values of these coefficients (b0, b1, b2, ..., bn) that minimize the difference between the predicted values and the true values of the output variable.

The process of finding the best coefficients is typically done through a method called least squares optimization, which finds the values of the coefficients that minimize the sum of the squared differences between the predicted and true values of the output variable.

Linear regression is a widely used method and has a number of applications in various fields, such as economics, finance, medicine, and engineering. It's easy to interpret, and it's a good starting point for modeling data. However, it's important to note that linear regression assumes a linear relationship between the input variables and the output variable, and it may not be suitable for modeling non-linear relationships. Additionally, it assumes that the error terms are normally distributed and have constant variance. If these assumptions are not met, other methods such as polynomial regression or nonlinear regression should be considered.

Updated on: 26/01/2023