What is linear regression?

Question

Accepted Answer

Linear regression is a statistical method used to model the relationship between two variables.

It assumes a linear relationship between the dependent and independent variables.
It is used to predict the value of the dependent variable based on the value of the independent variable.
It can be used for both simple and multiple regression analysis.
Example: predicting the price of a house based on its size or predicting the salary of an employee based on their years of experience.

Accepted Answer

Linear regression is a method of fitting a regression line to data and getting predictions for new values. Linear regression is based on the hypothesis that the dependent variable has a linear relation with independent variables. The functional form linear regression is y=mx+c+e,

where y is the dependent variable, x is the independent variable, m and c are the parameters of the model and e is the error term.

The cost or loss function in linear regression is OLS (ordinary least square).

The basic assumptions of linear regression are:

There is a linear relation between dependent and independent variables.
The error values in prediction are normally distributed.
The error values in prediction have constant variance (Homoscedascity)
The error values in prediction are not autocorrelated.
The independent variables do not have multi-collinearity.

What is linear regression?

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