Assumptions of Linear Regression | Machine Learning | Data Science #artificialintelligence #shorts

Linear regression is the simplest machine learning algorithm of predictive analysis. Linear regression algorithm shows a linear relationship between a dependent (y variable) and one or more independent (x variable) variables hence it is known as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.

The theory of linear regression is based on certain statistical assumptions. It is crucial to check these regression assumptions before modeling the data using the linear regression approach.

Mainly there are 7 assumptions taken while using Linear Regression:

Linear Model
No Multicolinearlity in the data
Homoscedasticity of Residuals or Equal Variances
No Autocorrelation in residuals
Number of observations Greater than the number of predictors
Each observation is unique
Predictors are distributed Normally


#artificialintelligence #technology #dataanalytics #datascience #gate #engineering #bigdata #dataengineering #basics #statistics #machinelearning