AI & Machine Learning · reviewed in July 2026
Linear regression
Linear regression is a statistical model that describes the relationship between a numeric variable you want to predict (the dependent variable) and one or more explanatory variables, assuming that relationship is a straight line. It's one of the simplest, most interpretable models in both statistics and machine learning.
Frequently asked questions
What do a linear regression's coefficients mean?
Each coefficient tells you how much the dependent variable changes, on average, when its associated explanatory variable increases by one unit, holding the others constant.
What is a regression's R²?
It's a metric between 0 and 1 indicating what proportion of the dependent variable's variation the model explains; an R² of 0.8 means the model explains 80% of that variation.
When is linear regression not appropriate?
When the real relationship between the variables isn't roughly linear, or when the variable you're predicting is categorical instead of numeric — in that last case you'd use logistic regression or another classification model.