Regression Analysis
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
A statistical method that models how one or more input variables relate to a continuous outcome, used to forecast values like prices, demand, or risk.
What is Regression Analysis?
Regression analysis is a statistical method that models the relationship between a dependent variable and one or more independent variables to predict a continuous outcome, such as a price, a temperature, or a demand level.
It does double duty. The fitted model produces forecasts, and its coefficients show how much each input moves the outcome, which makes regression as useful for explanation as for prediction.
Types of Regression Analysis
- Linear regression fits a straight line: one input, one output, a constant rate of change between them.
- Multiple regression extends the line to several inputs, so the model weighs square footage, location, and age together instead of one factor at a time.
- Polynomial regression fits curves rather than lines, for relationships that bend.
- Logistic regression is the misnamed one: it estimates the probability of a categorical outcome, which makes it a classification method in regression clothing.
Regression Analysis vs Classification
Regression analysis predicts a number; classification predicts which category an input belongs to. Ask “how much?” and you want regression; ask “which kind?” and you want a classifier. Both learn from labeled examples, and many problems fit either framing, depending on the question you pose.
| Regression Analysis | Classification | |
|---|---|---|
| Predicts | A continuous number | A category label |
| Typical question | “What will this house sell for?” | “Is this transaction fraud?” |
| Output | A numeric value | A class, usually with a confidence score |
| Evaluated with | Error measures such as MSE or MAE | Accuracy, precision, recall |
Example of Regression Analysis
A real estate agency wants to price listings from data instead of gut feel. It takes several years of local sales, records each home’s square footage, bedroom count, age, and distance to the city center, and fits a multiple regression against the sale price.
The output is a formula. Plug in a new listing’s numbers and it returns an estimated price; read the coefficients and you learn what the market pays for an extra square meter or an added bedroom.
The residuals matter too. Homes that sold far above or below the model’s estimate are exactly the ones worth a second look.
Related AI terms: Classification · Predictive Modeling · Time Series · Logistic Regression
Did you like the Regression Analysis gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.
Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About