Definition
Regression is a statistical technique that models the relationship between a dependent variable (the outcome you want to predict) and one or more independent variables (the factors you think influence the outcome). The simplest form, linear regression, fits a straight line through data points to describe and predict the relationship.
How It Works
Linear regression finds the line that minimizes the total squared distance between the data points and the line itself.
A real estate analyst wants to predict house prices based on square footage.
After analyzing 500 homes, the regression equation is: Price = $50,000 + $150 per square foot.
For a 2,000 sq ft home: $50,000 + ($150 x 2,000) = $350,000 predicted price.
Why It Matters
Regression is one of the most widely used tools in statistics. Businesses use it to forecast sales, economists use it to model growth, and scientists use it to understand how variables interact. It goes beyond correlation by giving you a predictive equation.
Multiple regression extends the concept by including several predictor variables at once. For example, predicting house prices using square footage, number of bedrooms, and neighborhood together. This makes regression a flexible and powerful tool for real-world analysis.
Regression models relationships between variables and enables predictions. It is the foundation of predictive analytics and one of the most practical statistical tools.