Linear regression is a statistical modeling technique used to describe a continuous response variable as a function of one or more predictor variables. It can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data.
Linear regression techniques are used to create a linear model. The model describes the relationship between a dependent variable \(y\) (also called the response) as a function of one or more independent variables \(X_i\) (called the predictors). The general equation for a linear regression model is:
\[y = \beta_0 + \sum \ \beta_i X_i + \epsilon_i\]
where \(\beta\) represents linear parameter estimates to be computed and \(\epsilon\) represents the error terms.
There are several types of linear regression models:
Simple: model with only one predictor
Multiple: model with multiple predictors
Multivariate: model for multiple response variables
- Generate predictions
- Compare linear model fits
- Plot residuals
- Evaluate goodness-of-fit
- Detect outliers
To create a linear model that fits curves and surfaces to your data, see Curve Fitting Toolbox.