This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Multiple Linear Regression

Linear regression with multiple predictor variables

For greater accuracy on low- through medium-dimensional data sets, fit a linear regression model using fitlm.

For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, fit a linear regression model using fitrlinear.


Regression Learner Train regression models to predict data using supervised machine learning


LinearModel Linear regression model class
CompactLinearModel Compact linear regression model class
RegressionLinear Linear regression model for high-dimensional data
RegressionPartitionedLinear Cross-validated linear regression model for high-dimensional data


fitlm Create linear regression model
stepwiselm Create linear regression model using stepwise regression
compact Compact linear regression model
disp Display linear regression model
feval Evaluate linear regression model prediction
predict Predict response of linear regression model
random Simulate responses for linear regression model
plot Scatter plot or added variable plot of linear model
plotAdjustedResponse Adjusted response plot for linear regression model
fitrlinear Fit linear regression model to high-dimensional data
predict Predict response of linear regression model
dummyvar Create dummy variables
invpred Inverse prediction
plsregress Partial least-squares regression
x2fx Convert predictor matrix to design matrix
relieff Importance of attributes (predictors) using ReliefF algorithm
regress Multiple linear regression
robustdemo Interactive robust regression
robustfit Robust regression
rsmdemo Interactive response surface demonstration
rstool Interactive response surface modeling

Examples and How To

Linear Regression

Fit a linear regression model and examine the result.

Interpret Linear Regression Results

Display and interpret linear regression output statistics.

Linear Regression Workflow

Import and prepare data, fit a regression, test and improve its quality, and share it.

Regression with Categorical Covariates

Perform a regression with categorical covariates using categorical arrays and fitlm.

Regression Using Dataset Arrays

This example shows how to perform linear and stepwise regression analyses using dataset arrays.

Robust Regression — Reduce Outlier Effects

Fit a robust model that is less sensitive than ordinary least squares to large changes in small parts of the data.

Parametric Regression Analysis

Choose a regression function, and update legacy code using new fitting functions.

Partial Least Squares

Partial least squares (PLS) constructs new predictor variables as linear combinations of the original predictor variables, while considering the observed response values, leading to a parsimonious model with reliable predictive power.


What Are Linear Regression Models?

Regression models describe the relationship between a dependent variable and one or more independent variables.

Wilkinson Notation

Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

Was this topic helpful?