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Multivariate Linear Regression

Linear regression with a multivariate response variable

When you need to include more than one response variable in a regression model, use a multivariate linear regression model. A multivariate linear regression model expresses a d-dimensional continuous response vector as a linear combination of predictor terms plus a vector of error terms with a multivariate normal distribution. You can use mvregress to create a multivariate linear regression model.

Partial least-squares (PLS) regression is a dimension reduction method that constructs new predictor variables that are linear combinations of the original predictor variables. To fit a PLS regression model that has multiple response variables, use plsregress.

Note

A multivariate linear regression model is different from a multiple linear regression model, which models a univariate continuous response as a linear combination of exogenous terms plus an independent and identically distributed error term. To fit a multiple linear regression model, use fitlm or fitrlinear.

Functions

mvregressMultivariate linear regression
mvregresslikeNegative log-likelihood for multivariate regression
plsregressPartial least-squares (PLS) regression

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