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Regresión lineal múltiple

Regresión lineal con múltiples variables predictoras

Para una mayor precisión en los conjuntos de datos de bajo a medio dimensional, ajuste un modelo de regresión lineal utilizando fitlm.

Para un tiempo de cómputo reducido en conjuntos de datos de alta dimensión que caben en el espacio de trabajo MATLAB^®, ajuste un modelo de regresión lineal utilizando fitrlinear.

Aplicaciones

Estudiante de regresión

Train regression models to predict data using supervised machine learning

Clases

`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

Funciones

`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
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plsregress`	Partial least-squares regression
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`x2fx`	Convert predictor matrix to design matrix

`regress`	Multiple linear regression
`robustdemo`	Interactive robust regression
`robustfit`	Robust regression
`rsmdemo`	Interactive response surface demonstration
`rstool`	Interactive response surface modeling

Ejemplos y procedimientos

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 Tables

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

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.