Regresión lineal múltiple

Regresión lineal con múltiples variables predictoras

Para una mayor precisión en conjuntos de datos de baja dimensión a media dimensión, ajuste un modelo de regresión lineal utilizando .fitlm

Para reducir el tiempo de cálculo en conjuntos de datos de alta dimensión, ajuste un modelo de regresión lineal mediante .fitrlinear

Apps

	Train regression models to predict data using supervised machine learning
Alumno de regresión	Train regression models to predict data using supervised machine learning

Objetos

`LinearModel`	Modelo de regresión lineal
`CompactLinearModel`	Compact linear regression model
`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

Funciones

expandir todo

y Funciones`LinearModelCompactLinearModel`

Crear objeto`LinearModel`

`fitlm`	Ajustar modelo de regresión lineal
`stepwiselm`	Perform stepwise regression

Crear objeto`CompactLinearModel`

compact Compact linear regression model

Agregar o quitar términos del modelo lineal

`addTerms`	Add terms to linear regression model
`removeTerms`	Remove terms from linear regression model
`step`	Improve linear regression model by adding or removing terms

Predecir respuestas

`feval`	Predict responses of linear regression model using one input for each predictor
`predict`	Predict responses of linear regression model
`random`	Simulate responses with random noise for linear regression model

Evaluar modelo lineal

`anova`	Analysis of variance for linear regression model
`coefCI`	Confidence intervals of coefficient estimates of linear regression model
`coefTest`	Linear hypothesis test on linear regression model coefficients
`dwtest`	Durbin-Watson test with linear regression model object

Visualizar estadísticas de resumen y modelo lineal

`plot`	Scatter plot or added variable plot of linear regression model
`plotAdded`	Added variable plot of linear regression model
`plotAdjustedResponse`	Adjusted response plot of linear regression model
`plotDiagnostics`	Plot observation diagnostics of linear regression model
`plotEffects`	Plot main effects of predictors in linear regression model
`plotInteraction`	Plot interaction effects of two predictors in linear regression model
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of linear regression model
`plotSlice`	Plot of slices through fitted linear regression surface

y Funciones`RegressionLinearRegressionPartitionedLinear`

Crear objeto

fitrlinear Fit linear regression model to high-dimensional data

Trabajar con objeto`RegressionLinear`

`predict`	Predict response of linear regression model
`loss`	Regression loss for linear regression models
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`selectModels`	Select fitted regularized linear regression models

Trabajar con objeto`RegressionPartitionedLinear`

`kfoldLoss`	Regression loss for observations not used in training
`kfoldPredict`	Predict responses for observations not used for training

Regresión lineal sin usar objeto

Ajustar y evaluar la regresión lineal

`dwtest`	Durbin-Watson test with residual inputs
`invpred`	Inverse prediction
`linhyptest`	Linear hypothesis test
`plsregress`	Partial least-squares (PLS) regression
`regress`	Regresión lineal múltiple
`regstats`	Regression diagnostics
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`robustfit`	Fit robust linear regression
`stepwisefit`	Fit linear regression model using stepwise regression

Preparar datos

`x2fx`	Convert predictor matrix to design matrix
`dummyvar`	Create dummy variables

Herramientas interactivas

`robustdemo`	Interactive robust regression
`rsmdemo`	Interactive response surface demonstration
`rstool`	Interactive response surface modeling
`stepwise`	Interactive stepwise regression

Temas

Introducción a la regresión lineal

What Is a Linear Regression Model?

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

Regresión lineal

Ajuste un modelo de regresión lineal y examine el resultado.

Stepwise Regression

In stepwise regression, predictors are automatically added to or trimmed from a model.

Reduce Outlier Effects Using Robust Regression

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

Choose a Regression Function

Choose a regression function depending on the type of regression problem, and update legacy code using new fitting functions.

Summary of Output and Diagnostic Statistics

Evaluate a fitted model by using model properties and object functions.

Wilkinson Notation

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

Flujos de trabajo de regresión lineal

Linear Regression Workflow

Import and prepare data, fit a linear regression model, test and improve its quality, and share the model.

Interpretar resultados de regresión lineal

Mostrar e interpretar estadísticas de salida de regresión lineal.

Linear Regression with Interaction Effects

Construct and analyze a linear regression model with interaction effects and interpret the results.

Linear Regression Using Tables

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

Linear Regression with Categorical Covariates

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

Train Linear Regression Model

Train a linear regression model using fitlm to analyze in-memory data and out-of-memory data.

Regresión parcial de mínimos cuadrados

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.

Regresión parcial de mínimos cuadrados y regresión de componentes principales

Este ejemplo muestra cómo aplicar la regresión parcial de mínimos cuadrados (PLSR) y la regresión de componentes principales (PCR), y describe la eficacia de los dos métodos.

Regresión lineal múltiple

Apps

Objetos

Funciones