Regresión lineal múltiple
Regresión lineal con varias variables predictoras
Para aumentar la precisión en conjuntos de datos de dimensiones bajas y medianas, ajuste un modelo de regresión lineal mediante fitlm
.
Para reducir el tiempo de cálculo en conjuntos de datos de altas dimensiones, ajuste un modelo de regresión lineal mediante fitrlinear
.
Apps
Regression Learner | Train regression models to predict data using supervised machine learning |
Objetos
LinearModel | Linear regression model |
CompactLinearModel | Compact linear regression model |
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
Funciones
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. - Linear Regression
Fit a linear regression model and examine the result. - 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 las regresiones lineales
- Linear Regression Workflow
Import and prepare data, fit a linear regression model, test and improve its quality, and share the model. - Interpret Linear Regression Results
Display and interpret linear regression output statistics. - 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 andfitlm
. - Analyze Time Series Data
This example shows how to visualize and analyze time series data using atimeseries
object and theregress
function. - Train Linear Regression Model
Train a linear regression model usingfitlm
to analyze in-memory data and out-of-memory data.
Regresión de mínimos cuadrados parciales
- 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. - Partial Least Squares Regression and Principal Components Regression
Apply partial least squares regression (PLSR) and principal components regression (PCR), and explore the effectiveness of the two methods.