If you need to investigate a fitted regression model further, create a linear regression model object LinearModel by using fitlm or stepwiselm. There are several Statistics and Machine Learning Toolbox™ functions for performing regression. regress is useful when you simply need the output arguments of the function and when you want to repeat fitting a model multiple times in a loop. Update Legacy Code with New Fitting Methods b regress (y,X) returns a p-by-1 vector b of coefficient estimates for a multilinear regression of the responses in y on the predictors in X. Generate data with the trend y 1 0 - 2 x, and then change one value to simulate an outlier. The linearity in a linear regression model refers to the linearity of the predictor coefficients. Compare Robust and Least-Squares Regression. Multiple regression using weight and horsepower as predictors. See Lasso and Elastic Net or Ridge Regression.Ĭorrelated continuous predictors, continuous response, linear modelĬontinuous or categorical predictors, continuous response, unknown modelĬontinuous predictors, multivariable response, linear modelįitted multivariate regression model coefficientsĬontinuous predictors, continuous response, mixed-effects model b regress( y, X ) returns a vector b of coefficient estimates for a multiple linear regression of the responses in vector y on the predictors in matrix X. A regression model describes the relationship between a response and predictors. Set of models from ridge, lasso, or elastic net regression See Generalized Linear Models.Ĭontinuous predictors with a continuous nonlinear response, parametrized nonlinear modelĬontinuous predictors, continuous response, linear model Continuous or categorical predictors, continuous response, linear modelĬontinuous or categorical predictors, continuous response, linear model of unknown complexityĬontinuous or categorical predictors, response possibly with restrictions such as nonnegative or integer-valued, generalized linear modelįitted generalized linear model coefficientsįitglm or stepwiseglm. This function takes cell array or matrix target t and output y, each with total matrix rows of N, and returns the regression values, r, the slopes of regression.
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