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**Superclasses: **`CompactLinearModel`

Linear regression model class

An object comprising training data, model description, diagnostic
information, and fitted coefficients for a linear regression. Predict
model responses with the `predict`

or `feval`

methods.

or `mdl`

=
fitlm(`tbl`

)

create
a linear model of a table or dataset array `mdl`

=
fitlm(`X`

,`y`

)`tbl`

,
or of the responses `y`

to a data matrix `X`

.
For details, see `fitlm`

.

or `mdl`

= stepwiselm(`tbl`

)

create
a linear model of a table or dataset array `mdl`

=
stepwiselm(`X`

,`y`

)`tbl`

,
or of the responses `y`

to a data matrix `X`

,
with unimportant predictors excluded. For details, see `stepwiselm`

.

addTerms | Add terms to linear regression model |

compact | Compact linear regression model |

dwtest | Durbin-Watson test of linear model |

fit | Create linear regression model |

plot | Scatter plot or added variable plot of linear model |

plotAdded | Added variable plot or leverage plot for linear model |

plotAdjustedResponse | Adjusted response plot for linear regression model |

plotDiagnostics | Plot diagnostics of linear regression model |

plotResiduals | Plot residuals of linear regression model |

removeTerms | Remove terms from linear model |

step | Improve linear regression model by adding or removing terms |

stepwise | Create linear regression model by stepwise regression |

anova | Analysis of variance for linear model |

coefCI | Confidence intervals of coefficient estimates of linear model |

coefTest | Linear hypothesis test on linear regression model coefficients |

disp | Display linear regression model |

feval | Evaluate linear regression model prediction |

plotEffects | Plot main effects of each predictor in linear regression model |

plotInteraction | Plot interaction effects of two predictors in linear regression model |

plotSlice | Plot of slices through fitted linear regression surface |

predict | Predict response of linear regression model |

random | Simulate responses for linear regression model |

Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).

The main fitting algorithm is QR decomposition. For robust fitting,
the algorithm is `robustfit`

.

To remove redundant predictors in linear regression using lasso
or elastic net, use the `lasso`

function.

To regularize a regression with correlated terms using ridge
regression, use the `ridge`

or `lasso`

functions.

To regularize a regression with correlated terms using partial
least squares, use the `plsregress`

function.

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