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Multiple linear 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. If
you need to investigate a fitted regression model further, create a linear regression
model object `LinearModel`

by using `fitlm`

or `stepwiselm`

. A `LinearModel`

object provides more features than `regress`

.

Use the properties of

`LinearModel`

to investigate a fitted linear regression model. The object properties include information about coefficient estimates, summary statistics, fitting method, and input data.Use the object functions of

`LinearModel`

to predict responses and to modify, evaluate, and visualize the linear regression model.Unlike

`regress`

, the`fitlm`

function does not require a column of ones in the input data. A model created by`fitlm`

always includes an intercept term unless you specify not to include it by using the`'Intercept'`

name-value pair argument.You can find the information in the output of

`regress`

using the properties and object functions of`LinearModel`

.Output of `regress`

Equivalent Values in `LinearModel`

`b`

See the `Estimate`

column of the`Coefficients`

property.`bint`

Use the `coefCI`

function.`r`

See the `Raw`

column of the`Residuals`

property.`rint`

Not supported. Instead, use studentized residuals ( `Residuals`

property) and observation diagnostics (`Diagnostics`

property) to find outliers.`stats`

See the model display in the Command Window. You can find the statistics in the model properties ( `MSE`

and`Rsquared`

) and by using the`anova`

function.

[1] Chatterjee, S., and A. S. Hadi. “Influential Observations, High Leverage
Points, and Outliers in Linear Regression.” *Statistical
Science*. Vol. 1, 1986, pp. 379–416.

`LinearModel`

| `fitlm`

| `stepwiselm`

| `mvregress`

| `rcoplot`