Scatter plot or added variable plot of linear regression model

`plot(mdl)`

`h = plot(mdl)`

`plot(`

creates a plot of the linear
regression model `mdl`

)`mdl`

. The plot type depends on the number of
predictor variables.

If

`mdl`

includes multiple predictor variables,`plot`

creates an Added Variable Plot for the whole model except the constant (intercept) term, equivalent to`plotAdded(mdl)`

.If

`mdl`

includes a single predictor variable,`plot`

creates a scatter plot of the data along with a fitted curve and confidence bounds.If

`mdl`

does not include a predictor,`plot`

creates a histogram of the residuals, equivalent to`plotResiduals(mdl)`

.

returns graphics objects for the lines or patch in the plot. Use
`h`

= plot(`mdl`

)`h`

to modify the properties of a specific line or patch
after you create the plot. For a list of properties, see Line Properties and Patch Properties.

The data cursor displays the values of the selected plot point in a data tip (small text box located next to the data point). The data tip includes the

*x*-axis and*y*-axis values for the selected point, along with the observation name or number.

A

`LinearModel`

object provides multiple plotting functions.When creating a model, use

`plotAdded`

to understand the effect of adding or removing a predictor variable.When verifying a model, use

`plotDiagnostics`

to find questionable data and to understand the effect of each observation. Also, use`plotResiduals`

to analyze the residuals of the model.After fitting a model, use

`plotAdjustedResponse`

,`plotPartialDependence`

, and`plotEffects`

to understand the effect of a particular predictor. Use`plotInteraction`

to understand the interaction effect between two predictors. Also, use`plotSlice`

to plot slices through the prediction surface.

The

`plot`

function creates an added variable plot for the model as a whole (except a constant term) if the model includes multiple terms. Use`plotAdded`

to select particular predictors for an added variable plot.