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Plot main effects of predictors in linear regression model

`plotEffects(mdl)`

`h = plotEffects(mdl)`

`plotEffects(`

creates an effects plot
of the predictors in the linear regression model `mdl`

)`mdl`

. An
effects plot shows the estimated main effect on the
response from changing each predictor value, averaging out the effects of the other
predictors. A horizontal line through an effect value indicates the 95% confidence
interval for the effect value.

returns line objects. Use `h`

= plotEffects(`mdl`

)`h`

to modify the properties of a
specific line after you create the plot. For a list of properties, see Line 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. Use the*x*-axis values to view an estimated effect value and its confidence bounds.

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.

`CompactLinearModel`

| `LinearModel`

| `plotAdjustedResponse`

| `plotInteraction`