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Plot observation diagnostics of linear regression model

`plotDiagnostics`

creates a plot of observation diagnostics
such as leverage, Cook's distance, and delete-1 statistics to identify outliers and
influential observations.

`plotDiagnostics(`

creates a leverage
plot of the linear regression model (`mdl`

)`mdl`

) observations. A
dotted line in the plot represents the recommended threshold values.

`plotDiagnostics(`

specifies the graphical properties of diagnostic data points using one or more
name-value pair arguments. For example, you can specify the marker symbol and size
for the data points.`mdl`

,`plottype`

,`Name,Value`

)

returns graphics objects for the lines or contour in the plot using any of the input
argument combination in the previous syntaxes. Use `h`

= plotDiagnostics(___)`h`

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

`legend('show')`

to show the pre-populated legend.

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.

[1] Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. *Applied
Linear Statistical Models*, Fourth Edition. Chicago: McGraw-Hill Irwin,
1996.