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Plot of slices through fitted linear regression surface

`plotSlice(`

creates a figure
containing one or more plots, each representing a slice through the regression
surface predicted by `mdl`

)`mdl`

. Each plot shows the fitted response
values as a function of a single predictor variable, with the other predictor
variables held constant.

`plotSlice`

also displays the 95% confidence bounds for the
response values. Use the **Bounds** menu to choose the type
of confidence bounds, and use the **Predictors** menu to
select which predictors to use for each slice plot. For details, see Tips.

Use the

**Bounds**menu in the figure window to choose the type of confidence bounds. You can choose**Simultaneous**or**Non-Simultaneous**, and**Curve**or**Observation**. You can also choose**No Bounds**to have no confidence bounds.**Simultaneous**or**Non-Simultaneous**Simultaneous (default) —

`plotSlice`

computes confidence bounds for the curve of the response values using Scheffe's method. The range between the upper and lower confidence bounds contains the curve consisting of true response values with 95% confidence.Non-Simultaneous —

`plotSlice`

computes confidence bounds for the response value at each observation. The confidence interval for a response value at a specific predictor value contains the true response value with 95% confidence.

Simultaneous bounds are wider than separate bounds, because requiring the entire curve of response values to be within the bounds is stricter than requiring the response value at a single predictor value to be within the bounds.

**Curve**or**Observation**A regression model for the predictor variables

*X*and the response variable*y*has the form*y*=*f*(*X*) +*ε*,where

*f*is a function of*X*and*ε*is a random noise term.**Curve**(default) —`plotSlice`

predicts confidence bounds for the fitted responses*f*(*X*).**Observation**—`plotSlice`

predicts confidence bounds for the response observations*y*.

The bounds for

*y*are wider than the bounds for*f*(*X*) because of the additional variability of the noise term.

Use the

**Predictors**menu in the figure window to select which predictors to use for each slice plot. If the regression model`mdl`

includes more than eight predictors,`plotSlice`

creates plots for the first five predictors by default.

Use

`predict`

to return the predicted response values and confidence bounds. You can also specify the confidence level for confidence bounds by using the`'Alpha'`

name-value pair argument of the`predict`

function. Note that`predict`

finds nonsimultaneous bounds by default whereas`plotSlice`

finds simultaneous bounds by default.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.