---
title: At Your Request
---

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# At Your Request

Many of the new features in STATGRAPHICS Centurion XV were added at the request of current users. A small sample of these additions is shown below:

[Graphical ANOVA](https://www.statgraphics.com/at-your-request#graphicalanova)

[Multiple Data Sheets](https://www.statgraphics.com/at-your-request#multipledatasheets)

[Cuscore Charts](https://www.statgraphics.com/at-your-request#cuscorecharts)

[Bayesian Neural Network Classifier](https://www.statgraphics.com/at-your-request#bayesianneuralnetworkclassifier)

[Control Charts for Non-Normal Data](https://www.statgraphics.com/at-your-request#controlchartsfornon-normaldata)

[Rich Edit Tables](https://www.statgraphics.com/at-your-request#richedittables)

[Automatic Selection of Distributions](https://www.statgraphics.com/at-your-request#automaticselectionofdistributions)

[Consolidated System Preferences](https://www.statgraphics.com/at-your-request#consolidatedsystempreferences)

[Improved Contour Plots](https://www.statgraphics.com/at-your-request#improvedcontourplots)

[Multivariate Capability Analysis](https://www.statgraphics.com/at-your-request#multivariatecapabilityanalysis)

### Graphical ANOVA

The second edition of Statistics for Experimenters by Box, Hunter and Hunter (Wiley, 2005) includes a new method for displaying the results of an analysis of variance:

![graphicalanova](https://www.statgraphics.com/hubfs/website-assets/graphicalanova.jpg)The deviations of each factor mean from the grand mean are scaled so that their spread may be compared to that of the residuals. Factor levels separated by more than that exhibited in the residual distribution correspond to significant differences.

### Multiple Data Sheets

The data window may now hold data from up to 10 different data sources. Sheets may be linked to STATGRAPHICS data files, Excel workbooks, ASCII files, or ODBC-accessible databases. Sheets may be set to read-only if desired and polled periodically to update the analyses:

![datasheet](https://www.statgraphics.com/hubfs/website-assets/datasheet.jpg)

### Cuscore Charts

Statistical Control by Monitoring and Feedback Adjustment by Box and Luceno (Wiley, 1997) describes a method for constructing control charts that are designed to detect specific types of process disturbances. For example, the chart below is designed to detect a sine wave with a period of 12:

![cuscore](https://www.statgraphics.com/hubfs/website-assets/cuscore.jpg)

### Rich Edit Tables

All text is now displayed in rich edit tables so that numeric values may be easily pasted into applications such as Microsoft Excel. Significant results may be highlighted in red by the StatAdvisor:

![anovatable](https://www.statgraphics.com/hubfs/website-assets/anovatable.jpg)

### Bayesian Neural Network Classifier

The Bayesian neural network classifies observations into groups. It begins with prior probabilities for each group and combines those priors with information from the data:

![neuralnet-1](https://www.statgraphics.com/hubfs/website-assets/neuralnet-1.jpg)

### Control Charts for Non-Normal Data

Both the Capability Analysis and Control Charts procedures now allow you to handle non-normal data by either of 2 methods:

1. Transforming the data 
2.  Selecting one of 26 alternative distributions

The charts are displayed in the original metric:

![forecasting-1](https://www.statgraphics.com/hubfs/website-assets/forecasting-1.jpg)

### Automatic Selection of Distributions

Up to 45 probability distributions may be fit to a sample of data and sorted according to their goodness of fit:

![distfits](https://www.statgraphics.com/hubfs/website-assets/distfits.jpg)

### Consolidated System Preferences

System preferences have been consolidated on a single tabbed dialog box:

![preferences](https://www.statgraphics.com/hubfs/website-assets/preferences.jpg)

### Improved Contour Plots

Color ramps may now be used to create contour plots and contoured surfaces:

![surface](https://www.statgraphics.com/hubfs/website-assets/surface.jpg)

### Multivariate Capability Analysis

For processes with correlated variables, the joint probability of being out of spec on one or more variables can now be calculated:

![mvcapable](https://www.statgraphics.com/hubfs/website-assets/mvcapable.jpg)

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