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:
Bayesian Neural Network Classifier
Control Charts for Non-Normal Data
Automatic Selection of Distributions
Consolidated System Preferences
Multivariate Capability Analysis
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:
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.
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:
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:
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:
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:
Both the Capability Analysis and Control Charts procedures now allow you to handle non-normal data by either of 2 methods:
The charts are displayed in the original metric:
Up to 45 probability distributions may be fit to a sample of data and sorted according to their goodness of fit:
System preferences have been consolidated on a single tabbed dialog box:
Color ramps may now be used to create contour plots and contoured surfaces:
For processes with correlated variables, the joint probability of being out of spec on one or more variables can now be calculated:
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