What's New in SPSS 16.0: Add-on Modules
Enhancements to the add-on modules in SPSS 16.0 enable you to explore data in new ways, leading to more reliable predictive models. The following modules complement other SPSS add-on modules, allowing you to add
functionality as you need it.
In addition to an entirely new module offering data mining techniques to SPSS users, SPSS 16.0 includes enhancements to SPSS Advanced Models and SPSS Complex Samples.
NEW: SPSS Neural Networks
With this release, SPSS Inc. introduces a new module to the SPSS Family. SPSS Neural Networks 16.0 provides a complementary approach to the statistical techniques available in SPSS Base and other add-on modules. From
the familiar SPSS interface, use SPSS Neural Networks to mine data and discover more complex relationships than is possible using more traditional, linear statistical techniques.
Neural networks are non-linear statistical data mining tools that consist of input and output layers plus one or more hidden layers of unobservable nodes. In a neural network, the connections between neurons have
weights associated with them. By adjusting the connection weights during training to match predictions to target variables or specific records, the network "learns" to generate better and better predictions.
In an MLP network like the one shown here, the data feeds forward from the input layer through one or more hidden layers to the output layer.
With the SPSS Neural Networks module, you can choose either the Multilayer Perceptron (MLP) or Radial Basis Function (RBF) procedure to explore your data.
SPSS Advanced Models™ 16.0 enhancements
SPSS Advanced Models includes generalized linear models (GENLIN) and generalized estimating equations (GEE) procedures. These procedures help
address a wide range of statistical modeling problems. For example, they can be used to more accurately predict ordinal outcomes, such as customer satisfaction.
Enhancements available in SPSS 16.0 enable analysts to predict outcomes that are a combination of discrete and continuous outcomes—such as claim amounts—using a Tweedie distribution.
SPSS Complex Samples™ 16.0 enhancements
SPSS Complex Samples now includes the Cox Regression technique for time-to-event data. If you have data based on a complex sample design, you can
use this technique to accurately predict the time to a specific event—how long a high-value customer remains active, for example, or how long people fitting a
certain profile will survive a certain medical condition.
SPSS Complex Samples Cox Regression (CSCOXREG) enables you to more easily analyze differences in subgroups as well as the effects of a set of predictors. The procedure takes the sample design into account when
estimating variances, and can handle data involving multiple cases, such as multiple patient visits, encounters, and observations.
In addition, the Complex Samples Select (CSSELECT) routine is multithreaded, to improve performance on computers with multiple processors and multi-core processors.
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