Find More Complex Relationships in your Data

A new addition with SPSS 16.0, SPSS Neural Networks offers non-linear data modeling procedures that enable you to discover more complex relationships in your data. Using these procedures, you can develop more accurate and effective predictive models. The result? Deeper insight and better decision-making.

The procedures in SPSS Neural Networks complement the more traditional statistics in SPSS Base and its modules. Find new associations in your data with SPSS Neural Networks and then confirm their significance with traditional statistical techniques.

What is a neural network?

A computational neural network is a set of non-linear data modeling tools consisting of input and output layers plus one or two hidden layers. The connections between neurons in each layer have associated weights, which are iteratively adjusted by the training algorithm to minimize error and provide accurate predictions.

In an MLP procedure like the one shown here, nodes in the input and output layers are connected to nodes in one or more hidden layers.

How can you use SPSS Neural Networks?

You can combine SPSS Neural Networks with other statistical procedures to gain clearer insight in a number of areas:

• Market research
  • Create customer profiles
  • Discover customer preferences
• Database marketing
  • Segment your customer base
  • Optimize campaigns
• Financial analysis
  • Analyze applicants’ creditworthiness
  • Detect possible fraud
• Operational analysis
  • Manage cash flow
  • Improve logistics planning
• Healthcare
  • Forecast treatment costs
  • Perform medical outcomes analysis