Additionally a "macro" language can be used to write command language subroutines. A Python programmability extension can access the information in the data dictionary and data and dynamically build command syntax programs. The Python programmability extension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the Python extension allows SPSS to run any of the statistics in the free software package R. From version 14 onwards, SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins". (From Version 20 onwards, these two scripting facilities, as well as many scripts, are included on the installation media and are normally installed by default.)
SPSS Statistics can read and write data from ASCII text files (including hierarchical files), other statistics packages, spreadsheets and databases. SPSS Statistics can read and write to external relational database tables via ODBC and SQL.
SPSS Statistics launched version 25 on Aug 08, 2017. SPSS v25 adds new and advanced statistics, such as random effects solution results (GENLINMIXED), robust standard errors (GLM/UNIANOVA), and profile plots with error bars within the Advanced Statistics and Custom Tables add-on. V25 also includes new Bayesian Statistics capabilities, a method of statistical inference and publication ready charts, such as powerful new charting capabilities, including new default templates and the ability to share with Microsoft Office applications.
IBM SPSS Decision Trees enables you to explore results and visually determine how your model flows. This helps you find specific subgroups and relationships that you might not uncover using more traditional statistics. The module includes four established tree-growing algorithms.
Using the procedures in IBM SPSS Neural Networks, you can develop more accurate and effective predictive models. The result? Deeper insight and better decision making.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.Complement traditional statistical techniquesThe procedures in IBM SPSS Neural Networks complement the more traditional statistics in IBM SPSS Statistics Base and its modules. Find new associations in your data with Neural Networks and then confirm their significance with traditional statistical techniquesHow can you use IBM SPSS Neural Networks?You can combine Neural Networks with other statistical procedures to gain clearer insight in a number of areas:
Expand the capabilities of IBM SPSS Statistics Base for the data analysis stage in the analytical process. Using IBM SPSS Exact Tests with IBM SPSS Statistics Base gives you an even wider range of statistics, so you can get the most accurate response when: 2b1af7f3a8