The IBM® SPSS® software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open source extensibility, integration with big data and seamless deployment into applications.
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World they almost always are for UCS-2/UTF-16 files, and often are for UTF-8 files. Thefile utility will not even recognize UCS-2 files without a BOM, but many other utilities will refuse.
Its ease of use, flexibility and scalability make SPSS accessible to users of all skill levels. What’s more, its suitable for projects of all sizes and levels of complexity, and can help you and your organization find new opportunities, improve efficiency and minimize risk.
Within the SPSS software family of products, SPSS Statistics supports a top-down, hypothesis testing approach to your data while SPSS Modeler exposes patterns and models hidden in data through a bottom-up, hypothesis generation approach.
SPSS Modeler is also available on IBM Cloud Pak® for Data, a containerized data and AI platform that enables you to build and run predictive models anywhere — on any cloud and on premises. It can be added as a service by itself, or it is included as part of IBM Watson® Studio Premium, a suite of software tools designed to help you accelerate the building and scaling of predictive models.
Why IBM SPSS Statistics?
IBM® SPSS® Statistics is a powerful statistical software platform. It delivers a robust set of features that lets your organization extract actionable insights from its data.
With SPSS Statistics you can:
- Analyze and better understand your data, and solve complex business and research problems through a user friendly interface.
- Understand large and complex data sets quickly with advanced statistical procedures that help ensure high accuracy and quality decision making.
- Use extensions, Python and R programming language code to integrate with open source software.
- Select and manage your software easily, with flexible deployment options.
SPSS Statistics is available for Windows and Mac operating systems.
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A powerful statistical analysis software platform
Easy to use
Perform powerful analysis and easily build visualizations and reports through a point-and-click interface, and without any coding experience.
Efficient data conditioning
Reduce data preparation time by identifying invalid values, viewing patterns of missing data and summarizing variable distributions.
Quick and reliable
Analyze large data sets and prepare data in a single step with automated data preparation.
Run advanced and descriptive statistics, regression and more with an integrated interface. Plus, you can automate common tasks through syntax.
Open source integration
Enhance SPSS syntax with R and Python using a library of extensions or by building your own.
Store files and data on your computer rather than in the cloud with SPSS that’s installed locally.
Take a closer look at IBM SPSS Statistics
SPSS Statistics 27: New release
Learn about new statistical algorithms, productivity and feature enhancements in the new release that boost your analysis.
IBM SPSS Statistics tutorial
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Get hands-on experience with SPSS Statistics by analyzing a simple set of employee data and running a variety of statistical tests.
A leader in statistical analysis software
Learn why G2 Crowd named SPSS Statistics a Leader in Statistical Analysis Software for Winter 2020.
Explore advanced statistical procedures with SPSS Statistics
Use univariate and multivariate modeling for more accurate conclusions in analyzing complex relationships.
Predict categorical outcomes and apply nonlinear regression procedures.
Use classification and decision trees to help identify groups and relationships and predict outcomes.
Identify the right customers easily and improve campaign results.
Build time-series forecasts regardless of your skill level.
Discover complex relationships and improve predictive models.
Predict outcomes and reveal relationships using categorical data.
Analyze statistical data and interpret survey results from complex samples.
Understand and measure purchasing decisions better.
Reach more accurate conclusions with small samples or rare occurrences.
Uncover missing data patterns, estimate summary statistics and impute missing values.