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.
Its ease of use, flexibility and scalability make SPSS accessible to users of all skill levels. What’s more, it’s suitable for projects of all sizes and levels of complexity, and can help you find new opportunities, improve efficiency and minimize risk.
Within the SPSS software family of products, IBM SPSS Statistics supports a top-down, hypothesis testing approach to your data, while IBM SPSS Modeler exposes patterns and models hidden in data through a bottom-up, hypothesis generation approach.
Prepare and analyze data with an easy-to-use interface without having to write code.
Choose from purchase options including subscription and traditional licenses.
Empower coders, noncoders and analysts with visual data science tools.
SPSS Statistics solves business and research problems using ad hoc analysis, hypothesis testing, geospatial analysis and predictive analytics.
SPSS Modeler helps you tap into data assets and modern applications, with algorithms and models that are ready for immediate use.
SPSS Modeler is available on IBM Cloud Pak for Data. Take advantage of SPSS Modeler on the public cloud.
Learn how to use linear regression analysis to predict the value of a variable based on the value of another variable.
Learn how logistic regression estimates the probability of an event occurring, based on a dataset of independent variables.
Learn about new statistical procedures, data visualization tools and other improvements in SPSS Statistics 29.
Get technical tips and insights from other SPSS users.
Gain new perspective through expert guidance.
Find support resources for SPSS Statistics.
Gain affordable access to best-in-class statistical software with a single-user license for students and teachers.
Manage analytical assets, automate processes and share results more efficiently and securely.
Get descriptive and predictive analytics, data preparation and real-time scoring.
Use structural equation modeling (SEM) to test hypotheses and gain new insights from data.
Create a platform that can make predictive analytics easier for big data.