IBM® SPSS® Statistics is one of the world’s leading statistical software. It enables you to quickly dig deeper into your data, making it a much more effective tool than spreadsheets, databases or standard multi-dimensional tools for analysts. SPSS Statistics excels at making sense of complex patterns and associations—enabling users to draw conclusions and make predictions.
IBM® SPSS® Statistics software delivers a powerful set of statistical features that enable your organization to make the most of the valuable information your data provides. By digging deeper into your data, you can discover information to improve decision making—ultimately expanding markets, improving research outcomes, enabling regulatory compliance, managing risk and maximizing ROI to name a few.
To easily access the IBM SPSS Statistics features, we split them in categories, following the IBM SPSS Statistics online purchasing plan model. For more information on purchasing the features of interest, we recommend that you view our pricing plans or contact a sales representative.
SPSS Base subscription features
The IBM SPSS Base edition offers robust data management and visualization tools, and advanced statistical analytics capabilities like descriptive statistics, linear regression, bivariate statistics techniques and integration with R and Python.
Custom Tables and Advanced Statistics features
The features presented under Custom Tables and Advanced Statistics group allow users to easily design and share interactive tables. You can analyze data more comprehensively with non-linear, logistic, 2-stage least squares regression, generalized linear modeling and survival analysis.
Forecasting and Decision Trees features
The features included in the Forecasting and Decision Trees group provide ARIMA (AutoRegressive Integrated Mobing Average) and exponential smoothing forecasting capabilities. Build decision trees through IBM's four established tree-growing algorithms. You can also create neural network predictive models as well as perform RFM analysis to test marketing campaigns.
Complex Sampling and Testing features
You can analyze small sample sizes, handle missing data, and do complex sampling. You can employ regression with optimal scaling and techniques like lasso and elastic net, and use features like categorical principal components analysis, multidimensional scaling and unfolding, and multiple correspondence analysis.