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SPSS Statistics

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Discover how advanced statistical procedures help ensure quality, high-accuracy decision making
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From data to insights: What's new in IBM SPSS Statistics 30.0.0.

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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.

Key features

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User experience Data visualization Regression

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.

Statistics Base Data Preparation Bootstrapping

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.

Regression Advanced Statistics Custom Tables

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.

Forecasting Decision Trees Direct Marketing Neural Networks

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.

Complex Samples Conjoint Exact Tests Missing Values Categories
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