IBM SPSS Statistics Base

IBM SPSS Statistics Base forms the basis of many deployments with statistical tests and procedures that are fundamental to many analyses.

  • Overview
  • Features

You can take the analytical process from start to finish with IBM SPSS Statistics Base. In addition to the data preparation, data management, output management and charting features now available in all IBM SPSS Statistics modules, IBM SPSS Statistics Base offers the most frequently used procedures for statistical analysis that are the foundation for many analyses.

IBM SPSS Statistics Base contains many fundamental procedures analysts need.
IBM SPSS Statistics Base contains many fundamental procedures analysts need.

IBM SPSS Statistics Base contains many fundamental procedures analysts need.



The procedures within IBM SPSS Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry out a number of procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.

In IBM SPSS Statistics Base 18, you'll find the following enhancements:

  • New nonparametric tests allow multiple comparisons and operate on large datasets more efficiently. Tests include: Chi-square, binomial, runs, one-sample, two independent samples, k-independent samples, two related samples, k-related samples and descriptives.
  • Charts for statistical process control tests now include rule-checking on secondary charts

Feature Demonstrations

Bootstrapping in IBM SPSS Statistics

Bootstrapping in IBM SPSS Statistics

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The fundamental procedures within IBM SPSS Statistics Base allow you to get an understanding of the basic structure of your data and formulate hypotheses for additional testing, and then carry out a number of procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.

A number of tests and procedures are multithreaded for improved performance, including the SORT operation, plus correlation, partial correlation, linear regression, and factor analysis procedures.

Descriptive Statistics

  • Crosstabulations
  • Frequencies
  • Descriptives
  • Descriptive ratio statistics
  • Compare means
  • ANOVA and ANCOVA
  • Correlation
  • Nonparametric tests
  • Explore

Tests to Predict Numerical Outcomes and Identify Groups

  • Factor Analysis
  • K-means Cluster Analysis
  • Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only
  • Optionally, specify variable whose values are used to label casewise output and request analysis of variance F statistics
  • Hierarchical Cluster Analysis
  • TwoStep Cluster Analysis
  • Discriminant
  • Linear Regression
  • Ordinal regression—PLUM
  • Nearest Neighbor analysis
Learn more about Statistics Family