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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:
SPSS Statistics is available for Windows and Mac operating systems.
Perform powerful analysis and easily build visualizations and reports through a point-and-click interface, and without any coding experience.
Reduce data preparation time by identifying invalid values, viewing patterns of missing data and summarizing variable distributions.
/igi-4-download-for-pc.html. 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.
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.
SPSS Statistics 27: New releaseSerial number magic lines addicting.
Learn about new statistical algorithms, productivity and feature enhancements in the new release that boost your analysis.
IBM SPSS Statistics tutorial
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.
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.

