Version 31 Features

• Advanced statistical analyses: including regression, analysis of variance, nonparametric tests, factor analysis, cluster analysis, and predictive modeling.
• Proximity Mapping capability: For visualizing spatial or relational data, suitable for geographic or network analysis.
• New native algorithms: To increase the accuracy and speed of analyses on complex data.
• Data management: Ability to define variables, clean data, transform data, and run hypothesis tests automatically.
• Professional reporting: Generate statistical reports with charts, tables, and printable or saveable output.
• Support for specialized modules: such as survival analysis, multivariate analysis, linear and nonlinear modeling, time series analysis.

Common uses

• Academic and scientific research: questionnaire data analysis, statistical tests, and hypothesis modeling.
• Market and business research: customer behavior analysis, sales forecasting, market segmentation.
• Medicine and health: evaluating the effectiveness of treatments, analyzing clinical data.
• Engineering and industry: quality control, process capability analysis.

IBM SPSS Statistics 31 is a powerful statistical analysis software widely used by researchers, data analysts, and business professionals. This version introduces enhanced capabilities for data modeling, visualization, and predictive analytics

Key Features in Version 31

Advanced Statistical Procedures: Includes regression, ANOVA, non-parametric tests, factor analysis, clustering, and predictive modeling
Proximity Mapping: Visualize spatial or relational data, ideal for geographic or network analysis
New Native Algorithms: Improved accuracy and speed for complex data analysis
Data Management Tools: Define variables, clean data, transform datasets, and automate hypothesis testing
Professional Reporting: Generate statistical reports with charts, tables, and exportable outputs
Modular Expansion: Supports specialized modules like survival analysis, multivariate analysis, linear/nonlinear modeling, and time series forecasting

Common Applications

Academic Research: Survey analysis, hypothesis testing, and statistical modeling
Market & Business Analytics: Customer behavior analysis, sales forecasting, market segmentation
Healthcare & Medicine: Clinical data analysis, treatment effectiveness studies
Engineering & Industry: Quality control, process capability analysis