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A statistical toolkit that provides effective support for every step of data analysis, from data preparation to data manipulation and modelling.

The CLEMSTAT solution is a statistical toolkit, complemented with industry-specific syntax and macro sets, which allow you to perform industry-specific analyses in market research and science, as well as for the financial, telecom, and government sectors. With built-in case studies and a 'Statistics Coach', CLEMSTAT provides effective support at every step of data analysis, from data preparation to data manipulation and modelling.

CLEMSTAT includes a combination of the following IBM SPSS Statistics modules on demand:

  • SPSS Statistics Base
  • Regression: allows you to build much more accurate predictive models that go beyond traditional linear regression approximations
  • Advanced Statistics: allows complex relationships to be explored using powerful multivariate techniques
  • Decision Trees: visual classification, decision trees to identify individual subgroups and explore relationships in the data
  • Custom Tables: display results in a presentation-ready tabular format with a user-friendly interface
  • Exact Tests: more accurate analyses on small samples and rare occurrences in large databases
  • Categories: detect unknown relationships between categorical data using efficient scaling and dimensionality reduction techniques
  • Forcasting: forecasting by analysing historical data, modelling, and using powerful time series analysis techniques
  • Conjoint: analysing consumer preferences for more effective product development
  • Missing Values: solving missing data problems by quickly diagnosing the situation and managing the data accordingly for more robust results
  • Data Preparation: lightweight, automated data validation instead of labor-intensive manual verification for more accurate results
  • Complex Samples: designing and analysing non-random surveys and studying the population efficiently and accurately
  • Direct Marketing: : includes the tools needed for a sophisticated analysis of customer data, such as RFM analysis, segmentation, profiling.
  • Bootstrapping: helps to reduce the effects of outliers and anomalies that would reduce the accuracy and applicability of analyses.
  • Neural Networks: enables non-linear modelling processes to explore more complex data relationships than ever before.