Minitab – Additional Modules
You can complement your Minitab software by the following optional, chargeable additional modules:
Predictive Analytics Module
With this module, users can extend the CART®technology already integrated in Minitab by the algorithms TreeNet® and Random Forest® ffor machine learning. Both tree-based algorithms are especially useful for mapping nonlinear relations and can handle fuzzy data, which other methods face difficulties with. Both algorithms, TreeNet as well as Random Forest, are very accurate and easy to interpret.
The Automated Machine Learning (AutoML) helps Minitab to select and display the model best suited from the algorithms CART (Classification and Regression Tree), TreeNet and Random Forest for predictive analytics.
The Healthcare module provides experts in healthcare and hospital management with a guided data analysis. The focus here lies on improving typical performance indicators. No statistical knowledge is necessary to select the appropriate analysis method.
The Insurance Module offers insurance experts a guided data analysis, so they can focus on improving key perfomance indicators without the need to learn additional statstics.
Measurement System Analysis Module
The Measurement System Analysis Module enables Minitab users to solve the most common MSA challenges without the need for additional statistical knowledge by providing a guided data analysis.
Sample Size Module
The Sample Size module provides a guided data analysis to estimate the required sample size to determine whether the analysis, which should be perfomed, will have enough power to meet the needs.
The additional, chargeable Minitab Reliability Module Module makes it easy for reliability engineers to navigate through complicated data analyses with its intuitive user interface and a vast variety of tools. Using advanced resources, Minitab's Reliability Module provides robust calculations, enabling accurate insights and informed decisions.
Research & Development Module
Minitab's Research & Development Module provides a comprehensive solution to improve the data-driven decision-making process. With its intuitive user interface and comprehensive tools included, the module enables research and development professionals to engage in complex data analysis.