Introduction
Minitab software is a modern quality management statistical software and a common language for the implementation of Six Sigma worldwide. Minitab was established in 1972 at Pennsylvania State University in the United States and has been widely used in over 120 countries and over 5000 universities worldwide.
Function
Minitab software is a pioneer in providing statistical software and services for quality improvement, education, and research applications. It is a globally leading quality management and Six Sigma implementation software tool.
Minitab 21 is a common language for the implementation of Six Sigma worldwide, and is highly favored by quality scholars and statistical experts for its unparalleled powerful functions and easy visualization operations.
It provides statistical analysis, visual analysis, predictive analysis, and improvement analysis to support data-driven decision-making. Whether or not you have statistical background knowledge, Minitab can pass.
Its easy-to-use software or statistical expert support network helps businesses better predict results, design better products, and create a better future.
Analyze datasets of all sizes through simplified interfaces and new powerful features
Interface changes
Manage and organize your project with the flexibility you need. With the new navigator feature, you can group results/analyses by worksheet and sort them alphabetically or run them in order. With the new split view feature, you can easily compare multiple analyses side by side.
Merge multiple windows
Graphics updated with data
Quickly send results
Optimization of Worksheet Window
Quickly view worksheet information
Navigator optimization
Command Line and History Optimization
New statistical function
New statistical features, stepwise regression, and normality enhancement for DOE (experimental design) can accelerate deep data analysis.
Five 'Resample' commands
Analyze binary response
Adding matrix graphs and confidence intervals
Fitting regression models with Pareto plots to evaluate whether predictive variables have a significant impact on response
Selecting models using AICc or BIC statistics
Enhancement of Normal Ability Analysis
Improved brush functionality
Faster analysis of massive data
