Minitab is the unmatched, all-in-one data analysis and statistics software for everyone that lets data be used for what it is worth. It is not only a leader in providing statistical software and services for quality improvement, education and research applications, but also a world-leading software tool for quality management and Six Sigma implementation, as well as a good tool for continuous quality improvement. It can analyze data to identify problems and meaningful solutions when users encounter the most intractable business problems.

Minitab mainly provides statistical analysis, visualization analysis, prediction analysis and improvement analysis to support data-driven decisions; can analyze data sets of various sizes through a simplified user interface and powerful features; can easily synthesize larger volumes of data; can guide users through the entire analysis; can even help you display and explain the results. In addition, it also supports scatter chart, bubble chart, box chart, dot chart, column diagram, table chart, time series chart, and other visual graphics outputs, and when the data changes, the graph can be updated seamlessly.

As a leader in the field of modern quality management and statistics, and the common language of the global Six Sigma implementation, Minitab is favored by mass quality scholars and statistics experts around the world for its incomparable powerful functions and simple and visualized operation. Since it was founded in the Pennsylvania State University in United States in 1972, it has been widely used in more than 100 countries, more than 4800 universities (its customers include the world’s leading enterprises and academies), and has been cited in more than 500 textbooks.

Minitab is so easy to use that it requires almost no specialized learning and training for users at any level. Instead, it takes us just a few minutes to get started. This is one of its irreplaceable advantages! In summary, Minitab can help us analyze larger data sets faster, better, more accurately, and more easily.

// Key Features //

Feature	Description
Assistant	Measurement systems analysis Capability analysis Graphical analysis Hypothesis tests Regression DOE Control charts
Graphics	Binned scatterplots, boxplots, charts, correlograms, dotplots, heatmaps, histograms, matrix plots, parallel plots, scatterplots, time series plots, etc. Contour and rotating 3D plots Probability and probability distribution plots Automatically update graphs as data change Brush graphs to explore points of interest Export: TIF, JPEG, PNG, BMP, GIF, EMF
Basic Statistics	Descriptive statistics One-sample Z-test, one- and two-sample t-tests, paired t-test One and two proportions tests One- and two-sample Poisson rate tests One and two variances tests Correlation and covariance Normality test Outlier test Poisson goodness-of-fit test
Regression	Linear regression Nonlinear regression Binary, ordinal and nominal logistic regression Stability studies Partial least squares Orthogonal regression Poisson regression Plots: residual, factorial, contour, surface, etc. Stepwise: p-value, AICc, and BIC selection criterion Best subsets Response prediction and optimization Validation for Regression and Binary Logistic Regression
Analysis of Variance	ANOVA General linear models Mixed models MANOVA Multiple comparisons Response prediction and optimization Test for equal variances Plots: residual, factorial, contour, surface, etc. Analysis of means
Measurement Systems Analysis	Data collection worksheets Gage R&R Crossed Gage R&R Nested Gage R&R Expanded Gage run chart Gage linearity and bias Type 1 Gage Study Attribute Gage Study Attribute agreement analysis
Quality Tools	Run chart Pareto chart Cause-and-effect diagram Variables control charts: XBar, R, S, XBar-R, XBar-S, I, MR, I-MR, I-MR-R/S, zone, Z-MR Attributes control charts: P, NP, C, U, Laney P’ and U’ Time-weighted control charts: MA, EWMA, CUSUM Multivariate control charts: T2, generalized variance, MEWMA Rare events charts: G and T Historical/shift-in-process charts Box-Cox and Johnson transformations Individual distribution identification Process capability: normal, non-normal, attribute, batch Process Capability SixpackTM Tolerance intervals Acceptance sampling and OC curves Multi-Vari chart Variability chart
Design of Experiments	Definitive screening designs Plackett-Burman designs Two-level factorial designs Split-plot designs General factorial designs Response surface designs Mixture designs D-optimal and distance-based designs Taguchi designs User-specified designs Analyze binary responses Analyze variability for factorial designs Botched runs Effects plots: normal, half-normal, Pareto Response prediction and optimization Plots: residual, main effects, interaction, cube, contour, surface, wireframe
Reliability/Survival	Parametric and nonparametric distribution analysis Goodness-of-fit measures Exact failure, right-, left-, and interval-censored data Accelerated life testing Regression with life data Test plans Threshold parameter distributions Repairable systems Multiple failure modes Probit analysis Weibayes analysis Plots: distribution, probability, hazard, survival Warranty analysis
Power and Sample Size	Sample size for estimation Sample size for tolerance intervals One-sample Z, one- and two-sample t Paired t One and two proportions One- and two-sample Poisson rates One and two variances Equivalence tests One-Way ANOVA Two-level, Plackett-Burman and general full factorial designs Power curves
Predictive Analytics	CART Classification CART Regression Random Forests Classification Random Forests Regression TreeNet Classification TreeNet Regression
Multivariate	Principal components analysis Factor analysis Discriminant analysis Cluster analysis Correspondence analysis Item analysis and Cronbach’s alpha
Time Series and Forecasting	Time series plots Trend analysis Decomposition Moving average Exponential smoothing Winters’ method Auto-, partial auto-, and cross correlation functions ARIMA
Nonparametrics	Sign test Wilcoxon test Mann-Whitney test Kruskal-Wallis test Mood’s median test Friedman test Runs test
Equivalence Tests	One- and two-sample, paired 2×2 crossover design
Tables	Chi-square, Fisher’s exact, and other tests Chi-square goodness-of-fit test Tally and cross tabulation
Simulations and Distributions	Random number generator Probability density, cumulative distribution, and inverse cumulative distribution functions Random sampling Bootstrapping and randomization tests
Macros and Customization	Customizable menus and toolbars Extensive preferences and user profiles Powerful scripting capabilities Python integration R integration

// Official Demo Video //

// System Requirements //

• Microsoft Visual C++ 2015-2019 Redistributable Packages

// Edition Statement //

AppNee provides the Minitab multilingual full installers and unlocked files, as well as pre-activated full installers for Windows 64-bit only.

// Installation Notes //

for v20.3.0+ on Windows:

1. Download and install Minitab as trial
2. Copy the unlocked files to installation folder and overwrite
3. Done

*** You may need to block this program with your firewall.

// Related Links //

• Minitab Official Video Tutorials
• Minitab Online Help

// Download URLs //

Version	Download	Size
for Windows
v22.2.0		284 MB

(Homepage)