June 16 2021

Minitab 20.3 Multilingual

Minitab 20.3 Multilingual
Windows x64 | Languages: Multilingual | File Size: 246.33 MB


Visualize, analyze and harness the power of your data to solve your toughest challenges and eliminate mistakes before they happen. Data is everywhere these days, but are you truly taking advantage of yours Minitab Statistical Software can look at current and past data to find trends and predict patterns, uncover hidden relationships between variables, visualize data interactions and identify important factors to answer even the most challeg of questions. Visualizations are good, but pair them with analytics to make them great. With the power of statistics and data analysis on your side, the possibilities are endless.

Regardless of statistical background, Minitab can empower all parts of an organization to predict better outcomes, design better products and improve processes to generate higher revenues and reduce costs. Only Minitab offers a unique, integrated approach by providing software and services that drive business excellence now from anywhere thanks to the cloud. Key statistical tests include t tests, one and two proportions, normality test, chi-square and equivalence tests.

Access modern data analysis and explore your data even further with our advanced analytics and open source integration. Skillfully predict, compare alternatives and forecast your business with ease using our revolutionary predictive analytics techniques. Use classical methods in Minitab Statistical Software, integrate with open-source languages R or Python, or boost your capabilities further with machine learning algorithms like Classification and Regression Trees (CART) or TreeNet and Random Forests, now available in Minitab's Predictive Analytics Module.

Seeing is believing. Visualizations can help communicate your findings and achievements through correlograms, binned scatterplots, bubble plots, boxplots, dotplots, histograms, heatmaps, parallel plots, series plots and more. Graphs seamlessly update as data changes and our cloud-enabled web app allows for secure analysis sharing with lightning speed.

Measurement systems analysis

Capability analysis

Graphical analysis

Hypothesis tests



Control charts

Binned scatterplots*, boxplots, charts, correlograms*, dotplots, heatmaps*, histograms, matrix plots, parallel plots*, scatterplots, 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


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

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*


General linear models

Mixed models


Multiple comparisons

Response prediction and optimization

Test for equal variances

Plots: residual, factorial, contour, surface, etc.

Analysis of means

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

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'

-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

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

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

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


Two-level, Plackett-Burman and general full factorial designs

Power curves

CART Classification

CART Regression

Random Forests Classification*

Random Forests Regression*

TreeNet Classification*

TreeNet Regression*

Principal components analysis

Factor analysis

Discriminant analysis

Cluster analysis

Correspondence analysis

Item analysis and Cronbach's alpha

series plots

Trend analysis


Moving average

Exponential smoothing

Winters' method

Auto-, partial auto-, and cross correlation functions


Sign test

Wilcoxon test

Mann-Whitney test

Kruskal-Wallis test

Mood's median test

Friedman test

Runs test

One- and two-sample, paired

2x2 crossover design

Chi-square, Fisher's exact, and other tests

Chi-square goodness-of-fit test

Tally and cross tabulation

Random number generator

Probability density, cumulative distribution, and inverse cumulative distribution functions

Random sampling

Bootstrapping and randomization tests

Customizable menus and toolbars

Extensive preferences and user profiles

Powerful scripting capabilities

Python integration

R integration

Operating System: Windows 8.1, Windows 10 (64-bit)

RAM: 64-bit systems: 4 GB of memory or more recommended

Processor: Intel Pentium 4 or AMD Athlon Dual Core, with SSE2 technology

Hard Disk Space: 2 GB (minimum) free space available

Screen Resolution: 1024 x 768 or higher

Browser: A web browser is required for Minitab Help.
Chinese, English, French, German, Japanese, Korean, Portuguese, Spanish




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