Minitab includes all the statistics and graphs needed for beginning through advanced courses within a user-friendly design that makes it easy for you to analyze data. Used by more than 9 out of 10 of the top Fortune 500 companies, Minitab helps prepare students for a data-driven world.
Minitab is available in eight languages–English, Simplified Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.
Minitab is the leading statistical software used in education worldwide because it is:
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Easy to use - Minitab has a highly intuitive interface designed to complement how people learn statistics.
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Comprehensive - Minitab contains a complete set of sound, peer-reviewed methods; you do not need to buy, learn, or maintain separate add-ins or modules
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Compatible with any curriculum - The organization of statistics and graphs in Minitab parallels the approach used in virtually all leading textbooks.
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Widely used in business and industry - Students who learn statistics with Minitab acquire real-world skills employers want. Thousands of companies in more than 100 countries use Minitab, and they recruit graduates who do, too.
Minitab's latest release is now available through both desktop app and cloud-enabled web app, which means you can access it from anywhere on nearly any device. Log in to Minitab whether you're in class virtually or in-person. Other newly added features include heatmaps, parallel plots and binned scatterplots.
Minitab Features List
* New or Improved
Assistant
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Measurement systems analysis
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Capability analysis
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Graphical analysis
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Hypothesis tests
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Regression
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DOE
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Control charts
Graphics
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Scatterplots, matrix plots, boxplots, dotplots, histograms, charts, time series plots, etc.
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Contour and rotating 3D plots
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Probability and probability distribution plots
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Automatically update graphs as data change
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Brush graphs to explore points of interest
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Export: TIF, JPEG, PNG, BMP, GIF, EMF
Basic Statistics
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Descriptive statistics
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One-sample Z-test, one- and two-sample t-tests, paired t-test
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One and two proportions tests
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One- and two-sample Poisson rate tests
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One and two variances tests
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Correlation and covariance
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Normality test
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Outlier test
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Poisson goodness-of-fit test
Regression
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Linear and nonlinear regression
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Binary, ordinal and nominal logistic regression
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Stability studies
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Partial least squares
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Orthogonal regression
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Poisson regression
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Plots: residual, factorial, contour, surface, etc.
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Stepwise: p-value, AICc, and BIC selection criterion
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Best subsets
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Response prediction and optimization
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Validation for Regression and Binary Logistic Regression*
Analysis of Variance
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ANOVA
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General linear models
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Mixed models
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MANOVA
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Multiple comparisons
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Response prediction and optimization
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Test for equal variances
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Plots: residual, factorial, contour, surface, etc.
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Analysis of means
Measurement Systems Analysis
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Data collection worksheets
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Gage R&R Crossed
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Gage R&R Nested
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Gage R&R Expanded
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Gage run chart
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Gage linearity and bias
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Type 1 Gage Study
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Attribute Gage Study
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Attribute agreement analysis
Quality Tools
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Run chart
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Pareto chart
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Cause-and-effect diagram
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Variables control charts: XBar, R, S, XBar-R, XBar-S, I, MR, I-MR, I-MR-R/S, zone, Z-MR
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Attributes control charts: P, NP, C, U, Laney P’ and U’
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Time-weighted control charts: MA, EWMA, CUSUM
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Multivariate control charts: T2, generalized variance, MEWMA
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Rare events charts: G and T
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Historical/shift-in-process charts
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Box-Cox and Johnson transformations
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Individual distribution identification
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Process capability: normal, non-normal, attribute, batch
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Process Capability SixpackTM
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Tolerance intervals
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Acceptance sampling and OC curves
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Multi-Vari chart
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Variability Chart*
Design of Experiments
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Definitive screening designs
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Plackett-Burman designs
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Two-level factorial designs
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Split-plot designs
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General factorial designs
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Response surface designs
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Mixture designs
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D-optimal and distance-based designs
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Taguchi designs
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User-specified designs
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Analyze binary responses
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Analyze variability for factorial designs
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Botched runs
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Effects plots: normal, half-normal, Pareto
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Response prediction and optimization
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Plots: residual, main effects, interaction, cube, contour, surface, wireframe
Reliability/Survival
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Parametric and nonparametric distribution analysis
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Goodness-of-fit measures
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Exact failure, right-, left-, and interval-censored data
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Accelerated life testing
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Regression with life data
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Test plans
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Threshold parameter distributions
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Repairable systems
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Multiple failure modes
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Probit analysis
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Weibayes analysis
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Plots: distribution, probability, hazard, survival
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Warranty analysis
Power and Sample Size
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Sample size for estimation
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Sample size for tolerance intervals
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One-sample Z, one- and two-sample t
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Paired t
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One and two proportions
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One- and two-sample Poisson rates
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One and two variances
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Equivalence tests
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One-Way ANOVA
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Two-level, Plackett-Burman and general full factorial designs
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Power curves
Predictive Analytics*
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CART® Classification*
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CART® Regression*
Multivariate
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Principal components analysis
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Factor analysis
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Discriminant analysis
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Cluster analysis
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Correspondence analysis
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Item analysis and Cronbach’s alpha
Time Series and Forecasting
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Time series plots
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Trend analysis
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Decomposition
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Moving average
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Exponential smoothing
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Winters’ method
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Auto-, partial auto-, and cross correlation functions
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ARIMA
Nonparametrics
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Sign test
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Wilcoxon test
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Mann-Whitney test
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Kruskal-Wallis test
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Mood’s median test
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Friedman test
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Runs test
Equivalence Tests
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One- and two-sample, paired
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2x2 crossover design
Tables
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Chi-square, Fisher’s exact, and other tests
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Chi-square goodness-of-fit test
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Tally and cross tabulation
Simulations and Distributions
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Random number generator
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Probability density, cumulative distribution, and inverse cumulative distribution functions
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Random sampling
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Bootstrapping and randomization tests
Macros and Customization
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Customizable menus and toolbars
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Extensive preferences and user profiles
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Powerful scripting capabilities
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Python integration*