Feature List (Windows Version) * New or Improved in Minitab 21 Assistant Measurement systems analysis Capability analysis Graphical analysis Hypothesis tests Regression DOE Control charts Graphics Graph Builder* 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 Cox 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 Predictive Analytics module 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 2x2 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 Chargeable, additional Modules Predictive Analytics Module* Automated Machine Learning* Random Forests® Classification Random Forests® Regression TreeNet® Classification TreeNet® Regression Healthcare Module*
