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MINITAB Statistical Software is the ideal package for Six Sigma and other quality improvement projects. From Statistical Process Control to Design of Experiments, it offers you the methods you need to implement every phase of your quality project, along with features like StatGuide and ReportPad that help you understand and communicate your results. No package is more accurate, reliable, or easy to use. In addition to more statistical power than our previous release, MINITAB offers many exciting new features such as: A powerful new graphics engine that delivers engaging results that offer tremendous insight into your data An effortless method to create, edit, and update graphs The ability to customize your menus and toolbars so you can conveniently access the methods you use most. Feature List * New or Improved Assistant Measurement Systems Analysis * Capability Analysis * Graphical Analysis * Hypothesis Tests * Regression * DOE * Control Charts * 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 Graphics
Easily create professional-looking graphics * Scatterplots, matrix plots, boxplots, dotplots, histograms, charts, time series plots, etc. Bubble plot * Contour and rotating 3D plots Probability and probability distribution plots Edit attributes: axes, labels, reference lines, etc. Interactively recreate custom graphs with new data Easily place multiple graphs on one page Automatically update graphs as data change Brush graphs to explore points of interest Export: TIF, JPEG, PNG, BMP, GIF, EMF Regression
Linear regression * Binary, ordinal and nominal logistic regression * Nonlinear regression Stability studies * Orthogonal regression Partial least squares Poisson regression * Plots: residual, factorial, contour, surface, etc. * Stepwise and best subsets * Response prediction and optimization Analysis of Variance
ANOVA * General Linear Model * MANOVA * Multiple comparisons * Response prediction and optimization * Test for equal variances * Plots: residual, factorial, contour, surface, etc. * Analysis of means Statistical Process Control
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 for multiple variables Process Capability SixpackTM * Tolerance intervals * Acceptance sampling and OC curves Measurement Systems Analysis
Data collection worksheets Gage R&R Crossed: ANOVA and Xbar-R methods * Gage R&R Nested Gage R&R Expanded * Misclassification probabilities Gage run chart Gage linearity and bias Type 1 Gage Study Attribute Gage Study – AIAG analytic method Attribute agreement analysis
Design of Experiments
Two-level factorial designs * Split-plot designs * General factorial designs * Plackett-Burman designs * Response surface designs * Mixture designs D-optimal and distance-based designs Taguchi designs User-specified designs 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 ML and least squares estimates * Exact failure, right-, left-, and interval-censored data Accelerated life testing Regression with life data Reliability test plans Threshold parameter distributions Repairable systems Multiple failure modes Probit analysis Weibayes analysis Hypothesis tests on distribution parameters 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 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 and 2x2 crossover design * Tables Chi-square, Fisher’s exact, and other tests * Chi-square goodness-of-fit test Tally individual variables Simulations and Distributions
Random number generator Density, cumulative distribution, and inverse cumulative distribution functions Random sampling Macros and Customization
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