Minitab v22.1 + Fix [AppDoze]

Minitab Overview

Harness the power of statistics. Data is everywhere, but are you truly taking advantage of yours? Minitab Statistical Software can look at current and past data to discover trends, find and predict patterns, uncover hidden relationships between variables, and create stunning visualizations to tackle even the most daunting challenges and opportunities. With powerful statistics, industry-leading data analytics, and dynamic visualizations on your side, the possibilities are endless.
Features of Minitab

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

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