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
VirusTotal Results:
Setup:
https://www.virustotal.com/gui/file/a08717d5de55da13376d5ac75d29a397911172fc35d58938817e9959353cf5be
Crack:
https://www.virustotal.com/gui/file/529215ba7b277a41edae5a1cb541329c0364530046186376fbb76fc0f9018690
https://www.virustotal.com/gui/file/fe1852374b693298eecaa13c676029f310fb2519eba317c992e14b2e167bdd69