Minitab 22.5.2

Minitab 22.5.2

Description: 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.

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
- 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

System Requirements
- Operating System: Windows 10 and higher (64-bit)
- RAM: 64-bit systems: 4 GB of memory or more recommended
- Processor: Intel® Pentium® 4 or AMD Athlon™ Dual Core, with SSE2 technology
- Hard Disk Space: 2 GB (minimum) free space available
- Screen Resolution: 1024 x 768 or higher
- Browser: A web browser is required for Minitab Help.

Supported Languages
Chinese, English, French, German, Japanese, Korean, Portuguese, Spanish

Release Name: Minitab 22.5.2
Size: 237.4 MB
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