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Minitab 21.4.2 + Portable [moderator]

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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.
With powerful statistics, industry-leading data analytics, and dynamic visualizations on your side, the possibilities
are endless.

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

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