Minitab 20.4

Predict. Visualize, analyze and harness the power of your data to solve your toughest challenges and eliminate mistakes before they happen. Data is everywhere these days, but are you truly taking advantage of yours?

Minitab Statistical Software can look at current and past data to find trends and predict patterns, uncover hidden relationships between variables, visualize data interactions and identify important factors to answer even the most challenging of questions. Visualizations are good, but pair them with analytics to make them great. With the power of statistics and data analysis 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
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

Download Minitab 20.4