Minitab Statistical Software

Minitab Statistical Software

By harnessing the capabilities of Minitab Statistical Software, one can analyze both present and historical data to identify trends, predict patterns, unveil concealed connections among variables, and generate impressive visual representations to confront even the most challenging situations and prospects. With its robust statistical tools, cutting-edge data analytics, and interactive visualizations, there are limitless possibilities at your disposal.

Minitab Statistical Software helps to discover

Irrespective of one's level of statistical expertise, Minitab enables every facet of an organization to enhance their ability to forecast favorable outcomes, develop superior products, and enhance processes to increase revenues and decrease costs. What sets Minitab apart is its distinctive and comprehensive approach, offering both software and services that facilitate business excellence regardless of location through cloud-based solutions.

Noteworthy statistical tests available in Minitab encompass t-tests, one and two proportions analysis, tests for normality, chi-square tests, and equivalence tests.

Minitab Statistical Software helps to predict

Unlock the potential of contemporary data analysis and delve deeper into your data with our state-of-the-art advanced analytics. Effortlessly predict outcomes, compare alternatives, and confidently forecast the future of your business using our groundbreaking predictive analytics techniques.

Leverage classical methodologies within Minitab Statistical Software, seamlessly integrate with popular open-source languages such as R or Python, or elevate your capabilities even further with the inclusion of machine learning algorithms like Classification and Regression Trees (CART®), TreeNet®, and Random Forests® through Minitab's Predictive Analytics Module.

Minitab Statistical Software helps to achieve

Witness the power of visual representations. Visualizations play a pivotal role in effectively conveying discoveries and accomplishments. Making informed decisions about the most suitable graph to showcase your data and support your analysis is now quick and effortless with the assistance of Graph Builder.

Our innovative interactive tool, equipped with a user-friendly gallery, allows you to effortlessly browse and examine numerous graph options without the need to rerun your analysis. With consistent data selection, Graph Builder seamlessly updates from bar charts to correlograms to heat maps, and beyond, enabling you to concentrate on selecting the ideal visual representation to showcase your insights.

Minitab Statistical Software can help in analytics journey for

Minitab Statistical Software covers a wide range of techniques and methodologies such as gage studies, attribute agreement analysis, control charts, multivariate analysis, rare event charts, capability analysis, acceptance sampling, reliability engineering, distribution analysis, censoring methods, test plans, accelerated life tests, warranty analysis, regression analysis, design of experiments, screening designs, response surface methodology, Taguchi methods, power and sample size calculations, correlation and regression modeling, cluster analysis, classification and regression trees, random forests, time series analysis, visualization techniques, process validation, hypothesis testing, process capability, measurement system analysis, and control charts for continued process validation. These techniques are used for analyzing and improving product quality, optimizing processes, making predictions, and making data-driven decisions in business and industry.

QualityMeasurement System AnalysisGage Studies
Attribute Agreement Analysis
Control ChartsVariable, attribute
Time weighted
Rare event charts
Capability AnalysisCapabilitySixpack
Acceptance Sampling
Tolerance Intervals
Reliability EngineeringDistribution AnalysisArbitrary censoring (left, right or interval censoring)
Weibull Analysis
Censored Data
Test PlansDemonstration
Estimation (sample size for distribution analysis)
Accelerated Life Test
Warranty Analysis
Reparable System Analysis
Regression with Life Data
Probit Analysis
Product DevelopmentDesign of Experiments (DoE)Screening Designs
Full Factorial
Fractional Factoria
Response Surface
Power and Sample SizeTolerance Intervals
Normal and Non-Normal Distribution
Business AnalyticsCorrelation Statistical ModelingRegression
Nonlinear Regression
Multivariate models
Cluster Analysis
Classification and Regression Trees (CART®)
Random Forests®
Time Series AnalyticsARIMA Modeling
Time Series / Forecasting
Multivariate Methods
Chi-Square Test for Association
Process ValidationStage 1: Process DesignMeasurement Systems Analysis
Hypothesis Testing
Regression / ANOVA
Process Capability
Stage 2: Process QualificationControl Charts
Capability Analysis
Tolerance Intervals
Stage 3: Continued Process ValidationMeasurement System Analysis
Acceptance Sampling
Control Charts

Features of Minitab Statistical Software

Minitab Statistical Software offers a wide range of statistical analysis tools, including measurement systems analysis, hypothesis tests, regression, control charts, and a healthcare module. It supports various types of plots and graph customization, allowing automatic updates as data change. Users can explore data using brush graphs and export results in different file formats. It covers basic and descriptive statistics, correlation analysis, regression techniques, ANOVA, multivariate analysis, reliability analysis, power calculations, predictive analytics, time series forecasting, nonparametric tests, equivalence tests, tables, and simulations. It also provides customization options through scripting and integration with Python and R.

AssistantMeasurement Systems Analysis
Capability Analysis
Graphical Analysis
Hypothesis Tests
Control Charts
GraphicsGraph Builder
Binned scatterplots, boxplots, bubble plots, bar charts, correlograms, dotplots, heatmaps, histograms, matrix plots, parallel plots, scatterplots, time series plots, etc.Measurement System Analysis
Contour and rotating 3D plots
Probability and probability distribution plots
Automatically update graphs as data change
Brush graphs to explore points of interest
Basic StatisticsDescriptive 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
RegressionCox 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
Model validation
Multivariate Adaptive Regression Splines
Analysis of VarianceANOVA
General linear models
Mixed models
Multiple comparisons
Response prediction and optimization
Test for equal variances
Plots: residual, factorial, contour, surface, etc.
Analysis of means
Measurement Systems AnalysisData 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 ToolsRun 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 Sixpack™
Tolerance intervals
Acceptance sampling and OC curves
Multi-Vari chart
Variability chart
Design of ExperimentsDefinitive 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 / SurvivalParametric 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 SizeSample 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
Two-level, Plackett-Burman and general full factorial designs
Power curves
Predictive AnalyticsAutomated Machine Learning
CART® Classification
CART® Regression
Random Forests® Classification
Random Forests® Regression
TreeNet® Classification
TreeNet® Regression
MultivariatePrincipal components analysis
Factor analysis
Discriminant analysis
Cluster analysis
Correspondence analysis
Item analysis and Cronbach’s alpha
Multivariate Adaptive Regression Splines
Time Series and ForecastingTime series plots
Trend analysis
Moving average
Exponential smoothing
Winters’ method
Auto-, partial auto-, and cross correlation functions
Box-Cox Transformation
Augmented Dickey-Fuller Test
Forecast with Best ARIMA Model
NonparametricsSign test
Wilcoxon test
Mann-Whitney test
Kruskal-Wallis test
Mood’s median test
Friedman test
Runs test
TablesChi-square, Fisher’s exact, and other tests
Chi-square goodness-of-fit test
Tally and cross tabulation
Simulations and DistributionsRandom number generator
Probability density, cumulative distribution, and inverse cumulative distribution functions
Random sampling
Bootstrapping and randomization tests
Macros and CustomizationCustomizable menus and toolbars
Extensive preferences and user profiles
Powerful scripting capabilities
Python integration
R integration
Healthcare Module