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.
| Quality | Measurement System Analysis | Gage Studies |
| Attribute Agreement Analysis | ||
| Control Charts | Variable, attribute | |
| Multivariate | ||
| Time weighted | ||
| Rare event charts | ||
| Capability Analysis | CapabilitySixpack | |
| Acceptance Sampling | ||
| Tolerance Intervals | ||
| Reliability Engineering | Distribution Analysis | Arbitrary censoring (left, right or interval censoring) |
| Weibull Analysis | ||
| Censored Data | ||
| Test Plans | Demonstration | |
| Estimation (sample size for distribution analysis) | ||
| Accelerated Life Test | ||
| Warranty Analysis | ||
| Reparable System Analysis | ||
| Regression with Life Data | ||
| Probit Analysis | ||
| Product Development | Design of Experiments (DoE) | Screening Designs |
| Full Factorial | ||
| Fractional Factoria | ||
| Response Surface | ||
| Mixture | ||
| Taguchi | ||
| Power and Sample Size | Tolerance Intervals | |
| Normal and Non-Normal Distribution | ||
| Business Analytics | Correlation Statistical Modeling | Regression |
| Nonlinear Regression | ||
| Multivariate models | ||
| Cluster Analysis | ||
| Classification and Regression Trees (CART®) | ||
| TreeNet® | ||
| Random Forests® | ||
| Time Series Analytics | ARIMA Modeling | |
| Time Series / Forecasting | ||
| Visualizations | Heatmaps | |
| Boxplots | ||
| Multivariate Methods | ||
| Chi-Square Test for Association | ||
| Process Validation | Stage 1: Process Design | Measurement Systems Analysis |
| Hypothesis Testing | ||
| Regression / ANOVA | ||
| Process Capability | ||
| Stage 2: Process Qualification | Control Charts | |
| Capability Analysis | ||
| Tolerance Intervals | ||
| Stage 3: Continued Process Validation | Measurement 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.
| Assistant | Measurement Systems Analysis |
| Capability Analysis | |
| Graphical Analysis | |
| Hypothesis Tests | |
| Regression | |
| DoE | |
| Control Charts | |
| Graphics | Graph 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 | |
| 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 | Cox 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 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 Sixpack™ | |
| 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 | Automated Machine Learning |
| CART® Classification | |
| CART® Regression | |
| MARS® | |
| 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 | |
| Multivariate Adaptive Regression Splines | |
| Time Series and Forecasting | Time series plots |
| Trend analysis | |
| Decomposition | |
| Moving average | |
| Exponential smoothing | |
| Winters’ method | |
| Auto-, partial auto-, and cross correlation functions | |
| ARIMA | |
| Box-Cox Transformation | |
| Augmented Dickey-Fuller Test | |
| Forecast with Best ARIMA Model | |
| Nonparametrics | Sign test |
| Wilcoxon test | |
| Mann-Whitney test | |
| Kruskal-Wallis test | |
| Mood’s median test | |
| Friedman test | |
| Runs test | |
| 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 | |
| Healthcare Module |