Minitab 22.2 Languages: Multilingual File Size: 315.91 MB 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 "Release Notes" https://www.minitab.com/en-us/products/minitab/whats-new/ if(window.hljsLoader && !document.currentScript.parentNode.hasAttribute('data-s9e-livepreview-onupdate')) { window.hljsLoader.highlightBlocks(document.currentScript.parentNode); } 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. 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Minitab 22.2

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Minitab 22.2

Languages: Multilingual
File Size: 315.91 MB

Harness the power of statistics. Data is everywhere, but are you truly taking advantage of yours? Minitab Statistical Software can look at c

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