Minitab 21.4.2 + Fix [AppDoze]

Minitab 21.4.2 + Fix [AppDoze]

Minitab 21.4.2 + Fix [AppDoze]
Minitab Overview

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.
Features of Minitab

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.
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.
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.
Measurement systems analysis
Capability analysis
Graphical analysis
Hypothesis tests
Control charts
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
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
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
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 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
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
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*
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
Moving average
Exponential smoothing
Winters’ method
Auto-, partial auto-, and cross correlation functions
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
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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Minitab 21.4.2 + Fix [AppDoze]

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