## Assistant

Measurement systems analysis

Capability analysis

Graphical analysis

Hypothesis tests

Regression

DOE

Control chart

## Graphics

Scatterplots, matrix plots, boxplots, dotplots, histograms, charts, 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 and 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

LRun 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

## Multivariate

Principal components analysis

Factor analysis

Discriminant analysis

Cluster analysis

Correspondence analysis

Item analysis and Cronbach’s alpha

## Tables

Chi-square, Fisher’s exact, and other tests

Chi-square goodness-of-fit test

Tally and cross tabulation

## Time Series and Forecasting

Time series plots

Trend analysis

Decomposition

Moving average

Exponential smoothing

Winters’ method

Auto-, partial auto-, and cross correlation functions

ARIMA

## Equivalence Tests

One- and two-sample, paired

2×2 crossover design

## Predictive Analytics*

CART® Classification*

CART® Regression*

## Macros and Customization

Customizable menus and toolbars

Extensive preferences and user profiles

Powerful scripting capabilities

Python integration*

## Non parametrics

Sign test

Wilcoxon test

Mann-Whitney test

Kruskal-Wallis test

Mood’s median test

Friedman test

Runs test

## Simulations and Distributions

Random number generator

Probability density, cumulative distribution, and inverse cumulative distribution functions

Random sampling

Bootstrapping and randomization tests

## Qsutra® is the Sole Authorised Minitab Partner, Minitab Distributor and Minitab Reseller in India, Sri Lanka, Bangladesh & Nepal. Qsutra® provides Minitab's  Training & Technical Support Services.We have been working very closely with hundreds of organisations for Integrating Minitab products into their Improvement initiatives like Six Sigma,Lean, Statistical Process Control (SPC), Kaizen, Total Quality Management (TQM), QbD, CMMI etc. or their own methodologies.Minitab is the Software of choice for Statisticians – for its Versatility & Customisation capabilities, as well as for Non – Statisticians for its Ease of Use & world-class Resources. Our Minitab Customer Support team is the best place to reach out for any query you may have – Where to buy Minitab Software in India, how to evaluate Minitab Products, who can help with Minitab Customisation, Minitab for Education in India, Minitab Macros, what is Minitab Pricing model in India, clarity on Minitab Licencing, interested in Minitab Reselling etc.Write to us at [email protected]

Start typing and press Enter to search

Shopping Cart

No products in the cart.