* New or Improved


Measurement systems analysis
Capability analysis
Graphical analysis
Hypothesis tests
Control charts


Scatterplots, binned scatterplots, matrix plots, boxplots, dotplots, histograms, charts, time series plots, parallel coordinates plot, heatmap 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*


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
2x2 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 *

Bizit Systems is the leading provider of software and services for quality improvement.