Unistat

Infobox_Software
name = Unistat



caption =
developer = Unistat Ltd
latest_release_version = 5.6
latest_release_date = November 15, 2005
operating_system = Windows
genre = statistical package
license = proprietary
website = [http://www.unistat.com/ Unistat]

The Unistat computer program is a statistical data analysis tool featuring two modes of operation: The stand-alone user interface is a complete workbench for data input, analysis and visualization while the Microsoft Excel add-in mode extends the features of the mainstream spreadsheet application with powerful analytical capabilities.

With its first release in 1984, Unistat soon differentiated itself by targeting the new generation of microcomputers that were becoming commonplace in offices and homes at a time when data analysis was largely the domain of big iron mainframe and minicomputers. Since then, the product has gone through several major revisions targeting various desktop computing platforms, but its development has always been focused on user interaction and dynamic visualization.

As desktop computing has continued to proliferate throughout the 1990s and onwards, Unistat's end-user oriented interface has attracted a following amongst biomedicine researchers, social scientists, market researchers, government departments and students, enabling them to perform complex data analysis without the need for large manuals and scripting languages.

Statistics procedures supported by Unistat include:
* Parametric statistics
* Non-parametric statistics: binomial test, chi-square test, Cohen's kappa, Fisher's exact test, Friedman two-way analysis of variance, Kendall's tau, Kendall's W, Kolmogorov-Smirnov test, Kruskal-Wallis one-way analysis of variance, Mann-Whitney U, McNemar's test, median test, Spearman's rank correlation coefficient, Duncan's new multiple range test, Wald-Wolfowitz runs test, Wilcoxon signed-rank test
* Regression: Linear regression, Stepwise regression, Nonlinear regression, logit/probit/Weibull, logistic regression, multinomial, Poisson regression and Cox regressions
* Analysis of variance
* General linear model
* Multivariate analysis: Principal components analysis, Linear discriminant analysis, canonical analysis, Multidimensional scaling, Canonical correlation analysis
* Time series
* reliability
* Survival analysis
* Quality control
* Bioassay Analysis: This optional module features potency estimation with parallel line, slope ratio and quantal response methods, with Fieller confidence intervals, validity tests, ED50 and graphical representations.

References

* [http://links.jstor.org/sici?sici=0035-9254(1994)43%3A3%3C562%3AUV1%3E2.0.CO%3B2-M Applied Statistics]
* [http://links.jstor.org/sici?sici=0003-1305(199502)49%3A1%3C99%3AAROFSP%3E2.0.CO%3B2-R American Statistician]
* [http://www.nature.com/nature/software/graphs/unistat.html Nature]
* [http://bioinformatics.oxfordjournals.org/cgi/issue_pdf/frontmatter_pdf/1/3.pdf Computer Applications in the Biosciences]
* [http://www.scientific-computing.com/scwjanfeb04review_stattools.html Scientific Computing World]