Icon of program: Visual Stats

Visual Stats for Windows

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Editors’ Review

Download.com staff
GraphNow offers robust statistical software for in-depth data analysis. It covers a vast array of statistical tests and modeling techniques for comprehensive multivariate analysis.

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Visual Stats 0/1

  • Pros

    • Extensive non-parametric and parametric testing options.
    • Wide range of regression analysis methods.
    • Diverse correlation analysis capabilities.
    • Multiple cluster analysis techniques included.
    • Advanced multivariate statistical techniques are supported.

  • Cons

    • No mention of visualization features.
    • User interface details are absent.
    • No information on data import/export capabilities.
    • No data manipulation tools are described.
    • No mention of collaboration features.
Icon of program: Visual Stats

Visual Stats for Windows

Download now

Used Visual Stats for Windows?

Explore More

Full Specifications

GENERAL
Release
Latest update
Version
2.0
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows 2000
  • Windows NT
  • Windows 2003
  • Windows 7
  • Windows Server
  • Windows ME
  • Windows Vista
  • Windows 10
  • Windows 8
  • Windows XP
  • Windows 98
Additional Requirements
None
POPULARITY
Total Downloads
291
Downloads Last Week
15
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Developer’s Description

By GraphNow
Implement data analysis and multivariate statistical analysis.
Statistics software for data analysis and multivariate statistical analysis. Probability analysis. Compute descriptive statistics of selected data. Frequency analysis. Compare means and variances. Variance analysis. Z test. Correlation tests. Jarque Bera test. Non-parametric tests, including one sample Chi-square test, two samples Chi-square test, one sample K-S test, two samples K-S test, sign test, Wilcoxon signed rank test, Mann Whitney U test. Regression analysis, including univariate linear regression, multivariate linear regression, linear curve fitting, nonlinear curve fitting, trend surface analysis, stepwise regression etc. Correlation analysis, including bivariate correlation analysis, partial correlation analysis and canonical correlation analysis. Cluster analysis, including stepwise cluster analysis, hierarchial cluster analysis and dynamical cluster analysis. Stepwise discriminant analysis. Principal component analysis. Factor analysis. Correspondence analysis.
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