jacobi static

We have hosted the application jacobi static in order to run this application in our online workstations with Wine or directly.

Quick description about jacobi static:

The STATIC/EX includes three applications: static.exe - a simple multivariate statistics calculator; spector.exe - file and online data viewer; tester.exe - an IPC client to develop and debug operations with embedded STATIC/EX IPC server. This toolkit can be used for:
�user's training (who are interested in multivariate statistics)
�scientific and other data researching.

Features:

• data entry, including directly from the command line
• splitting a data set into several and merging several data sets
• flexible management of data samples (for almost every processing step, you can specify your own set of objects and/or features)
• saving data to a file and reading it from a file (standard .std files and Comma?Separated?Values, .csv � a sequence of lines of numbers with separators)
• calculation of a set of traditional statistical parameters of the sample (for each feature � the number of values presented, the minimum, maximum and average value of the feature, its dispersion, standard deviation and error)
• data transformation (recalculation of values � change via a constant, standard function, or via the values of attributes, one or more)
• data cleaning from contamination
• factor analysis with the principal component processing
• obtaining a matrix of eigenvectors, a row of eigenvalues, a matrix of pair correlations (covariances, second moments � depending on the context)
• visual representation of �phase portraits� in the form of clouds of scattering of objects and spatial distribution of features
• saving for further use (for example, when preparing publications) the results of statistical processing in the form of .csv files
• find the level of statistically significant proportion of information contained in the data being studied (assessment of the level of �signal� and �noise�)
• investigate internal patterns consisting of manifestations of the action of independent factors
• describe the nature and content of the identified factors, based on correlations between features and their contributions to the eigenvectors
• compute autocorrelations for time series with arbitrary lag
• draw clustering of scattering clouds of objects, both in the space of original features and in the space of principal components
• find interrelation of features through projections onto phase planes of principal components
• study cyclical (quasi?cyclical) manifestations of processes represented by time series

.