Jacobi-Static

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

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