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SPSS Statistics is a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. Long produced by SPSS Inc., it was acquired by IBM in 2009. Versions of the software released since 2015 have the brand name IBM SPSS Statistics.
gretl is an example of an open-source statistical package. ADaMSoft – a generalized statistical software with data mining algorithms and methods for data management; ADMB – a software suite for non-linear statistical modeling based on C++ which uses automatic differentiation; Chronux – for neurobiological time series data; DAP – free ...
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.
Vinod (2006), [31] presents a method that bootstraps time series data using maximum entropy principles satisfying the Ergodic theorem with mean-preserving and mass-preserving constraints. There is an R package, meboot , [ 32 ] that utilizes the method, which has applications in econometrics and computer science.
SPSS: A dialog box for Propensity Score Matching is available from the IBM SPSS Statistics menu (Data/Propensity Score Matching), and allows the user to set the match tolerance, randomize case order when drawing samples, prioritize exact matches, sample with or without replacement, set a random seed, and maximize performance by increasing ...
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables.
"A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology . 21 (2).
In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space .
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