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SPSS Statistics places constraints on internal file structure, data types, data processing, and matching files, which together considerably simplify programming. SPSS datasets have a two-dimensional table structure, where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age ...
"A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology. 21 (2). Archived from the original (PDF) on October 25, 2005.
jamovi – A free software alternative to IBM SPSS Statistics; JASP – A free software alternative to IBM SPSS Statistics with additional option for Bayesian methods; JMulTi – For econometric analysis, specialised in univariate and multivariate time series analysis
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
This is the aim of multiple factor analysis which balances the different issues (i.e. the different groups of variables) within a global analysis and provides, beyond the classical results of factorial analysis (mainly graphics of individuals and of categories), several results (indicators and graphics) specific of the group structure.
SPSS Inc. was a software house headquartered in Chicago and incorporated in Delaware, most noted for the proprietary software of the same name SPSS. The company was started in 1968 when Norman Nie , Dale Bent, and Hadlai "Tex" Hull developed and started selling the SPSS software.
IGOR Pro, a software package with emphasis on time series, image analysis, and curve fitting. It comes with its own programming language and can be used interactively. LabPlot is a data analysis and visualization application built on the KDE Platform. MFEM is a free, lightweight, scalable C++ library for finite element methods.
Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots.
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