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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.
Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels, scikit-learn) Perl Data Language – Scientific computing with Perl; Ploticus – software for generating a variety of graphs from raw data; PSPP – A free software alternative to IBM SPSS Statistics
Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
Statistical analysis of sampled data. Free replacement for SPSS. QtiPlot: GUI, Python: proprietary: No 2004: May 31, 2017 / 0.9.9.11: Linux, Mac, Windows: Python scriptable R: Third party: GPL: Yes 1993: July 2, 2018 / 3.5.1("Feather Spray") Linux, Mac, Windows: In addition to base graphics, has a large number of graphics packages including ...
For example, if the data is in CSV form (text with commas between values), the program recognizes the format and creates a data set from the CSV file. Finally, the program can be used to do some analysis. In this analysis menu, the variables of interest can be selected, along with other options. Then the analysis is run and results are obtained.
FlexPro is a program for data analysis and presentation of measurement data. It provides a rich Excel-like user interface and its built-in vector programming language FPScript has a syntax similar to MATLAB. FreeMat, an open-source MATLAB-like environment with a GPL license.
Within statistics, oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented). These terms are used both in statistical sampling, survey design methodology and in machine learning.
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. General tests [ edit ]