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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.
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. The company was incorporated in 1975, and Nie was CEO from 1975 ...
SPSS 30: JASP 0.19.3: SPSS 30: Analysis: Classic: Classic: Bayesian: Bayesian: Acceptance Sampling X (repeated) (M)AN(C)OVA and non-parametrics ( ) ( ) Audit - Statistical Methods for Auditing X X Bain - Bayesian informative hypotheses evaluation X BSTS - Bayesian structural time series X Circular / Directional Statistics - analysis of ...
Concerns include increasingly deterministic and rigid processes, privileging of coding, and retrieval methods; reification of data, increased pressure on researchers to focus on volume and breadth rather than on depth and meaning, time and energy spent learning to use computer packages, increased commercialism, and distraction from the real ...
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.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
"A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology . 21 (2).
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.