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Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."
jamovi (stylised in all lower-case) is a free and open-source computer program for data analysis and performing statistical tests. The core developers of jamovi are Jonathon Love, Damian Dropmann, and Ravi Selker, who were developers for the JASP project.
Educational data mining Cluster analysis is for example used to identify groups of schools or students with similar properties. Typologies From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.
Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). [4]
In project management, trend analysis is a mathematical technique that uses historical results to predict future outcome. This is achieved by tracking variances in cost and schedule performance. In this context, it is a project management quality control tool. [4] [5]
Data analysis; Data assimilation; Data binning; Data classification (business intelligence) Data cleansing; Data clustering; Data collection; Data Desk – software; Data dredging; Data fusion; Data generating process; Data mining; Data reduction; Data point; Data quality assurance; Data set; Data-snooping bias; Data stream clustering; Data ...
Directional statistics to include measures of dispersion for circular data Conjoint analysis improve statement problem to be solved (more maths?) Overfitting could tie in test sets , the effects of high model complexity and curse of dimensionality as well as how adding noise sometimes alleviates the problem.