Lecture
The latest versions of almost all well-known statistical packages include, alongside the traditional statistical methods — correlation, regression and factor analysis — elements of Data Mining as well. A drawback of such systems is often considered to be the requirement for the user to have special training in mathematical statistics. It is also noted that powerful modern statistical packages are too complex and expensive for mass application in business.
A more serious fundamental drawback of statistical packages, limiting their application, should be noted. Most of the methods included in the packages rely on a statistical paradigm based on averaged sample characteristics, which, when studying real complex life phenomena, are often fictitious quantities. This extremely important circumstance must be taken into account when analyzing multidimensional data using statistical packages.
Examples of the most powerful and widely used statistical packages include SAS, SPSS, STATGRAPHICS, STATISTICA, STADIA, R and others. These packages are available to medium-sized organizations, while large multi-disciplinary companies can integrate them into their overall corporate network.
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