Statistical Packages

155

Solutions

335

Contributors

Statistical packages are software titles, such as JMP and GNU Octave, and programming languages, such as MATLAB, R and SAS, that are used to discover, explore and analyze data and suggest useful conclusions, either to learn something unexpected or to confirm a hypothesis. The field includes the design and analysis of techniques to give approximate but accurate solutions to hard problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

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There is no single "correct" calculation method for quartiles. Excel features two methods, some math and statistic packages offer some more, but here is presented no less than twenty methods for various purposes using VBA and Microsoft Access.
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OWASP: Forgery and Phishing
LVL 19
OWASP: Forgery and Phishing

Learn the techniques to avoid forgery and phishing attacks and the types of attacks an application or network may face.

The process of ML is comparable to data processing. Both systems search through information to appear for patterns. However, rather than extracting information for human comprehension as just in case of knowledge mining, ML uses that information to discover trends in data and alter program actions.
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When you discover the power of the R programming language, you are going to wonder how you ever lived without it! Learn why the language merits a place in your programming arsenal.
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Statistical Packages

155

Solutions

335

Contributors

Statistical packages are software titles, such as JMP and GNU Octave, and programming languages, such as MATLAB, R and SAS, that are used to discover, explore and analyze data and suggest useful conclusions, either to learn something unexpected or to confirm a hypothesis. The field includes the design and analysis of techniques to give approximate but accurate solutions to hard problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

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