By Peter J. Huber
This e-book explores the various provocative questions about the basics of information research. it really is in line with the time-tested adventure of 1 of the professionals of the subject material. Why may still one learn info research? How should still it's taught? What recommendations paintings most sensible, and for whom? How legitimate are the implications? How a lot facts may be established? Which laptop languages may be used, if used in any respect? Emphasis on apprenticeship (through hands-on case reviews) and anecdotes (through real-life functions) are the instruments that Peter J. Huber makes use of during this quantity. hindrance with particular statistical thoughts isn't really of instant price; relatively, questions of approach – whilst to exploit which process – are hired. vital to the dialogue is an realizing of the importance of big (or strong) info units, the implementation of languages, and using types. every one is sprinkled with an plentiful variety of examples and case reports. own practices, numerous pitfalls, and latest controversies are offered while appropriate. The booklet serves as an exceptional philosophical and old spouse to any present-day textual content in info research, strong records, facts mining, statistical studying, or computational facts.
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Extra info for Data Analysis: What Can Be Learned From the Past 50 Years (Wiley Series in Probability and Statistics)
87) werden. (Clausewitz, p. 210) If war is a continuation of politics, then data analysis is a continuation of science. Here, "science" stands as a collective term for all intellectual endeavors concerned with gaining insight in any applied field in the real world. The goals of the scientific project have the primacy, and the data analyst must keep them in mind all of the time. Ask yourself whether the problem is relevant, and whether the data at hand are relevant to the problem. It is far better to give an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise (Tukey 1962, p.
Such systems will run into problems caused by human factors and in particular by the requirements of communication between humans. While it is essential to keep track of modifications, it is devilishly tricky to do so by machine in such a way that somebody else than the originator of the modifications can make sense of a pile of slightly different and sometimes slightly wrong analyses. A few months later, even the originator will be baffled. 4. ) replace the creative processes going on in the human mind.
The principal questions to be raised and addressed thus are: What is the purpose of the analysis? Do we already have in hand suitable data? How can we collect such data and in such a way that we will have a reasonable chance to achieve our purpose? Will the data contain the information we are looking for and which is necessary for answering our questions? Will the questions still be the same by the time the data will be in hand? There are cases (mostly hushed up and therefore rarely documented) where multimillion dollar data collections had to be junked because of their poor design.