Anbieter: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Deutschland
321 S. Ill. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Wiley series in computational statistics. Sprache: Englisch.
EUR 98,44
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 100,82
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In den WarenkorbZustand: New. In.
EUR 128,36
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In den WarenkorbZustand: New. pp. 330 Illus.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 150,22
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In den WarenkorbHardcover. Zustand: Brand New. 1st edition. 330 pages. 9.00x6.00x1.00 inches. In Stock.
EUR 111,87
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In den WarenkorbZustand: New. The first book to present a unified account of symbolic data analysis methods in a consistent statistical framework, Symbolic Data Analysis features a substantial number of examples from a range of application areas, including health, the social sciences, e.
EUR 158,77
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In den WarenkorbZustand: New. The first book to present a unified account of symbolic data analysis methods in a consistent statistical framework, Symbolic Data Analysis features a substantial number of examples from a range of application areas, including health, the social sciences, economics, and computer science. Series: Wiley Series in Computational Statistics. Num Pages: 330 pages, illustrations. BIC Classification: PB; PS; UF. Category: (P) Professional & Vocational. Dimension: 233 x 162 x 23. Weight in Grams: 598. . 2007. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - With the advent of computers, very large datasets have become routine. Standard statistical methods don't have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis.