000 04158nam a22005415i 4500
001 978-0-8176-4444-4
003 DE-He213
005 20161121231028.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780817644444
_9978-0-8176-4444-4
024 7 _a10.1007/0-8176-4444-X
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aGood, Phillip I.
_eauthor.
245 1 0 _aResampling Methods
_h[electronic resource] :
_bA Practical Guide to Data Analysis /
_cby Phillip I. Good.
250 _aThird Edition.
264 1 _aBoston, MA :
_bBirkhäuser Boston,
_c2006.
300 _aXX, 218 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aSoftware for Resampling -- Estimating Population Parameters -- Comparing Two Populations -- Choosing the Best Procedure -- Experimental Design and Analysis -- Categorical Data -- Multiple Variables and Multiple Hypotheses -- Model Building -- Decision Trees.
520 _a"…the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start." —Technometrics (Review of the Second Edition) This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. Topics and Features * Practical presentation covers both the bootstrap and permutations along with the program code necessary to put them to work. * Includes a systematic guide to selecting the correct procedure for a particular application. * Detailed coverage of classification, estimation, experimental design, hypothesis testing, and modeling. * Suitable for both classroom use and individual self-study. New to the Third Edition * Procedures are grouped by application; a prefatory chapter guides readers to the appropriate reading matter. * Program listings and screen shots now accompany each resampling procedure: Whether one programs in C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-PLUS, Stata, or StatXact, readers will find the program listings and screen shots needed to put each resampling procedure into practice. * To simplify programming, code for readers to download and apply is posted at http://www.springeronline.com/0-8176-4386-9. * Notation has been simplified and, where possible, eliminated. * A glossary and answers to selected exercises are included. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.
650 0 _aMathematics.
650 0 _aMathematical analysis.
650 0 _aAnalysis (Mathematics).
650 0 _aProbabilities.
650 0 _aStatistics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aAnalysis.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780817643867
856 4 0 _uhttp://dx.doi.org/10.1007/0-8176-4444-X
912 _aZDB-2-SMA
950 _aMathematics and Statistics (Springer-11649)
999 _c507511
_d507511