000 04130nam a2200601 i 4500
001 11249678
003 CaPaEBR
005 20241023114847.0
006 m eo d
007 cr cn |||m|||a
008 160829s2016 nyu foa 001 0 eng d
020 _a9781631575518
_qe-book
035 _a(BEP)4648714
035 _a(OCoLC)957560388
035 _a(CaBNVSL)swl00406797
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA278.8
100 1 _aNaghshpour, Shahdad.,
_eauthor.
245 1 2 _aA primer on nonparametric analysis.
_nVolume II /
_cShahdad Naghshpour.
250 _aFirst edition.
264 1 _aNew York, New York (222 East 46th Street, New York, NY 10017) :
_bBusiness Expert Press,
_c[(c)2016.]
300 _a1 online resource (85 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _adata file
_2rda
490 1 _aEconomics collection,
_x2163-7628
500 _aIncludes index.
505 0 _aSection I. K related samples --
_t1. Three or more related populations: one-way analysis of variance --
_t2. K unrelated samples tests --
_tSection II. Measures of association --
_t3. Correlation coefficient --
_tIndex.
506 _aAccess restricted to authorized users and institutions.
520 3 _aNonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. In social sciences, often, it is not possible to obtain measurements, which renders customary analysis impossible. For example, it is not possible to measure utility but is possible to rank preference, which is based on the unmeasurable utility. Nonparametric methods provide theoretically valid options for analysis, making the use of unscientific methods unnecessary. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. The only prerequisite for this book is high school level elementary algebra. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. The book is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities. It is recommended that everyone read the Introduction and Chapter 1 because determining whether data are random or normally distributed is essential in the selection of parametric versus nonparametric methods.
530 _a2
_ub
530 _aAlso available in printing.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat reader.
588 _aTitle from PDF title page (viewed on August 29, 2016).
650 0 _aNonparametric statistics.
653 _aNonparametric statistics
653 _amedian
653 _aorder statistics
653 _arank
653 _aone sample
653 _atwo samples
653 _aseveral samples
653 _amultiple comparison
653 _anormality
653 _askewness
655 0 _a[genre]
776 0 8 _iPrint version:
_z9781631575501
830 0 _aEconomics collection.
_x2163-7628
856 4 0 _uhttps://go.openathens.net/redirector/ciu.edu?url=https://portal.igpublish.com/iglibrary/search/BEPB0000513.html
942 _2lcc
_bCIU
_cOB
_eBEP
_QOL
_zBEP11249678
999 _c104649
_d104649
902 _a1
_bCynthia Snell
_c1
_dCynthia Snell