000			03260cam a2200313 i 4500
001			18132035
005			20221019131551.0
008			140429s2014 flua b 001 0 eng
010			_a 2014013362
020			_a9781466567283 (hbk.)
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA278 _b.K597 2014
082	0	0	_a519.535 _223 _bKON/I
084			_aMAT029000 _2bisacsh
100	1		_aKonishi, Sadanori. _91224
245	1	0	_aIntroduction to multivariate analysis : _blinear and nonlinear modeling / _cby Sadanori Konishi.
260			_aBoca Raton : _bCRC Press, _c2014.
300			_axxv, 312 p. : _billu. ; _c24 cm.
490	0		_aChapman & Hall/CRC Texts in Statistical Science series.
504			_aIncludes bibliographical references and index.
520			_a"Multivariate techniques are used to analyze data that arise from more than one variable in which there are relationships between the variables. Mainly based on the linearity of observed variables, these techniques are useful for extracting information and patterns from multivariate data as well as for the understanding the structure of random phenomena. This book describes the concepts of linear and nonlinear multivariate techniques, including regression modeling, classification, discrimination, dimension reduction, and clustering"--
520			_a"The aim of statistical science is to develop the methodology and the theory for extracting useful information from data and for reasonable inference to elucidate phenomena with uncertainty in various fields of the natural and social sciences. The data contain information about the random phenomenon under consideration and the objective of statistical analysis is to express this information in an understandable form using statistical procedures. We also make inferences about the unknown aspects of random phenomena and seek an understanding of causal relationships. Multivariate analysis refers to techniques used to analyze data that arise from multiple variables between which there are some relationships. Multivariate analysis has been widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Techniques would include regression, discriminant analysis, principal component analysis, clustering, etc., and are mainly based on the linearity of observed variables. In recent years, the wide availability of fast and inexpensive computers enables us to accumulate a huge amount of data with complex structure and/or high-dimensional data. Such data accumulation is also accelerated by the development and proliferation of electronic measurement and instrumentation technologies. Such data sets arise in various fields of science and industry, including bioinformatics, medicine, pharmaceuticals, systems engineering, pattern recognition, earth and environmental sciences, economics and marketing. "--
650		0	_aMultivariate analysis. _987
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh _989
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cTRB _02
999			_c8056 _d8056