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008			230510s2021 sz | o |||| 0|eng d
020			_a9783031024337
024	7		_a10.1007/978-3-031-02433-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA278.8 _b2021 EB
100	1		_aKatari, Manpreet Singh _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688464
245	1	0	_aStatistics is Easy : _bCase Studies on Real Scientific Datasets _cby Manpreet Singh Katari, Sudarshini Tyagi, Dennis Shasha
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XI, 62 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Mathematics & Statistics _x1938-1751
505	0		_aAcknowledgments -- Introduction -- Chick Weight and Diet -- Breast Cancer Classification -- RNA-seq Data Set/ Summary and Perspectives -- Bibliography -- Authors' Biographies.
520			_aComputational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9686387 _aEstadística no paramétrica
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aTyagi, Sudarshini _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688465
700	1		_aShasha, Dennis Elliott _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686965
776	0	8	_iPrinted edition: _z9783031002793
776	0	8	_iPrinted edition: _z9783031013058
776	0	8	_iPrinted edition: _z9783031035616
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02433-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI