000			04077nam a22004455i 4500
003			ESmaUEC
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
040			_aES-MaUEC _bspa _cES-MaUEC
336			_aTexto _btxt _2rdacontent
710	2		_aSpringerLink (Online service) _9106996
999			_c109367 _d109367 _x1
001			109367
005			20230102113412.0
008			190507s2019 ne | o |||| 0|eng d
020			_a9789402416961
024	7		_a10.1007/978-94-024-1696-1 _2doi
050		4	_aQA402 _b2019 EB
100	1		_aMakowski, David, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _d1972- _9670473
245	1	0	_aFrom Experimental Network to Meta-analysis : _bMethods and Applications with R for Agronomic and Environmental Sciences _cby David Makowski, François Piraux, François Brun.
264		1	_aDordrecht _bSpringer Netherlands _c2019
300			_a1 recurso en línea (X, 155 páginas) _b69 ilustraciones, 43 ilustraciones a color
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aBiomedical and Life Sciences (Springer-11642)
505	0		_aChapter 1. Introduction and examples -- Part I. Analysis of experimental networks -- Chapter 2. Basic Concepts -- Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor -- Chapter 4. Advanced Methods for Network Analysis -- Chapter 5. Planning an Experimental Network -- Part II. The meta-analysis -- Chapter 6. Basics for meta-analysis -- Chapter 7. Specific statistical problems for the meta-analysis -- Annex. R resources to implement the methods of analysis networks and meta-analysis -- Package Codes.
520	3		_aData analysis plays an increasing role in research, scientific expertise and prospective studies. Multiple data sources are often available to estimate a key parameter or to test a hypothesis of scientific or societal interest. These data, obtained under different environmental conditions or based on different experimental protocols, are generally heterogeneous. Sometimes they are not even directly accessible and should be extracted from scientific articles or reports. However, a comprehensive analysis of the available data is essential to increase the accuracy of estimates, assess the validity of research conclusions and understand the origin of the variability of the experimental results. A quantitative synthesis of the data set available allows for a better understanding of the effects of explanatory factors and for evidence-based recommendations. Designed as a methodological guide, this book shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science. Our objective is to present the main statistical methods to analyze data from experimental networks and scientific publications. Each chapter exposes one or more methods and illustrates them with examples processed with the R software. Data and R codes are provided and commented in order to facilitate their adaptation to other situations. The codes can be reused from the KenSyn R package associated with this book.
988			_aPrimersemestre_2019_BiomedLife
650		7	_2embne _aAnálisis de sistemas _9138446
650		7	_2embne _9164496 _aR (Lenguaje de programación)
650		7	_2 _aAgricultura _9665994
700	1		_aBrun, François. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aPiraux, François. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789402416954
776	0	8	_iPrinted edition: _z9789402416978
776	0	8	_iPrinted edition: _z9789402416985
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-94-024-1696-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b09/2019 _ea _zSI