000 06023cam a2200769Mi 4500
001 ocn887507303
003 OCoLC
005 20171026113439.0
006 m o d
007 cr cnu---unuuu
008 140816s2014 nju o 000 0 eng d
020 _a9781118861769
_q(electronic bk.)
020 _a1118861760
_q(electronic bk.)
020 _z9781118861813
020 _z1118861817
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035 _a(OCoLC)887507303
040 _aEBLCP
_beng
_epn
_cEBLCP
_dMHW
_dDG1
_dE7B
_dDEBSZ
_dOCLCQ
_dCHVBK
_dOHI
_dOCLCF
_dOCLCQ
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_dOCLCQ
_dK6U
049 _aMAIN
050 4 _aQA276
082 0 4 _a519.5
_223
100 1 _aGalwey, Nick.
245 1 0 _aIntroduction to Mixed Modelling : Beyond Regression and Analysis of Variance /
_h[electronic resource]
250 _a2nd ed.
260 _aHoboken :
_bWiley,
_c2014.
300 _a1 online resource (504 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aCover; Title Page; Copyright; Contents; Preface; Chapter 1 The need for more than one random-effect term when fitting a regression line; 1.1 A data set with several observations of variable Y at each value of variable X; 1.2 Simple regression analysis: Use of the software GenStat to perform the analysis; 1.3 Regression analysis on the group means; 1.4 A regression model with a term for the groups; 1.5 Construction of the appropriate F test for the significance of the explanatory variable when groups are present; 1.6 The decision to specify a model term as random: A mixed model.
505 8 _a1.7 Comparison of the tests in a mixed model with a test of lack of fit; 1.8 The use of REsidual Maximum Likelihood (REML) to fit the mixed model; 1.9 Equivalence of the different analyses when the number of observations per group is constant; 1.10 Testing the assumptions of the analyses: Inspection of the residual values; 1.11 Use of the software R to perform the analyses; 1.12 Use of the software SAS to perform the analyses; 1.13 Fitting a mixed model using GenStat's Graphical User Interface (GUI); 1.14 Summary; 1.15 Exercises; References.
505 8 _aChapter 2 The need for more than one random-effect term in a designed experiment; 2.1 The split plot design: A design with more than one random-effect term; 2.2 The analysis of variance of the split plot design: A random-effect term for the main plots; 2.3 Consequences of failure to recognize the main plots when analysing the split plot design; 2.4 The use of mixed modelling to analyse the split plot design; 2.5 A more conservative alternative to the F and Wald statistics; 2.6 Justification for regarding block effects as random.
505 8 _a2.7 Testing the assumptions of the analyses: Inspection of the residual values; 2.8 Use of R to perform the analyses; 2.9 Use of SAS to perform the analyses; 2.10 Summary; 2.11 Exercises; References; Chapter 3 Estimation of the variances of random-effect terms; 3.1 The need to estimate variance components; 3.2 A hierarchical random-effects model for a three-stage assay process; 3.3 The relationship between variance components and stratum mean squares; 3.4 Estimation of the variance components in the hierarchical random-effects model; 3.5 Design of an optimum strategy for future sampling.
505 8 _a3.6 Use of R to analyse the hierarchical three-stage assay process; 3.7 Use of SAS to analyse the hierarchical three-stage assay process; 3.8 Genetic variation: A crop field trial with an unbalanced design; 3.9 Production of a balanced experimental design by `padding'' with missing values; 3.10 Specification of a treatment term as a random-effect term: The use of mixed-model analysis to analyse an unbalanced data set; 3.11 Comparison of a variance component estimate with its standard error; 3.12 An alternative significance test for variance components.
505 8 _a3.13 Comparison among significance tests for variance components.
520 _aThis book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs) estimates of individual effects that take account of their random nature. It is intended to be an introductory guide to a relatively advanced specialised topic, and to convince the reader that mixed modelling is neither so specia.
588 0 _aPrint version record.
650 0 _aMultilevel models (Statistics)
650 0 _aExperimental design.
650 0 _aRegression analysis.
650 0 _aAnalysis of variance.
650 7 _aAnalysis of variance.
_2fast
_0(OCoLC)fst00808330
650 7 _aExperimental design.
_2fast
_0(OCoLC)fst00918404
650 7 _aMultilevel models (Statistics)
_2fast
_0(OCoLC)fst01028902
650 7 _aRegression analysis.
_2fast
_0(OCoLC)fst01432090
650 7 _aGemischtes Modell.
_0(DE-588)4156565-4
_2gnd
650 7 _aVarianzanalyse.
_0(DE-588)4187413-4
_2gnd
650 7 _aRegressionsanalyse.
_0(DE-588)4129903-6
_2gnd
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aGalwey, N.W.
_tIntroduction to Mixed Modelling : Beyond Regression and Analysis of Variance.
_dHoboken : Wiley, ©2014
_z9781119945499
856 4 0 _uhttp://onlinelibrary.wiley.com/book/10.1002/9781118861769
_zWiley Online Library
942 _2ddc
_cBK
999 _c207626
_d207626