000			05272nam a22003974i 4500
005			20250919002744.0
008			150317s2012 flua bi 001 0 eng
020			_a9781439873656 _q(hardback) _cRM327.39
020			_a1439873658 _q(hardback)
039		9	_a201506100851 _blan _c201506040907 _dlan _c201506021600 _dhaiyati _c201505270930 _dhamudah _y03-17-2015 _zhamudah
040			_aDLC _beng _cDLC _dYDX _dBTCTA _dYDXCP _dBWX _dIG# _dUKMGB _dUPM _dESUDE _dCDX _dUBY _dSTF _dNLGGC _dOCLCQ _dOCLCF _dOCLCQ _dCRCPR _dOCLCQ _dOCLCO _dUKM _erda
090			_aQA279.H39.C487
090			_aQA279 _b.H39.C487
100	1		_aHay-Jahans, Christopher _eauthor.
245	1	3	_aAn R companion to linear statistical models / _cChristopher Hay-Jahans.
264		1	_aBoca Raton, FL : _bCRC Press, _cò012.
264		4	_a© 2012.
300			_axvii, 354 pages : _billustrations ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aNote continued: 13.5.2. Without a control treatment -- 13.6. Tukey's Nonadditivity Test -- 13.7. For the Curious -- 13.7.1. Bonferroni's pairwise comparisons -- 13.7.2. Generating data to play with -- 14. Two-Factor Models -- 14.1. Introduction -- 14.2. Exploratory Data Analysis -- 14.3. Model Construction and Fit -- 14.4. Diagnostics -- 14.5. Pairwise Comparisons of Treatment Effects -- 14.5.1. With a control treatment -- 14.5.2. Without a control treatment -- 14.6. What if Interaction Effects Are Significant? -- 14.7. Data with Exactly One Observation per Cell -- 14.8. Two-Factor Models with Covariates -- 14.9. For the Curious: Scheffe's F-Tests -- 15. Simple Remedies for Fixed-Effects Models -- 15.1. Introduction -- 15.2. Issues with the Error Assumptions -- 15.3. Missing Variables -- 15.4. Issues Specific to Covariates -- 15.4.1. Multicollinearity -- 15.4.2. Transformations of covariates -- 15.4.3. Blocking as an alternative to covariates -- 15.5. For the Curious.
520			_a'Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: Those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.'-- _cProvided by publisher.
520			_a'Preface This work (referred to as Companion from here on) targets two primary audiences: Those who are familiar with the theory and applications of linear statistical models and wish to learn how to use R or supplement their abilities with R through unfamiliar ideas that might appear in this Companion; and those who are enrolled in a course on linear statistical models for which R is the computational platform to be used. About the Content and Scope While applications of several pre-packaged functions for complex computational procedures are demonstrated in this Companion, the focus is on programming with applications to methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. The intent in compiling this Companion has been to provide as comprehensive a coverage of these topics as possible, subject to the constraint on the Companion's length. The reader should be aware that much of the programming code presented in this Companion is at a fairly basic level and, hence, is not necessarily very elegant in style. The purpose for this is mainly pedagogical; to match instructions provided in the code as closely as possible to computational steps that might appear in a variety of texts on the subject. Discussion on statistical theory is limited to only that which is necessary for computations; common'rules of thumb' used in interpreting graphs and computational output are provided. An effort has been made to direct the reader to resources in the literature where the scope of the Companion is exceeded, where a theoretical refresher might be useful, or where a deeper discussion may be desired. The bibliography lists a reasonable starting point for further references at a variety of levels'-- _cProvided by publisher.
650		0	_aLinear models (Statistics).
650		0	_aR (Computer program language).
907			_a.b16095972 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA279.H39.C487
914			_avtls003581088
990			_anh
991			_aFakulti Sains dan Teknologi
998			_at _b2015-04-03 _cm _da _feng _gflu _y0 _z.b16095972
999			_c588668 _d588668