000			03635nam a22004335i 4500
001			978-1-4419-5557-9
003			DE-He213
005			20140220084507.0
007			cr nn 008mamaa
008			100301s2010 xxu| s |||| 0|eng d
020			_a9781441955579 _9978-1-4419-5557-9
024	7		_a10.1007/978-1-4419-5557-9 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aPerrett, Jamis J. _eauthor.
245	1	2	_aA SAS/IML Companion for Linear Models _h[electronic resource] / _cby Jamis J. Perrett.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics and Computing, _x1431-8784
505	0		_aSAS/IML: A Brief Introduction -- IML Language Structure -- IML Programming Features -- Matrix Manipulations in SAS/IML -- Mathematical and Statistical Basics -- Linear Algebra -- The Multivariate Normal Distribution -- The General Linear Model -- Linear Mixed Models -- Statistical Computation Methods.
520			_aLinear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice. Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG that require the user to provide a few options and statements and in return produce vast amounts of output. This book uses PROC IML to show how analytic linear models formulas can be typed directly into PROC IML, as they were presented in the linear models course, and solved using data. This helps students see the link between theory and application. This also assists researchers in developing new methodologies in the area of linear models. The book contains complete examples of SAS code for many of the computations relevant to a linear models course. However, the SAS code in these examples automates the analytic formulas. The code for high-level procedures like PROC MIXED is also included for side-by-side comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, non-linear optimization, etc. in a format conducive to a linear models or a special topics course. Also included in the book is an example of a basic analysis of a linear mixed model using restricted maximum likelihood estimation (REML). The example demonstrates tests for fixed effects, estimates of linear functions, and contrasts. The example starts by showing the steps for analyzing the data using PROC IML and then provides the analysis using PROC MIXED. This allows students to follow the process that lead to the output.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441955562
830		0	_aStatistics and Computing, _x1431-8784
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-5557-9
912			_aZDB-2-SMA
999			_c110481 _d110481