A SAS/IML Companion for Linear Models
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The work A SAS/IML Companion for Linear Models represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
A SAS/IML Companion for Linear Models
Resource Information
The work A SAS/IML Companion for Linear Models represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 A SAS/IML Companion for Linear Models
 Statement of responsibility
 by Jamis J. Perrett
 Language

 eng
 eng
 Summary
 Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using highlevel 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 highlevel 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 highlevel procedures like PROC MIXED is also included for sidebyside comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, nonlinear 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
 Dewey number
 519.535
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 l4gBiODE0E0
 Language note
 English
 LC call number

 QA273.A1274.9
 QA274274.9
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Statistics and Computing,
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