LEADER 04201nam 2200469 450 001 9910821784203321 005 20221203202816.0 010 $a1-4398-6952-9 010 $a0-429-10736-6 010 $a0-429-52703-9 035 $a(MiAaPQ)EBC6985643 035 $a(Au-PeEL)EBL6985643 035 $a(CKB)22283697500041 035 $a(EXLCZ)9922283697500041 100 $a20221203d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied regression and ANOVA using SAS /$fPatricia F. Moodie, Dallas E. Johnson 210 1$aBoca Raton, Florida :$cCRC Press,$d[2021] 210 4$d©2021 215 $a1 online resource (428 pages) 311 08$aPrint version: Moodie, Patricia F. Applied Regression and ANOVA Using SAS Milton : CRC Press LLC,c2019 9781439869512 320 $aIncludes bibliographical references and index. 327 $aReview of some basic statistical ideas -- Introduction to simple linear regression -- Model checking in simple linear regression -- Interpreting a simple linear regression analysis -- Introduction to multiple linear regression -- Before interpreting a multiple linear regression analysis -- Interpreting an additive multiple linear regression model -- Modelling a two-way interaction between continuous predictors in multiple linear regression -- Evaluating a two-way interaction between a qualitative and a continuous predictor in multiple linear regression -- Subset selection of predictor variables in multiple linear regression -- Evaluating equality of group means with a one-way analysis of variance -- Multiple testing and simultaneous confidence intervals -- Analysis of covariance : adjusting group means for nuisance variables using regression -- Alternative approaches if ideal inference conditions are not satisfied 330 $a"Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps. Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided. Features: Statistical concepts presented in words without matrix algebra and calculus Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection Suggestions of alternative approaches when a method's ideal inference conditions are unreasonable for one's data This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics"--$cProvided by publisher. 606 $aRegression analysis 606 $aSAS (Computer program language) 615 0$aRegression analysis. 615 0$aSAS (Computer program language) 676 $a519.536 700 $aMoodie$b Patricia$01709392 702 $aJohnson$b Dallas E.$f1938- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821784203321 996 $aApplied regression and ANOVA using SAS$94099115 997 $aUNINA