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[null,"1.","Record Nr.",null,"UNINA9910821784203321"]
[null,null,"Autore",null,"Moodie Patricia"]
[null,null,"Titolo",null,"Applied regression and ANOVA using SAS / / Patricia F. Moodie, Dallas E. Johnson"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton, Florida : , : CRC Press, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"1-4398-6952-9"]
[{"$nulls":4},"0-429-10736-6"]
[{"$nulls":4},"0-429-52703-9"]
[null,null,"Descrizione fisica",null,"1 online resource (428 pages)"]
[null,null,"Disciplina",null,"519.536"]
[null,null,"Soggetti",null,"Regression analysis"]
[{"$nulls":4},"SAS (Computer program language)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Review 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"]
[null,null,null,"Sommario/riassunto","\"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 "]
[{"$nulls":4},"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\"--"]