Applied Regression and ANOVA Using SAS
Taylor & Francis

Applied Regression and ANOVA Using SAS

Edition: 1st Edition
Subjects: Maths & Science, Mathematics & science
ISBN13: 9781439869512
Published: 19 Jul 1944

Format - Hardback
By Patricia F. Moodie

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Regular price A$129.60
Sale price A$129.60 Regular price A$162.00

Applied Regression and ANOVA Using SAS

Regular price A$129.60
Sale price A$129.60 Regular price A$162.00
Product description

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 calculu•Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphic•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 selectio•Suggestions of alternative approaches when a method’s ideal inference conditions are unreasonable for one’s dataThis 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.

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