{"product_id":"regression-modeling-strategies-with-applications-to-linear-models-logistic-and-ordinal-regression-and-survival-analysis-paperback","title":"Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eFrank E. Harrell Jr\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eRegression Modelling Strategies \u003c\/i\u003epresents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.\u003c\/p\u003eAs in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques. \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with \"too many variables to analyze and not enough observations,\" and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve \"safe data mining.\" It also presents many graphical methods for communicating complex regression models to non-statisticians.\u003c\/p\u003e\u003cp\u003eRegression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.\u003c\/p\u003e\u003cp\u003eAs in\u003c\/p\u003e the first edition, this text is intended for Masters' or Ph.D. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models (\"analytics\") are useful, including economics, epidemiology, sociology, psychology, engineering, and marketing.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrank E. Harrell, Jr.\u003c\/b\u003e is Professor of Biostatistics and Chair, Department of Biostatistics, Vanderbilt University School of Medicine, Nashville. He has developed numerous methods for predictive modeling, quantifying predictive accuracy and model validation and has published numerous predictive models and articles on applied statistics, medical research and clinical trials. He is on the editorial board for several biomedical and methodologic journals. He is a Fellow of the American Statistical Association (ASA) and a consultant to the U.S. Food and Drug Administration and to the pharmaceutical industry. He teaches a graduate course in regression modeling strategies and a course in biostatistics for medical researchers. In 2014 he was chosen to receive the WJ Dixon Award for Excellence in Statistical Consulting by the ASA. \u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 582\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.23 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 22, 2016\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":45204255866924,"sku":"9783319330396","price":178.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0618\/6260\/8940\/files\/CK4Puin0LK9783319330396.webp?v=1783428904","url":"https:\/\/littleredgeneralstore.com\/products\/regression-modeling-strategies-with-applications-to-linear-models-logistic-and-ordinal-regression-and-survival-analysis-paperback","provider":"Little Red General Store","version":"1.0","type":"link"}