Multivariable Model Building PDF Download
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Author | : Patrick Royston |
Publisher | : John Wiley & Sons |
Total Pages | : 322 |
Release | : 2008-09-15 |
Genre | : Mathematics |
ISBN | : 9780470770788 |
Download Multivariable Model - Building Book in PDF, ePub and Kindle
Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.
Author | : Patrick Royston |
Publisher | : Wiley |
Total Pages | : 322 |
Release | : 2008-07-14 |
Genre | : Mathematics |
ISBN | : 9780470028421 |
Download Multivariable Model - Building Book in PDF, ePub and Kindle
Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This new approach, developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.
Author | : Patrick Royston |
Publisher | : |
Total Pages | : 303 |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Multivariable Model-Building Book in PDF, ePub and Kindle
Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standard approach. Existing options range from very simple step functions to highly complex adaptive methods such as ...
Author | : Douglas C. Montgomery |
Publisher | : John Wiley & Sons |
Total Pages | : 679 |
Release | : 2015-06-29 |
Genre | : Mathematics |
ISBN | : 1119180171 |
Download Introduction to Linear Regression Analysis Book in PDF, ePub and Kindle
Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
Author | : Paul Roback |
Publisher | : CRC Press |
Total Pages | : 436 |
Release | : 2021-01-14 |
Genre | : Mathematics |
ISBN | : 1439885400 |
Download Beyond Multiple Linear Regression Book in PDF, ePub and Kindle
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Author | : John A. Sonquist |
Publisher | : |
Total Pages | : 270 |
Release | : 1970 |
Genre | : Crafts & Hobbies |
ISBN | : |
Download Multivariate Model Building Book in PDF, ePub and Kindle
Author | : John A. Sonquist |
Publisher | : |
Total Pages | : 244 |
Release | : 1975 |
Genre | : |
ISBN | : |
Download Multivariate model building Book in PDF, ePub and Kindle
Author | : Richard McElreath |
Publisher | : CRC Press |
Total Pages | : 488 |
Release | : 2018-01-03 |
Genre | : Mathematics |
ISBN | : 1315362619 |
Download Statistical Rethinking Book in PDF, ePub and Kindle
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Author | : Jhareswar Maiti |
Publisher | : CRC Press |
Total Pages | : 421 |
Release | : 2022-10-25 |
Genre | : Business & Economics |
ISBN | : 1000618420 |
Download Multivariate Statistical Modeling in Engineering and Management Book in PDF, ePub and Kindle
The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.
Author | : Mitchell H. Katz |
Publisher | : Cambridge University Press |
Total Pages | : 228 |
Release | : 2006-02-09 |
Genre | : Medical |
ISBN | : 9780521549851 |
Download Multivariable Analysis Book in PDF, ePub and Kindle
How to perform and interpret multivariable analysis, using plain language rather than complex derivations.