Beyond ANOVA

Beyond ANOVA
Author: Rupert G. Miller, Jr.
Publisher: CRC Press
Total Pages: 340
Release: 1997-01-01
Genre: Mathematics
ISBN: 9780412070112

Renowned statistician R.G. Miller set the pace for statistics students with Beyond ANOVA: Basics of Applied Statistics. Designed to show students how to work with a set of "real world data," Miller's text goes beyond any specific discipline, and considers a whole variety of techniques from ANOVA to empirical Bayes methods; the jackknife, bootstrap methods; and the James-Stein estimator. This reissue of Miller's classic book has been revised by professors at Stanford University, California. As before, one of the main strengths of Beyond ANOVA is its promotion of the use of the most straightforward data analysis methods-giving students a viable option, instead of resorting to complicated and unnecessary tests. Assuming a basic background in statistics, Beyond ANOVA is written for undergraduates and graduate statistics students. Its approach will also be valued by biologists, social scientists, engineers, and anyone who may wish to handle their own data analysis.

Data Analysis

Data Analysis
Author: Charles M. Judd
Publisher:
Total Pages: 0
Release: 2017
Genre: Mathematical statistics
ISBN: 9781138819825

Noted for its model-comparison approach and unified framework based on the general linear model (GLM), this classic text provides readers with a greater understanding of a variety of statistical procedures including analysis of variance (ANOVA) and regression.

Advanced Analysis of Variance

Advanced Analysis of Variance
Author: Chihiro Hirotsu
Publisher: John Wiley & Sons
Total Pages: 432
Release: 2017-08-14
Genre: Mathematics
ISBN: 1119303338

Introducing a revolutionary new model for the statistical analysis of experimental data In this important book, internationally acclaimed statistician, Chihiro Hirotsu, goes beyond classical analysis of variance (ANOVA) model to offer a unified theory and advanced techniques for the statistical analysis of experimental data. Dr. Hirotsu introduces the groundbreaking concept of advanced analysis of variance (AANOVA) and explains how the AANOVA approach exceeds the limitations of ANOVA methods to allow for global reasoning utilizing special methods of simultaneous inference leading to individual conclusions. Focusing on normal, binomial, and categorical data, Dr. Hirotsu explores ANOVA theory and practice and reviews current developments in the field. He then introduces three new advanced approaches, namely: testing for equivalence and non-inferiority; simultaneous testing for directional (monotonic or restricted) alternatives and change-point hypotheses; and analyses emerging from categorical data. Using real-world examples, he shows how these three recognizable families of problems have important applications in most practical activities involving experimental data in an array of research areas, including bioequivalence, clinical trials, industrial experiments, pharmaco-statistics, and quality control, to name just a few. • Written in an expository style which will encourage readers to explore applications for AANOVA techniques in their own research • Focuses on dealing with real data, providing real-world examples drawn from the fields of statistical quality control, clinical trials, and drug testing • Describes advanced methods developed and refined by the author over the course of his long career as research engineer and statistician • Introduces advanced technologies for AANOVA data analysis that build upon the basic ANOVA principles and practices Introducing a breakthrough approach to statistical analysis which overcomes the limitations of the ANOVA model, Advanced Analysis of Variance is an indispensable resource for researchers and practitioners working in fields within which the statistical analysis of experimental data is a crucial research component. Chihiro Hirotsu is a Senior Researcher at the Collaborative Research Center, Meisei University, and Professor Emeritus at the University of Tokyo. He is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and he has been awarded the Japan Statistical Society Prize (2005) and the Ouchi Prize (2006). His work has been published in Biometrika, Biometrics, and Computational Statistics & Data Analysis, among other premier research journals.

Introduction to Mixed Modelling

Introduction to Mixed Modelling
Author: N. W. Galwey
Publisher: John Wiley & Sons
Total Pages: 379
Release: 2007-04-04
Genre: Mathematics
ISBN: 047003596X

Mixed modelling is one of the most promising and exciting areas ofstatistical analysis, enabling more powerful interpretation of datathrough the recognition of random effects. However, many perceivemixed modelling as an intimidating and specialized technique. Thisbook introduces mixed modelling analysis in a simple andstraightforward way, allowing the reader to apply the techniqueconfidently in a wide range of situations. Introduction to Mixed Modelling shows that mixedmodelling is a natural extension of the more familiar statisticalmethods of regression analysis and analysis of variance. In doingso, it provides the ideal introduction to this importantstatistical technique for those engaged in the statistical analysisof data. This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch. Illustrates how the capabilities of regression analysis can becombined with those of ANOVA by the specification of a mixedmodel. Introduces the criterion of Restricted Maximum Likelihood(REML) for the fitting of a mixed model to data. Presents the application of mixed model analysis to a widerange of situations and explains how to obtain and interpret BestLinear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions toexercises, further examples, and links to the computer softwaresystems GenStat and R. This book provides a comprehensive introduction to mixedmodelling, ideal for final year undergraduate students,postgraduate students and professional researchers alike. Readerswill come from a wide range of scientific disciplines includingstatistics, biology, bioinformatics, medicine, agriculture,engineering, economics, and social sciences.

Multiple Regression and Beyond

Multiple Regression and Beyond
Author: Timothy Z. Keith
Publisher: Routledge
Total Pages: 640
Release: 2019-01-14
Genre: Education
ISBN: 1351667939

Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources

Beyond the Numbers

Beyond the Numbers
Author: Edwin P. Christmann
Publisher: National Science Teachers Association
Total Pages: 228
Release: 2012
Genre: Education
ISBN:

Do you shudder when you hear the word ""statistics""? How about a book that explains ""statistics"" in an understandable way and possibly tells you more than you want to know? Look no further. The author of this book appears to ""feel your pain"" and explains how numbers work when working with statistics.

Beyond Multiple Linear Regression

Beyond Multiple Linear Regression
Author: Paul Roback
Publisher: CRC Press
Total Pages: 436
Release: 2021-01-14
Genre: Mathematics
ISBN: 1439885400

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)