Analysis Of Messy Data Volume Iii
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| Author | : George A. Milliken |
| Publisher | : CRC Press |
| Total Pages | : 625 |
| Release | : 2001-08-29 |
| Genre | : Mathematics |
| ISBN | : 1420036181 |
Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. Analysis of Messy Data, Volume 3: Analysis of Covariance takes the unique approach of treating the analysis of covariance problem by looking
| Author | : George A. Milliken |
| Publisher | : CRC Press |
| Total Pages | : 216 |
| Release | : 2017-01-06 |
| Genre | : Mathematics |
| ISBN | : 1351697129 |
Researchers often do not analyze nonreplicated experiments statistically because they are unfamiliar with existing statistical methods that may be applicable. Analysis of Messy Data, Volume II details the statistical methods appropriate for nonreplicated experiments and explores ways to use statistical software to make the required computations feasible.
| Author | : George A. Milliken |
| Publisher | : CRC Press |
| Total Pages | : 690 |
| Release | : 2009-03-02 |
| Genre | : Mathematics |
| ISBN | : 1420010158 |
A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researchers analyze the kinds of data sets encountered in the real world. Written by two long-time researchers and professors, this second edition has been fully updated to reflect the many developments that have occurred since t
| Author | : Christopher R. Bilder |
| Publisher | : CRC Press |
| Total Pages | : 1029 |
| Release | : 2024-07-31 |
| Genre | : Mathematics |
| ISBN | : 1040087760 |
Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated. Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.
| Author | : Marc S. Paolella |
| Publisher | : John Wiley & Sons |
| Total Pages | : 900 |
| Release | : 2018-10-10 |
| Genre | : Mathematics |
| ISBN | : 1119431859 |
A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
| Author | : Mervyn G. Marasinghe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 562 |
| Release | : 2008-12-10 |
| Genre | : Mathematics |
| ISBN | : 038777372X |
This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are p- marily graduate majors or advanced undergraduate students from a variety of disciplines. These students typically have taken an introductory-level s- tistical methods course that requires the use a software system such as SAS for performing statistical analysis. Thus students are expected to have an - derstanding of basic concepts of statistical inference such as estimation and hypothesis testing. Understandably, adequate time is not available in a ?rst course in stat- tical methods to cover the use of a software system adequately in the amount of time available for instruction. The aim of this book is to teach how to use the SAS system for data analysis. The SAS language is introduced at a level of sophistication not found in most introductory SAS books. Important features such as SAS data step programming, pointers, and line-hold spe- ?ers are described in detail. The powerful graphics support available in SAS is emphasized throughout, and many worked SAS program examples contain graphic components.
| Author | : Josafhat Salinas Ruíz |
| Publisher | : Springer Nature |
| Total Pages | : 267 |
| Release | : |
| Genre | : |
| ISBN | : 3031655753 |
| Author | : Harriet B. Nembhard |
| Publisher | : CRC Press |
| Total Pages | : 354 |
| Release | : 2019-06-13 |
| Genre | : Technology & Engineering |
| ISBN | : 0429947712 |
Success is driven through collaboration. The field of Industrial and Systems Engineering has evolved as a major engineering field with interdisciplinary strength drawn from effective utilization, process improvement, optimization, design, and management of complex systems. It is a broad discipline that is important to nearly every attempt to solve problems facing the needs of society and the welfare of humanity. In order to carry this forward, successful collaborations are needed between industry, government, and academia. This book brings together an international group of distinguished practitioners and academics in manufacturing, healthcare, logistics, and energy sectors to examine what enables successful collaborations. The book is divided into two key parts: 1) partnerships, frameworks, and leadership; and 2) engineering applications and case studies. Part I highlights some of the ways partnerships emerge between those seeking to innovate and educate in industrial and systems engineering, some useful frameworks and methodologies, as well as some of the ideas and practices that undergird leadership in the profession. Part II provides case studies and applications to illustrate the power of the partnerships between academia and practice in industrial and systems engineering. Features Examines the success from multiple industries Provides frameworks for building teams and avoiding pitfalls Contains international perspectives of success Uses collaborative approaches from industry, government, and academia Includes real world case studies illustrating the enabling factors Offers engineering education and student-centric takeaways
| Author | : Michael C. Calver |
| Publisher | : CSIRO PUBLISHING |
| Total Pages | : 354 |
| Release | : 2024-05-01 |
| Genre | : Science |
| ISBN | : 148631502X |
Quantifying Diets of Wildlife and Fish presents different techniques available to study animal diets. Ecologists determine animal diets to build natural history knowledge, test hypotheses in ecological theory and make informed management decisions for important ecosystems. Many researchers use techniques traditionally applied to the animals they study, rather than techniques with the greatest potential for the aims of each project. In an effort to encourage researchers to consider new approaches, this book focuses on the techniques, rather than on particular groups of organisms or specific environments. With contributions from leading ecologists, chapters explore experimental design, observational techniques (including new technologies), stomach contents and faecal analysis, eDNA, tracers and stable isotopes. They also cover the latest multivariate methods of analyses suitable for describing animal diets and feeding relationships, as well as testing hypotheses relevant to ecological theory, environmental management and biological conservation. The expert knowledge provided will encourage readers to look beyond the boundaries of their specialties, assist in testing important hypotheses and provide insights into management problems. The examples in this book cover a range of vertebrates and invertebrates, as well as different environments, to open these methods up for novice ecologists and stimulate lateral thinking in more experienced researchers.
| Author | : Mervyn G. Marasinghe |
| Publisher | : Springer |
| Total Pages | : 688 |
| Release | : 2018-04-12 |
| Genre | : Computers |
| ISBN | : 3319692399 |
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
