Recent classifications of Australian birds have been limited to lists of "species" which are inadequate as biodiversity indicators. The Directory of Australian Birds: Passerines fills a huge gap in ornithological knowledge by separating out and listing not only 340 species of song-birds but also the 720 distinct regional forms. Covering about half the national bird fauna, the Directory provides science and the community with baseline information about what bird it is and where it lives in an Australia-wide context. Identity is taken down to the level of distinct regional population. No other compendium on Australian birds does this.
Recent classifications of Australian birds have been limited to lists of "species" which are inadequate as biodiversity indicators. The Directory of Australian Birds: Passerines fills a huge gap in ornithological knowledge by separating out and listing not only 340 species of song-birds but also the 720 distinct regional forms. Covering about half the national bird fauna, the Directory provides science and the community with baseline information about what bird it is and where it lives in an Australia-wide context. Identity is taken down to the level of distinct regional population. No other compendium on Australian birds does this.
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.
A hands-on guide to using R to carry out key statisticalpractices in educational and behavioral sciencesresearch Computing has become an essential part of the day-to-daypractice of statistical work, broadening the types of questionsthat can now be addressed by research scientists applying newlyderived data analytic techniques. Comparing Groups:Randomization and Bootstrap Methods Using R emphasizes thedirect link between scientific research questions and dataanalysis. Rather than relying on mathematical calculations, thisbook focus on conceptual explanations and the use of statisticalcomputing in an effort to guide readers through the integration ofdesign, statistical methodology, and computation to answer specificresearch questions regarding group differences. Utilizing the widely-used, freely accessible R software, theauthors introduce a modern approach to promote methods that providea more complete understanding of statistical concepts. Following anintroduction to R, each chapter is driven by a research question,and empirical data analysis is used to provide answers to thatquestion. These examples are data-driven inquiries that promoteinteraction between statistical methods and ideas and computerapplication. Computer code and output are interwoven in the book toillustrate exactly how each analysis is carried out and how outputis interpreted. Additional topical coverage includes: Data exploration of one variable and multivariate data Comparing two groups and many groups Permutation tests, randomization tests, and the independentsamples t-Test Bootstrap tests and bootstrap intervals Interval estimates and effect sizes Throughout the book, the authors incorporate data fromreal-world research studies as well aschapter problems that providea platform to perform data analyses. A related Web site features acomplete collection of the book's datasets along with theaccompanying codebooks and the R script files and commands,allowing readers to reproduce the presented output and plots. Comparing Groups: Randomization and Bootstrap Methods UsingR is an excellent book for upper-undergraduate and graduatelevel courses on statistical methods, particularlyin theeducational and behavioral sciences. The book also serves as avaluable resource for researchers who need a practical guide tomodern data analytic and computational methods.
The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.
The fields of mathematical statistics, statistical graphics, computer science and operations research have created the rich set of methods now called Analytics. Often analytics is characterized along three poles: descriptive analytics (what do data tell us), predictive analytics (what can be forecast based on the data, and with what certainty), and prescriptive analytics (how can the data inform changes to improve system performance).This book focuses on the second pole, predictive analytics. The areas of predicting a number, a class, and dynamic behavior are distinct, with different methods. This text has three parts based on these areas. Topics in predicting a number include simple and multiple linear regression, transformation of variables, analysis of observational data via cross-validation, the generalized linear model, designed experiments, and Gaussian process and neural network regression. Classification methods include neural networks, logistic regression, k-nearest neighbor, and linear discriminant analysis. Methods for predicting dynamic behavior include trend analysis, time series analysis and discrete-event dynamic simulation.Characterizing prediction uncertainty is a key focus of this text. The text provides analytic methods appropriate to each area, with an explicit process for applying such methods. Case data with corresponding R code are used to illustrate each method.Predictive Analytics for Business using R is designed for a hybrid class structure. Class sessions can be a blend of lecture format and flipped classroom case analyses. In a two-meetings-per-week fifteen-week structure, one day per week would be devoted to explaining methodology and presenting a case study, with the second day focused on coaching. Given the case structure, the text does not contain homework problems. Instead, at the end of each chapter there are links to cases posted online.
Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysis Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesExplore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysisFind out how to work with different data types such as strings, numbers, date, and timeBuild your first model and visualize data with ease through advanced plot types and with ggplot2Book Description In this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data. Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization. The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards. By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R. What you will learnDiscover how to load datasets and explore data in RWork with different types of variables in datasetsCreate basic and advanced visualizationsFind out how to build your first data modelCreate graphics using ggplot2 in a step-by-step way in Microsoft Power BIGet familiarized with building an application in R with ShinyWho this book is for If you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.
This book introduces Probability Theory with R software and explains abstract concepts in a simple and easy-to-understand way by combining theory and computation. It discusses conceptual and computational examples in detail, to provide a thorough understanding of basic techniques and develop an enjoyable read for students seeking suitable material for self-study. It illustrates fundamental concepts including fields, sigma-fields, random variables and their expectations, various modes of convergence of a sequence of random variables, laws of large numbers and the central limit theorem. Computational exercises based on R software are included in each Chapter Includes a brief introduction to the basic functions of R software for beginners in R and serves as a ready reference Includes Numerical computations, simulation studies, and visualizations using R software as easy tools to explain abstract concepts Provides multiple-choice questions for practice Incorporates self-explanatory R codes in every chapter This textbook is for advanced students, professionals, and academic researchers of Statistics, Biostatistics, Economics and Mathematics.
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
This book focuses on group sequential methods for clinical trials with co-primary endpoints based on the decision-making frameworks for: (1) rejecting the null hypothesis (stopping for efficacy), (2) rejecting the alternative hypothesis (stopping for futility), and (3) rejecting the null or alternative hypothesis (stopping for either futility or efficacy), where the trial is designed to evaluate whether the intervention is superior to the control on all endpoints. For assessing futility, there are two fundamental approaches, i.e., the decision to stop for futility based on the conditional probability of rejecting the null hypothesis, and the other based on stopping boundaries using group sequential methods. In this book, the latter approach is discussed. The book also briefly deals with the group sequential methods for clinical trials designed to evaluate whether the intervention is superior to the control on at least one endpoint. In addition, the book describes sample size recalculation and the resulting effect on power and type I error rate. The book also describes group sequential strategies for three-arm clinical trials to demonstrate the non-inferiority of experimental intervention to actively control and to assess the assay sensitivity to placebo control.
This is a printed edition of the reference documentation for R (version 2.13.x), a powerful free environment for statistical computing and graphics. This is the third volume which covers the statistical functions of R. There are four volumes in the complete set of manuals. All the manuals are written by the developers of R, and represent the definitive guide to the software. The manuals explain the use of each command in detail, with references, implementation notes and examples. Every volume includes a comprehensive index of entries listed by keyword and topic. For each volume sold 1 USD is donated by the publisher (Network Theory Ltd) to the R Foundation to support the development of the software.
How to identify optimal phase II trial designs Providing a practical guide containing the information needed to make crucial decisions regarding phase II trial designs, A Practical Guide to Designing Phase II Trials in Oncology sets forth specific points for consideration between the statistician and clinician when designing a phase II trial, including issues such as how the treatment works, choice of outcome measure and randomization, and considering both academic and industry perspectives. A comprehensive and systematic library of available phase II trial designs is included, saving time otherwise spent considering multiple manuscripts, and real-life practical examples of using this approach to design phase II trials in cancer are given. A Practical Guide to Designing Phase II Trials in Oncology: Offers a structured and practical approach to phase II trial design Considers trial design from both an academic and industry perspective Includes a structured library of available phase II trial designs Is relevant to both clinical and statistical researchers at all levels Includes real life examples of applying this approach For those new to trial design, A Practical Guide to Designing Phase II Trials in Oncology will be a unique and practical learning tool, providing an introduction to the concepts behind informed decision making in phase II trials. For more experienced practitioners, the book will offer an overview of new, less familiar approaches to phase II trial design, providing alternative options to those which they may have previously used.
This tutorial manual provides an introduction to R, a software package for statistical computing and graphics. This revised and updated edition is based on the April 2009 release of R (version 2.9.0).
From the Preface: It appears to be sometimes supposed that a critical view of the literature and history of the Old Testament is incompatible with any real sense for the spiritual and moral teaching which it contains: it is my hope and wish that the pages which follow may suffice to show how very far this is from being the case.
An in-depth reference to owls around the world, "Owls of the World" traces the remarkable evolution of 205 owl species and their place within the avian order as both predators and prey.
This book provides an in-depth review of the historical and state-of-the-art use of technology by and for individuals with autism. The design, development, deployment, and evaluation of interactive technologies for use by and with individuals with autism have been rapidly increasing over the last few decades. There is great promise for the use of these technologies to enrich lives, improve the experience of interventions, help with learning, facilitate communication, support data collection, and promote understanding. Emerging technologies in this area also have the potential to enhance assessment and diagnosis of autism, to understand the nature and lived experience of autism, and to help researchers conduct basic and applied research. The intention of this book is to give readers a comprehensive background for understanding what work has already been completed and its impact as well as what promises and challenges lie ahead. A large majority of existing technologies have been designed for autistic children, there is increased interest in technology’s intersection with the lived experiences of autistic adults. By providing a classification scheme and general review, this book can help technology designers, researchers, autistic people, and their advocates better understand how technologies have been successful or unsuccessful, what problems remain open, and where innovations can further address challenges and opportunities for individuals with autism and the variety of stakeholders connected to them.
Chromosome biology has been brought to a golden age by phenomenal advanced in molecular genetics and techniques. This is true in the plant arena, and it is becoming increasingly true in animal studies, where chromosomes are more difficult to work with. With advanced knowledge of transformation, scientists can tell exactly where a new element enters a chromosome. Conversely, molecular biologists can make large mistakes if they do not understand the behavior of chromosomes. Written by internationally recognized experts in the field, this book is the most authoritative work on the subject to date. Students of genetics, crop science and plant breeding, entomology, animal science, and related fields will benefit from this comprehensive and practical textbook.
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