The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysi
Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines. - Features real-data to give students an engaging practice to connect with their areas of interest - Evolves from basic problems that can be worked by hand to the elementary use of opensource R software - Offers a direct, clear approach highlighted by useful visuals and examples
This book will be written by experts for professionals, scientists and all those involved in toxicological data generation and decision-making. It is the updated and expanded version of a monograph published in German in 2004. Chemical safety is regulated on various levels including production, storage, transport, handling, disposal or labelling. This book deals comprehensively with the safety-ensuring methods and concepts employed by regulatory agencies, industry and academics. Toxicologists use experimental and scientific approaches for data collection, e.g. about chemical hazards, physicochemical features or toxicokinetics. The respective experimental methods are described in the book. Toxicologists also deal with much insecurity in the exposure and effect scenarios during risk assessment. To overcome these, they have different extrapolation methods and estimation procedures at their disposal. The book describes these methods in an accessible manner. Differing concepts from one regulation area to another are also covered. Reasons and consequences become evident when reading the book. Altogether, the book Regulatory Toxicology will serve as an excellent reference.
Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.
This book explains how the computer programs work and why and when they can be applied to problems in toxicology. It discusses the statistical models used and their applications in a general fashion. The book overviews the problems that can arise from the blind use of the statistical models.
This collection of papers on aspects of statistics in toxicology is will be of interest to all medical statisticians. It offers findings from numerous leading experts from around the world including A. Whitehead and R. N. Connor (University of Reading), L. Ryan (Harvard), A. P. Grieve (Pfizer Research), K.J. Risko (Northern Telecom), and B.H. Margolin (University of North Carolina). This is the latest in the popular Royal Statistical Society Lecture Series, and will be essential reading for all those involved in this area.
Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.
This new fifth edition of Information Resources in Toxicology offers a consolidated entry portal for the study, research, and practice of toxicology. Both volumes represents a unique, wide-ranging, curated, international, annotated bibliography, and directory of major resources in toxicology and allied fields such as environmental and occupational health, chemical safety, and risk assessment. The editors and authors are among the leaders of the profession sharing their cumulative wisdom in toxicology's subdisciplines. This edition keeps pace with the digital world in directing and linking readers to relevant websites and other online tools.Due to the increasing size of the hardcopy publication, the current edition has been divided into two volumes to make it easier to handle and consult. Volume 1: Background, Resources, and Tools, arranged in 5 parts, begins with chapters on the science of toxicology, its history, and informatics framework in Part 1. Part 2 continues with chapters organized by more specific subject such as cancer, clinical toxicology, genetic toxicology, etc. The categorization of chapters by resource format, for example, journals and newsletters, technical reports, organizations constitutes Part 3. Part 4 further considers toxicology's presence via the Internet, databases, and software tools. Among the miscellaneous topics in the concluding Part 5 are laws and regulations, professional education, grants and funding, and patents. Volume 2: The Global Arena offers contributed chapters focusing on the toxicology contributions of over 40 countries, followed by a glossary of toxicological terms and an appendix of popular quotations related to the field.The book, offered in both print and electronic formats, is carefully structured, indexed, and cross-referenced to enable users to easily find answers to their questions or serendipitously locate useful knowledge they were not originally aware they needed. Among the many timely topics receiving increased emphasis are disaster preparedness, nanotechnology, -omics, risk assessment, societal implications such as ethics and the precautionary principle, climate change, and children's environmental health. - Introductory chapters provide a backdrop to the science of toxicology, its history, the origin and status of toxicoinformatics, and starting points for identifying resources - Offers an extensive array of chapters organized by subject, each highlighting resources such as journals, databases,organizations, and review articles - Includes chapters with an emphasis on format such as government reports, general interest publications, blogs, and audiovisuals - Explores recent internet trends, web-based databases, and software tools in a section on the online environment - Concludes with a miscellany of special topics such as laws and regulations, chemical hazard communication resources, careers and professional education, K-12 resources, funding, poison control centers, and patents - Paired with Volume Two, which focuses on global resources, this set offers the most comprehensive compendium of print, digital, and organizational resources in the toxicological sciences with over 120 chapters contributions by experts and leaders in the field
Statistical Models in Toxicology presents an up-to-date and comprehensive account of mathematical statistics problems that occur in toxicology. This is as an exciting time in toxicology because of the attention given by statisticians to the problem of estimating the human health risk for environmental and occupational exposures. The development of modern statistical techniques with solid mathematical foundations in the 20th century and the advent of modern computers in the latter part of the century gave way to development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically, they are widely used by industry and government regulatory agencies. Features: Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment, risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or as use as a textbook for advanced undergraduate or graduate students of mathematics and statistics.
A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.
