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The evolving field of emergency medical services (EMS) requires professional educators who are knowledgeable about teaching and learning strategies, classroom management, assessment and evaluation, technology in learning, legal implications in education, program infrastructure design, and administering programs of excellence to meet state and national accreditation guidelines. Foundations of Education: An EMS Approach, Third Edition, provides EMS educators with the tools, ideas, and information necessary to succeed in each of these areas. The content reflects how current educational knowledge and theory uniquely apply to EMS students, educators, and programs. This textbook is used in the NAEMSE Instructor Courses, and is an excellent reference for all EMS educators, as well as educators in allied health professions. Evidence-Based Content In addition to foundational topics such as teaching philosophy and classroom management, the text covers brain-based learning, accreditation and program evaluation, emerging technologies, and assessment strategies. It guides educators to write objectives, prepare lesson plans, and deliver education in engaging ways to maximize student learning. Grounded in this information, EMS educators can promote effective education regardless of the type of course or setting. Highlights -Covers current educational theory and teaching methodologies specific to EMS -Meets and exceeds the latest DOT National Guidelines for Educating EMS Instructors -Offer practical advice and scenarios in the form of Teaching Tips and Case in Points
The text presents a broad study of environmental issues and explores economic theories to reinforce the lessons. Offering a long-lasting understanding of real-world environmental problems and policy solutions, this work provides a foundation for the environmental managers of tomorrow.
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.
The classic reference on weeds and invasive plants has been revised and updated. The Third Edition of this authoritative reference provides an in-depth understanding of how weeds and invasive plants develop and interact in the environment so you can manage and control them more effectively. The guide includes an introduction to weeds and invasive plants in various environments and an overview of their ecology and evolution. With extensive examples, this book: Focuses on the biological features of weeds and invasive plants, especially as they exist in agriculture, forests, rangelands, and natural ecosystems. Includes coverage of exotic invasive plants. Discusses a variety of methods and tools for managing weeds and invasive plants, including physical, cultural, biological, and chemical approaches. Examines systems approaches for management, including modern Integrated Pest Management. Addresses future challenges for scientists, farmers, and land managers. This is the definitive, hands-on reference if you're a land manager or professional in plant sciences, agronomy, weed science, and horticulture. The book is also an excellent textbook for senior undergraduate or graduate students studying agriculture, ecology, natural resources management, environmental management, or related fields.
This indispensable guide to mixed models using SAS is completely revised and updated for SAS 9. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures.
A Primer of Ecological Statistics, Second Edition explains fundamental material in probability theory, experimental design, and parameter estimation for ecologists and environmental scientists. The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject. The book is divided into four parts. Part I discusses the fundamentals of probability and statistical thinking. It introduces the logic and language of probability (Chapter 1), explains common statistical distributions used in ecology (Chapter 2) and important measures of central tendency and spread (Chapter 3), explains P-values, hypothesis testing, and statistical errors (Chapter 4), and introduces frequentist, Bayesian, and Monte Carlo methods of analysis (Chapter 5). Part II discusses how to successfully design and execute field experiments and sampling studies. Topics include design strategies (Chapter 6), a 'bestiary' of experimental designs (Chapter 7), and transformations and data management (Chapter 8). Part III discusses specific analyses, and covers the material that is the main core of most statistics texts. Topics include regression (Chapter 9), analysis of variance (Chapter 10), categorical data analysis (Chapter 11), and multivariate analysis (Chapter 12). Part IV—new to this edition—discusses two central topics in estimating important ecological metrics. Topics include quantification of biological diversity (Chapter 13) and estimating occupancy, detection probability, and population sizes from marked and unmarked populations (Chapter 14). The book includes a comprehensive glossary, a mathematical appendix on matrix algebra, and extensively annotated tables and figures. Footnotes introduce advanced and ancillary material: some are purely historical, others cover mathematical/statistical proofs or details, and still others address current topics in the ecological literature. Data files and code used for some of the examples, as well as errata, are available online.