This book outlines the creative process of making environmental management decisions using the approach called Structured Decision Making. It is a short introductory guide to this popular form of decision making and is aimed at environmental managers and scientists.This is a distinctly pragmatic label given to ways for helping individuals and groups think through tough multidimensional choices characterized by uncertain science, diverse stakeholders, and d...

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share n...

Stack the odds in your favor for mastering probabilityDon't leave your knowledge of probability to chance. Instead, turn to Probability Demystified, Second Edition, for learning fundamental concepts and theories step-by-step.This practical guide eases you into the subject of probability using familiar items such as coins, cards, and dice. As you progress, you will master concepts such as addition and multiplication rules, odds and expectation, probability distrib...

The systematic analysis of baseball statistics, often called "sabermetrics," has evolved in recent years to resemble something of a science, attracting fans from diverse professional and educational backgrounds, all fascinated by the analysis itself and its insights into the game. But one problem has defied solution: estimating runs saved by fielders throughout history. Traditional statistics include errors and plays made, but not hits that could or should have been prevented. The latter c...

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introduci...

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and me...

Fully updated for SAS 9.2, Ron Cody's SAS Functions by Example, Second Edition, is a must-have reference for anyone who programs in Base SAS. With the addition of functions new to SAS 9.2, this comprehensive reference manual now includes more than 200 functions, including new character, date and time, distance, probability, sort, and special functions. This new edition also contains more examples for existing functions and more details concerning optional arguments. Like the first edition,...

Combining theoretical, methodological, and practical aspects, Latent Class Analysis of Survey Error successfully guides readers through the accurate interpretation of survey results for quality evaluation and improvement. This book is a comprehensive resource on the key statistical tools and techniques employed during the modeling and estimation of classification errors, featuring a special focus on both latent class analysis (LCA) techniques and models for categorical data from complex sa...

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast v...

Do you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures.Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get t...

This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametric approaches to data analysis are appropriateTo introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test...

This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis.• Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects• Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and ...