Although the web and online SAS® communities can provide volumes of information for programmers, these resources are often overwhelming and lack a simple path to guide coding SAS. This reference, however, does provide such a path from a data user’s standpoint vs. seeing things as a code writer. Written by an experienced SAS programmer, this book lets SAS coders easily find explanations and clarification to typical programming problems. This book presents practical real-world data analysis steps encountered by analysts in the field. These steps include the following: Getting to know raw data Understanding variables Getting data into SAS Creating new data variables Performing data manipulations, including sorting, ranking, grouping, subtotal, total, and percentage Statistical testing under a broad range of logical and conditional settings Data visualization Throughout this book, statements and codes are accompanied by thorough annotation. Line-by-line explanations ensure that all terms are clearly explained. Code examples and sample codes have broad usages. All the examples are related to highway transportation where the use of big data is exploding and presenting new challenges and opportunities for growth. Clear and precise practical introductory material on statistics is integrated into the relevant SAS procedures to bolster users’ confidence in applying such methods to their own work. Comprehensive and foundational coverage, systematic introduction of programming topics, thoroughly annotated code examples, and real-world code samples combine to make SAS® Coding Primer and Reference Guide an indispensable reference for beginners and experienced programmers.

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. Rather than explore the many options available for every command as well as the ever-increasing number of packages, the book focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point. The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated. While base R is used throughout, other functions or packages are listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R. Additional topics and R code are available from the book’s supporting website at www.statistics.life.ku.dk/primer/

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.

Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Provides simple explanations of the important concepts in population and community ecology. Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language. Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.

An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id

Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you: Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theory Data management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

This easy-to-understand guide makes SEM accessible to all users. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Get outside! A hands-on lab manual for instructors incorporating fieldwork into their courses on mammalogy. Mammals inhabit nearly every continent and every sea. They have adapted to life underground, in the frozen Arctic, the hottest deserts, and every habitat in-between. In Mammalogy Techniques Lab Manual—the only field manual devoted to training the next generation of mammalogists—biologist and educator James M. Ryan details the modern research techniques today’s professionals use to study mammals wherever they are found. Ideal for any mammalogy or wildlife biology course, this clear and practical guide aids students by getting them outside to study mammals in their natural environments. Twenty comprehensive chapters cover skull and tooth identification, radio and satellite GPS tracking, phylogeny construction, mark and recapture techniques, camera trapping, museum specimen preparation, optimal foraging, and DNA extraction, among other topics. Each chapter includes several exercises with step-by-step instructions for students to collect and analyze their own data, along with background information, downloadable sample data sets (to use when it is not practical to be out in the field), and detailed descriptions of useful open-source software tools. This pragmatic resource provides students with real-world experience practicing the complex techniques used by modern wildlife biologists. With more than 60 applied exercises to choose from in this unique manual, students will quickly acquire the scientific skills essential for a career working with mammals.