SAS Data Management for Public Health: An Introduction equips readers with the tools and knowledge they need to prepare public health data in SAS Data Management software for use in analysis. Highly accessible in nature, the book is specifically designed to help students who are new to SAS learn and master the system. The book is organized into 20 lessons. The opening lessons introduce SAS and provide tips and best practices for exploring data. Students are introduced to PROC MEANS, FREQ, UNIVARIATE, and PROC SGPLOT. They learn how to import data; merge, concatenate, and manage variables; perform data cleanup; and recode categorical and continuous variables. Specific lessons address comments, labels, and titles, formatting variables, conditional recoding, DO groups, arrays for recoding, and categorical data analysis. Closing lessons introduce stratified and subpopulation analysis, as well as logistic regression. The book includes an appendix to help students navigate and use SAS Studio. SAS Data Management for Public Health is an ideal resource for standalone courses in which SAS is taught or to complement any biostatistics or epidemiology course where students need to use SAS to analyze their data. Brianna Magnusson holds a Ph.D. in epidemiology and a M.P.H. from Virginia Commonwealth University. She is an associate professor in the Department of Public Health at Brigham Young University. Dr. Magnusson's research focuses on sexual and reproductive health with emphasis on factors influencing sexual decision-making. Caroline Stampfel holds an M.P.H. with a concentration in environmental epidemiology from the Yale School of Public Health. She serves as the director of programs for the Association of Maternal & Child Health Programs and leads a team of maternal and child health experts using data-driven, innovative approaches to improve the health and well-being of women, children, youth, families, and communities.

Learn to program SAS by example! Learning SAS by Example, A Programmer’s Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics. Because most programmers prefer examples rather than reference-type syntax, this book uses short examples to explain each topic. The second edition has brought this classic book on SAS programming up to the latest SAS version, with new chapters that cover topics such as PROC SGPLOT and Perl regular expressions. This book belongs on the shelf (or e-book reader) of anyone who programs in SAS, from those with little programming experience who want to learn SAS to intermediate and even advanced SAS programmers who want to learn new techniques or identify new ways to accomplish existing tasks. In an instructive and conversational tone, author Ron Cody clearly explains each programming technique and then illustrates it with one or more real-life examples, followed by a detailed description of how the program works. The text is divided into four major sections: Getting Started, DATA Step Processing, Presenting and Summarizing Your Data, and Advanced Topics. Subjects addressed include Reading data from external sources Learning details of DATA step programming Subsetting and combining SAS data sets Understanding SAS functions and working with arrays Creating reports with PROC REPORT and PROC TABULATE Getting started with the SAS macro language Leveraging PROC SQL Generating high-quality graphics Using advanced features of user-defined formats and informats Restructuring SAS data sets Working with multiple observations per subject Getting started with Perl regular expressions You can test your knowledge and hone your skills by solving the problems at the end of each chapter.

A holistic, step-by-step approach to analyzing health care data! Written for both beginner and intermediate JMP users working in or studying health care, Data Management and Analysis Using JMP: Health Care Case Studies bridges the gap between taking traditional statistics courses and successfully applying statistical analysis in the workplace. Authors Jane Oppenlander and Patricia Schaffer begin by illustrating techniques to prepare data for analysis, followed by presenting effective methods to summarize, visualize, and analyze data. The statistical analysis methods covered in the book are the foundational techniques commonly applied to meet regulatory, operational, budgeting, and research needs in the health care field. This example-driven book shows practitioners how to solve real-world problems by using an approach that includes problem definition, data management, selecting the appropriate analysis methods, step-by-step JMP instructions, and interpreting statistical results in context. Practical strategies for selecting appropriate statistical methods, remediating data anomalies, and interpreting statistical results in the domain context are emphasized. The cases presented in Data Management and Analysis Using JMP use multiple statistical methods. A progression of methods--from univariate to multivariate--is employed, illustrating a logical approach to problem-solving. Much of the data used in these cases is open source and drawn from a variety of health care settings. The book offers a welcome guide to working professionals as well as students studying statistics in health care-related fields.

Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes

The SAS® Certified Specialist Prep Guide: Base Programming Using SAS® 9.4 prepares you to take the new SAS 9.4 Base Programming -- Performance-Based Exam. This is the official guide by the SAS Global Certification Program. This prep guide is for both new and experienced SAS users, and it covers all the objectives that are tested on the exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers for the chapter quizzes and solutions for the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS® glossary and a list of practice data sets. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting both data syntax and programming logic errors. All exam topics are covered in these chapters: Setting Up Practice Data Basic Concepts Accessing Your Data Creating SAS Data Sets Identifying and Correcting SAS Language Errors Creating Reports Understanding DATA Step Processing BY-Group Processing Creating and Managing Variables Combining SAS Data Sets Processing Data with DO Loops SAS Formats and Informats SAS Date, Time, and Datetime Values Using Functions to Manipulate Data Producing Descriptive Statistics Creating Output Practice Programming Scenarios (Workbook)

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.