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SAS Books
SAS Essentials by
A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples. Keeping different experience levels in mind, the highly-qualified author team has developed the book over 20 years of teaching introductory SAS courses. Divided into two sections, the first part of the book provides an introduction to data manipulation, statistical techniques, and the SAS programming language. The second section is designed to introduce users to statistical analysis using SAS Procedures. Featuring self-contained chapters to enhance the learning process, the Second Edition also includes: Programming approaches for the most up-to-date version of the SAS platform including information on how to use the SAS University Edition Discussions to illustrate the concepts and highlight key fundamental computational skills that are utilized by business, government, and organizations alike New chapters on reporting results in tables and factor analysis Additional information on the DATA step for data management with an emphasis on importing data from other sources, combining data sets, and data cleaning Updated ANOVA and regression examples as well as other data analysis techniques A companion website with the discussed data sets, additional code, and related PowerPoint® slides SAS Essentials: Mastering SAS for Data Analytics, Second Edition is an ideal textbook for upper-undergraduate and graduate-level courses in statistics, data analytics, applied SAS programming, and statistical computer applications as well as an excellent supplement for statistical methodology courses. The book is an appropriate reference for researchers and academicians who require a basic introduction to SAS for statistical analysis and for preparation for the Basic SAS Certification Exam.
ISBN: 9781119042181Publication Date: 2015-08-18
Python for SAS Users by
Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows. What You'll Learn Quickly master Python for data analysis without using a trial-and-error approach Understand the similarities and differences between Base SAS and Python Better determine which language to use, depending on your needs Obtain quick results Who This Book Is For SAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS
ISBN: 9781484250006Publication Date: 2019-09-06
SAS Programming for Elementary Statistics by
SAS for Elementary Statistics: Getting Started provides an introduction to SAS programming for those who have experience with introductory statistical methods. It is also an excellent programming supplement for an introductory statistics course. It is appropriate for the beginning programmer with no prior SAS experience and the researcher who would like to refresh SAS programming skills. These lessons are those the author has found successful in the classroom. Strengths of this book include the following: Examples are easy to follow and understand. Chapters have user-friendly text and objectives. Each chapter has clear objectives with SAS syntax and output results given. Objectives are stated as tasks with detailed step-by-step instructions. Programming notes based on the author's experience occur throughout the book. The author assists the reader in making sense of the error messages in the SAS log. Brief reviews of statistical methods are included in chapters accompanying the corresponding SAS procedures. Easy transition from user terminology to SAS terminology is provided. The ability to select or suppress results using Output Delivery System (ODS) is made simple. Reading and writing to external files are among the most used SAS skills, and these concepts are clearly presented. The IMPORT and EXPORT procedures and ODS are used to accomplish these tasks. Statistical Graphics procedures and SAS/GRAPH can be quite challenging to learn, but these are presented in a very achievable format. Basic graph construction is first introduced then readers learn how to add color, pattern, and other enhancements to graphics images.
ISBN: 9780429959301Publication Date: 2020-12-07
Learn Data Science Using SAS Studio: A Quick-Start Guide by
Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks.The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS Studio.You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics.What You Will LearnBecome familiar with SAS Studio IDEUnderstand essential visualizationsKnow the fundamental statistical analysis required in most data science and analytics reportsClean the most common data set problemsUse linear progression for data predictionWrite programs in SASGet introduced to SAS-Viya, which is more potent than SAS studioWho This Book Is ForA general audience of people who are new to data science, students, and data analysts and scientists who are experienced but new to SAS. No programming or in-depth statistics knowledge is needed.
ISBN: 9781484262375
Statistical Data Analysis Using SAS Intermediate Statistical Methods by
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
Contents
1 Introduction to the SAS Language -- 2 More on SAS Programming and Some Applications -- 3 Introduction to SAS Graphics -- 4 Statistical Analysis of Regression Models -- 5 Analysis of Variance Models -- 6 Analysis of Variance: Random and Mixed Effects Models -- 7 Beyond Regression and Analysis of Variance -- Appendices -- References.
ISBN: 3319692399
Time Series Analysis Using SAS Enterprise Guide by
This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.
ISBN: 9811503206Publication Date: 2020-02-20
Public Health e-books
Beginning R 4 by
Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling. Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. You will: Acquire and install R and RStudio Import and export data from multiple file formats Analyze data and generate graphics (including confidence intervals) Interactively conduct hypothesis testing Code multiple and moderated regression solutions Who This Book Is For Programmers and data analysts who are new to R. Some prior experience in programming is recommended.
ISBN: 9781484260524Publication Date: 2020-10-18
Health Statistics by
Health statistics have been an essential tool for improving the health of populations for centuries, yet no single book covers the key elements in developing, using, and improving them. This volume fills that crucial gap by providing a comprehensive account of the essential concepts andcomplex underpinnings of health statistics. It gives a broad and detailed view of the sources and uses of health statistics and explores contemporary issues confronting the health statistics enterprise, including privacy, technology, and the emergence of health data standards. It also proposesfundamental changes needed to improve health statistics that can be embraced by practitioners at all levels of government and the private sector. The book is guided throughout by a comprehensive model of population health that expands the traditionally held view of what factors influence health. Thechapters are grouped into five sections: 1) defining health statistics-context, history, and organization; 2) collecting and compiling health statistics; 3) putting health statistics to use; 4) identifying current and forthcoming issues and 5) transforming health statistics through new conceptualframeworks. This logical organization helps make the book suitable for graduate courses in public health and public health surveillance, health services research, population health statistics, or population health information systems. It will be equally useful for the staff of the manyorganizations that comprise the health statistics enterprise, for health professionals seeking a broader context for their efforts, and for researchers aiming to advance the field of health statistics and their application to health policy or public health practice.
ISBN: 9780195149289Publication Date: 2005-06-02
Quantitative Data Analysis Using Spss by
Useful for students in health and social sciences with little or no experience of quantitative data analysis and statistics, this book take you through the step by step process of data analysis. It includes topics such as questionnaires and how to analyse them, and coding the data for SPSS, setting up an SPSS database and entering the data.
ISBN: 9780335235148Publication Date: 2007-12-01
SAS programming and data visualization techniques : a power user's guideISBN: 9781484205686
SPSS for Starters Part 2 by
The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests. The current part 2 of this title reviews multistep methods, multivariate models, assessments of missing data, performance of diagnostic tests, meta-regression, Poisson regression, confounding and interaction, and survival analyses using log tests and segmented time-dependent Cox regression. Methods for assessing non linear models, data seasonality, distribution free methods, including Monte Carlo methods and artificial intelligence, and robust tests are also covered. Each method of testing is explained using a data example from clinical practice,including every step in SPSS, and a text with interpretations of the results and hints convenient for data reporting. In order to facilitate the use of this cookbook the data files of the examples is made available by the editor through extras.springer.com. Both part 1 and 2 of this title contain a minima amount of text and maximal technical details, but we believe that this property will not refrain students from mastering the SPSS software systematics, and that, instead, it will be a help to that aim. Yet, we recommend that it will used together with the textbook "Statistics Applied to Clinical Trials" (5th edition, Springer, Dordrecht 2012) and the e-books "Statistics on a Pocket Calculator Part 1 and 2 (Springer, Dordrecht, 2011 and 2012) from the same authors.
ISBN: 9789400748040Publication Date: 2012-07-09
SPSS Statistics for Data Analysis and Visualization by
Dive deeper into SPSS Statistics for more efficient, accurate,and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goesbeyond the basics of SPSS Statistics to show you advancedtechniques that exploit the full capabilities of SPSS. The authorsexplain when and why to use each technique, and then walk youthrough the execution with a pragmatic, nuts and bolts example.Coverage includes extensive, in-depth discussion of advancedstatistical techniques, data visualization, predictive analytics,and SPSS programming, including automation and integration withother languages like R and Python. You'll learn the best methods topower through an analysis, with more efficient, elegant, andaccurate code. IBM SPSS Statistics is complex: true mastery requires a deepunderstanding of statistical theory, the user interface, andprogramming. Most users don't encounter all of the methods SPSSoffers, leaving many little-known modules undiscovered. This bookwalks you through tools you may have never noticed, and shows youhow they can be used to streamline your workflow and enable you toproduce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create bettervisualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient,more powerful code These "hidden tools" can help you produce charts that simplywouldn't be possible any other way, and the support for otherprogramming languages gives you better options for solving complexproblems. If you're ready to take advantage of everything thispowerful software package has to offer, SPSS Statistics for DataAnalysis and Visualization is the expert-led training youneed.
ISBN: 9781119003557Publication Date: 2017-05-01
Statistical Regression Modeling with R: Longitudinal and Multi-Level ModelingISBN: 9783030675837Publication Date: 2021- Statistical Data Analysis Using SAS Intermediate Statistical Methods by
The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
Contents
1 Introduction to the SAS Language -- 2 More on SAS Programming and Some Applications -- 3 Introduction to SAS Graphics -- 4 Statistical Analysis of Regression Models -- 5 Analysis of Variance Models -- 6 Analysis of Variance: Random and Mixed Effects Models -- 7 Beyond Regression and Analysis of Variance -- Appendices -- References.
ISBN: 3319692399
SPSS for Applied Sciences by ISBN: 9780643107113Publication Date: 2013-01-01Clearly explains how to use SPSS statistical software, including tests and worked examples.
Learn Data Analysis with Python by
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren't using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. What You Will Learn Get data into and out of Python code Prepare the data and its format Find the meaning of the data Visualize the data using iPython Who This Book Is For Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
ISBN: 9781484234853Publication Date: 2018-02-23- Statistical Data Analysis Using SAS Intermediate Statistical Methods by ISBN: 3319692399Publication Date: 2018
Categorical Data Analysis for the Behavioral and Social Sciences by
Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a "Look Ahead" section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book's accessible approach.
ISBN: 0429330308Publication Date: 2021-05-26
ISBN: 1493976931
Categorical Data Analysis by
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." --Statistics in Medicine "It is a total delight reading this book." --Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." --Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
ISBN: 9780470463635Publication Date: 2012-12-03
The Practice of Qualitative Data Analysis by Call Number: 10.36192/978-3-948768058.ISBN: 9783948768058Publication Date: 2021This book provides ten case studies with concise real-world examples illustrating how MAXQDA is used in practice.
Qualitative Research in Nursing and Healthcare by
Qualitative Research in Nursing and Healthcare is an invaluable resource for those who carry out qualitative research in the healthcare arena. It is intended to assist: Professionals and academics in the healthcare field who undertake or teach research in clinical or educational settings; Postgraduates who are undertaking qualitative research and want to revise qualitative research approaches and procedures before going on to more specialist texts; and Undergraduates in their last year who wish to learn about qualitative perspectives or carry out a project using these approaches. Fully updated from the earlier editions by Holloway and Wheeler, it reflects recent developments in nursing research. This new edition provides clear explanations of abstract ideas in qualitative research as well as practical procedures. Structured into four sections, the book looks at the initial stages, methods of data collection, qualitative approaches and analysis of collected data. It also contains a chapter on writing up and publishing qualitative research. With applied and practical examples throughout, Qualitative Research in Nursing and Healthcare is essential reading for those who are looking for a comprehensive introduction to qualitative research.
ISBN: 1118874471Publication Date: 2016-08-01
Understanding Qualitative Research and Ethnomethodology by ISBN: 9781412933612Publication Date: 2004-01-01
Declarative Mapping Sentences in Qualitative Research by
In this book, Hackett introduces the traditional usage of the mapping sentence within quantitative research, reviews its philosophical underpinnings, and proposes the "declarative mapping sentence" as an instrument and approach to qualitative scholarship. With a helpful glossary and a range of illustrative tables, Hackett takes the reader through a straightforward introduction to mapping sentences and their construction, before discussing declarative mapping sentences and possible future research directions. This innovative direction for social research provides a flexible structure for research domain, and it allows qualitative research results to be uniformly sorted. Declarative Mapping Sentences in Qualitative Research will be essential reading for researchers, academics, and postgraduate students in the fields of qualitative psychology and psychological methods, as well as philosophical psychology and social science research methods.
ISBN: 1351013998Publication Date: 2020-10-01
Qualitative Metasynthesis by
Qualitative Metasynthesis presents a research method developed for upcycling and synthesis of qualitative primary studies, aimed at researchers within medicine and health sciences. This book demonstrates how and why qualitative metasynthesis can be a method for reuse and expansion of medical knowledge. It presents the principles of metasynthesis as a qualitative research method, so that the reader can assess whether this is a research strategy that fits the aim of their study. The author offers practical advice for conducting research using this methodology. The presentation is illustrated by a study carried out by the author and collaborators,　reflecting on real-life challenges and solutions as an example of meta-ethnography, one of the most frequently used strategies for qualitative metasynthesis. The author also looks at systematic reviews, a methodology developed within in the tradition of evidence-based medicine, discussing strengths, weaknesses and pitfalls of this methodology. Rooted in the interpretative paradigm, qualitative metasynthesis challenges several of the principles from the evidence-based medicine tradition, offering reflections on challenges when epistemologically very different methodologies intersect. This book should be considered essential reading for anyone carrying out qualitative research within the fields of medicine, health and social care.
ISBN: 0429649193Publication Date: 2019-06-05
Displaying Your Findings by Call Number: Available at College of Charleston in PrintISBN: 9781557989789Publication Date: 2003-01-01
Regression Methods in Biostatistics by
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided.
ISBN: 9781461413523Publication Date: 2011-09-01
Introduction to Linear Regression Analysis by
Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." --Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
ISBN: 1118640764Publication Date: 2012-04-09- Categorical Data Analysis by
Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." --Statistics in Medicine "It is a total delight reading this book." --Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." --Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
ISBN: 9780470463635Publication Date: 2012-12-03
Principles of Exposure Measurement in Epidemiology by
The second edition of this internationally acclaimed title is the ideal handbook for those wishing to design or implement an epidemiological study. It provides an overview of numerous different techniques of exposure measurement of individuals, along with many specific details for each technique. It includes clear and comprehensive guidance on minimizing measurement error.The objective of much epidemiological research is to relate exposure toexternal agents to the occurrence of particular diseases. The achievement of this objective depends critically on accurate measurement of exposure. This book brings together the principles andtechniques of exposure measurement as they relate specifically to causes of non-infectious diseases. In doing so, it draws on experience and results from a number of disciplines, including psychology, survey research, sociology, environmental hygiene, epidemiology and biostatistics, and synthesizes them for direct practical application in epidemiological and related research.
ISBN: 9780198509851Publication Date: 2008-05-07
Environmental Health by
The bestselling environmental health text, with all new coverage of key topics Environmental Health: From Global to Local is a comprehensive introduction to the subject, and a contemporary, authoritative text for students of public health, environmental health, preventive medicine, community health, and environmental studies. Edited by the former director of the CDC's National Center for Environmental Health and current dean of the School of Public Health at the University of Washington, this book provides a multi-faceted view of the topic, and how it affects different regions, populations, and professions. In addition to traditional environmental health topics--air, water, chemical toxins, radiation, pest control--it offers remarkably broad, cross-cutting coverage, including such topics as building design, urban and regional planning, energy, transportation, disaster preparedness and response, climate change, and environmental psychology. This new third edition maintains its strong grounding in evidence, and has been revised for greater readability, with new coverage of ecology, sustainability, and vulnerable populations, with integrated coverage of policy issues, and with a more global focus. Environmental health is a critically important topic, and it reaches into fields as diverse as communications, technology, regulatory policy, medicine, and law. This book is a well-rounded guide that addresses the field's most pressing concerns, with a practical bent that takes the material beyond theory. Explore the cross-discipline manifestations of environmental health Understand the global ramifications of population and climate change Learn how environmental issues affect health and well-being closer to home Discover how different fields incorporate environmental health perspectives The first law of ecology reminds is that 'everything is connected to everything else.' Each piece of the system affects the whole, and the whole must sustain us all for the long term. Environmental Health lays out the facts, makes the connections, and demonstrates the importance of these crucial issues to human health and well-being, both on a global scale, and in our homes, workplaces, and neighborhoods.
ISBN: 9781118984765Publication Date: 2016-02-29
Molecular Epidemiology by
Molecular epidemiology has taken advantage of the emergence of technological advances collectively identified as "-omics" (genomics, transcriptomics, proteomic, metabonomics) and their usage in epidemiological studies has heralded a revolution in the design implementation, and interpretation of studies on disease causation. Not the least, molecular epidemiology has brought together scientists of all disciplines to interact into very large, often multi-national networks, fostering consortia that have the size and power to address diseases as a global challenge. This book captures these fascinating developments and provides an extended, forward-looking vision of the principles, practice and impact of molecular epidemiology. Written and coordinated by world leaders in the field, the book covers, in a systematic way, the major conceptual advances, with a strong emphasis on study design and on how to incorporate biomarker studies into epidemiology practice. While providing a cornerstone for specialists, the book is also a teaching and training manual for public health, biology and medical students at the undergraduate, graduate, and post-graduate levels. With its strong insistence on interdisciplinarity and its focus on translating complex concepts into information that can be shared across the borders of scientific disciplines, the book will be a door-opener for researchers whose work is being attracted by the potent magnet of molecular epidemiology.
ISBN: 9789283221630Publication Date: 2012-08-28
Formulation and Treatment in Clinical Health Psychology by
How do clinicians select appropriate treatment strategies to match their clients' needs? Formulation and Treatment in Clinical Health Psychology brings together leading experts in the fields of clinical health psychology and behavioural medicine with the aim of illustrating the formulation and treatment design procedures which they employ in their specialist areas. Each chapter covers the key biopsychosocial parameters, assessment modalities, empirically based treatment strategies and formulation procedures for specific problems. Areas covered include: cognitive-behavioural case formulation in the treatment of alcohol problems psychological treatment of hypertension cognitive therapy for irritable bowel syndrome miscarriage: conceptualisation and treatment of the psychological sequelae. Case studies are employed throughout to demonstrate a link between case formulation, treatment planning and outcome. The practical guidance provided in this volume will prove invaluable for all practising clinicians working in the context of health-related problems.
ISBN: 9780203929643
Jekel's Epidemiology, Biostatistics and Preventive Medicine
Succinct yet thorough, Epidemiology, Biostatistics, and Preventive Medicine, 3rd Edition brings you today's best knowledge on epidemiology, biostatistics, preventive medicine, and public health in one convenient source. You'll find the latest on healthcare policy and financing . infectious diseases . chronic disease . and disease prevention technology. This text also serves as an outstanding resource for preparing for the USMLE, and the American Board of Preventive Medicine recommends it as a top review source for its core specialty examination.Discusses the financial concerns and the use and limitations of screening in the prevention of symptomatic disease.Emphasizes the application of epidemiologic and biostatistical concepts to everyday clinical problem solving and decision making.Showcases important concepts and calculations inside quick-reference boxes.Presents abundant illustrations and well-organized tables to clarify and summarize complex concepts.Includes 350 USMLE-style questions and answers, complete with detailed explanations about why various choices are correct or incorrect.This book comes with STUDENT CONSULT at no extra charge Register at www.studentconsult.com today...so you can learn and study more powerfully than ever before Access the complete contents of the book online, anywhere you go...perform quick searches...and add your own notes and bookmarks.Follow Integration Links to related bonus content from other STUDENT CONSULT titles to help you see the connections between diverse disciplines.Reference all other STUDENT CONSULT titles you own online, too all in one place Look for the STUDENT CONSULT logo on your favorite Elsevier textbooks Includes the latest information on Bovine Spongiform Encephalopathy (BSE) . SARS . avian form of H5N1 influenza . the obesity epidemic . and more."
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