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Sharpening Your Advanced SAS Skills

Sharpening Your Advanced SAS® Skills presents sophisticated SAS programming techniques procedures and tools such as Proc SQL hash tables and SAS Macro programming for any industry. Drawing on his more than 20 years’ experience of SAS programming in the pharmaceutical industry the author provides a unique approach that empowers both advanced programmers who need a quick refresher and programmers interested in learning new techniques. The book helps you easily search for key points by summarizing and differentiating the syntax between similar SAS statements and options. Each chapter begins with an overview so you can quickly locate the detailed examples and syntax. The basic syntax expected data and descriptions are organized in summary tables to facilitate better memory recall. General rules list common points about similar statements or options. Real-world examples of SAS programs and code statements are line numbered with references such as SAS papers and websites for more detailed explanations. The text also includes end-of-chapter questions to reinforce your knowledge of the topics and prepare you for the advanced SAS certification exam. In addition the author’s website offers mindmaps and process flowcharts that connect concepts and relationships. | Sharpening Your Advanced SAS Skills

A Handbook of Statistical Analyses using SAS

Updated to reflect SAS 9. 2 A Handbook of Statistical Analyses using SAS Third Edition continues to provide a straightforward description of how to conduct various statistical analyses using SAS. Each chapter shows how to use SAS for a particular type of analysis. The authors cover inference analysis of variance regression generalized linear models longitudinal data survival analysis principal components analysis factor analysis cluster analysis discriminant function analysis and correspondence analysis. They demonstrate the analyses through real-world examples including methadone maintenance treatment the relation of cirrhosis deaths to alcohol consumption a sociological study of children heart transplant treatment and crime rate determinants. With the data sets and SAS code available online this book remains the go-to resource for learning how to use SAS for many kinds of statistical analysis. It serves as a stepping stone to the wider resources available to SAS users.

Producing High-Quality Figures Using SAS/GRAPH and ODS Graphics Procedures

Create Top-Quality Figures Using SAS Producing High-Quality Figures Using SAS/GRAPH and ODS Graphics Procedures gives statisticians and SAS programmers practical guidance on presenting research data in high-quality figures that meet the publication requirements of academic institutions and various industries such as pharmaceutical companies agricultural businesses and financial organizations. Choose the Proper Graph Formats Options and Fonts The book provides all the details on selecting the right figure formats options and fonts to produce high-quality figures. In-depth instructions and SAS programs using procedures in both SAS/GRAPH and ODS Graphics show how to generate sample figures in listing graphics formats and ODS document files. Each chapter includes practical examples and SAS programs. Readers can easily modify the SAS programs to develop high-quality figures to meet their own needs. For those unfamiliar with SAS programming and SAS Graphics the author describes how to produce sample figures in SAS ODS Graphics designer a convenient tool for generating figures without having to write SAS programs. The book also discusses how to produce figures in a SAS Enterprise Guide project. | Producing High-Quality Figures Using SAS/GRAPH® and ODS Graphics Procedures

SAS Programming The One-Day Course

Aimed at researchers and students SAS Programming: The One-Day Course provides an introduction to the SAS programming language. It gives the reader a start in SAS programming and the basic data manipulations and statistical summaries that are available through SAS. The book has its origins in material prepared by the author for a one-day course in SAS programming and the fact that it has been developed from a training course is reflected in the concise nature of the presentation. Unlike other introductory competitors on the market this is a pocket-sized reference that does not clutter the programming techniques presented by trying to teach statistical techniques at the same time. Strong on explanations of how to carry out data manipulations that real-life data often call for each programming technique is supported by tasks to develop skills and confidence. It also contains tasks for the reader complete with solutions. Datasets and the programming code are available to download from www. crcpress. com/e_products/downloads. Once readers have mastered the topics covered in the book they will be well placed to learn further aspects of SAS programming. | SAS Programming The One-Day Course

Applied Operational Research with SAS

Using a wide range of operational research (OR) optimization examples Applied Operational Research with SAS demonstrates how the OR procedures in SAS work. The book is one of the first to extensively cover the application of SAS procedures to OR problems such as single criterion optimization project management decisions printed circuit board assembly and multiple criteria decision making. The text begins with the algorithms and methods for linear programming integer linear programming and goal programming models. It then describes the principles of several OR procedures in SAS. Subsequent chapters explain how to use these procedures to solve various types of OR problems. Each of these chapters describes the concept of an OR problem presents an example of the problem and discusses the specific procedure and its macros for the optimal solution of the problem. The macros include data handling model building and report writing. While primarily designed for SAS users in OR and marketing analytics the book can also be used by readers interested in mathematical modeling techniques. By formulating the OR problems as mathematical models the authors show how SAS can solve a variety of optimization problems.

Using SAS for Data Management Statistical Analysis and Graphics

Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications tasks and graphics A unique companion for statistical coders Using SAS for Data Management Statistical Analysis and Graphics presents an easy way to learn how to perform an analytical task in SAS without having to navigate through the extensive idiosyncratic and sometimes unwieldy software documentation. Organized by short clear descriptive entries the book covers many common tasks such as data management descriptive summaries inferential procedures regression analysis multivariate methods and the creation of graphics. Through the extensive indexing cross-referencing and worked examples in this text users can directly find and implement the material they need. The text includes convenient indices organized by topic and SAS syntax. Demonstrating the SAS code in action and facilitating exploration the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book‘s website. Helping to improve your analytical skills this book lucidly summarizes the features of SAS most often used by statistical analysts. New users of SAS will find the simple approach easy to understand while more expert SAS programmers will appreciate the invaluable source of task-oriented information.

Regression Modeling Methods Theory and Computation with SAS

Regression Modeling: Methods Theory and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression logistic regression (including Poisson regression) Bayesian regression robust regression fuzzy regression random coefficients regression L1 and q-quantile regression regression in a spatial domain ridge regression semiparametric regression nonlinear least squares and time-series regression issues. For most of the regression methods the author includes SAS procedure code enabling readers to promptly perform their own regression runs. A Comprehensive Accessible Source on Regression Methodology and ModelingRequiring only basic knowledge of statistics and calculus this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations. | Regression Modeling Methods Theory and Computation with SAS

Clinical Trial Data Analysis Using R and SAS

Review of the First EditionThe goal of this book as stated by the authors is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall this book achieves the goal successfully and does a nice job. I would highly recommend it …The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods. —Journal of Statistical SoftwareClinical Trial Data Analysis Using R and SAS Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second EditionAdds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension duodenal ulcer beta blockers familial andenomatous polyposis and breast cancer trials. Covers the biostatistical aspects of various clinical trials including treatment comparisons time-to-event endpoints longitudinal clinical trials and bioequivalence trials.

Clinical Data Quality Checks for CDISC Compliance Using SAS

Clinical Data Quality Checks for CDISC Compliance using SAS is the first book focused on identifying and correcting data quality and CDISC compliance issues with real-world innovative SAS programming techniques such as Proc SQL metadata and macro programming. Learn to master Proc SQL’s subqueries and summary functions for multi-tasking process. Drawing on his more than 25 years’ experience in the pharmaceutical industry the author provides a unique approach that empowers SAS programmers to take control of data quality and CDISC compliance. This book helps you create a system of SDTM and ADaM checks that can be tracked for continuous improvement. How often have you encountered issues such as missing required variables duplicate records invalid derived variables and invalid sequence of two dates? With the SAS programming techniques introduced in this book you can start to monitor these and more complex data and CDISC compliance issues. With increased standardization in SDTM and ADaM specifications and data values codelist dictionaries can be created for better organization planning and maintenance. This book includes a SAS program to create excel files containing unique values from all SDTM and ADaM variables as columns. In addition another SAS program compares SDTM and ADaM codelist dictionaries with codelists from define. xml specifications. Having tools to automate this process greatly saves time from doing it manually. Features SDTMs and ADaMs Vitals SDTMs and ADaMs Data CDISC Specifications Compliance CDISC Data Compliance Protocol Compliance Codelist Dictionary Compliance

SAS Software Companion for Sampling Design and Analysis Third Edition

The SAS® Software Companion for Sampling: Design and Analysis designed to be read alongside Sampling: Design and Analysis Third Edition by Sharon L. Lohr (SDA; 2022 CRC Press) shows how to use the survey selection and analysis procedures of SAS® software to perform calculations for the examples in SDA. No prior experience with SAS software is needed. Chapter 1 tells you how to access the software introduces basic features and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA with code output and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. Features of the SAS software procedures are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book you will know how to use SAS software to select and analyze almost any type of probability sample. All code is available on the book website and is easily adapted for your own survey data analyses. The website also contains all data sets from the examples and exercises in SDA to help you develop your skills through analyzing survey data from social and public opinion research public health crime education business agriculture and ecology | SAS® Software Companion for Sampling Design and Analysis Third Edition

Analyzing Health Data in R for SAS Users

Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software but are new to R. For professors it is useful as a textbook for a descriptive or regression modeling class as it uses a publicly-available dataset for examples and provides exercises at the end of each chapter. For students and public health professionals not only is it a gentle introduction to R but it can serve as a guide to developing the results for a research report using R software. Features:Gives examples in both SAS and RDemonstrates descriptive statistics as well as linear and logistic regressionProvides exercise questions and answers at the end of each chapterUses examples from the publicly available dataset Behavioral Risk Factor Surveillance System (BRFSS) 2014 dataGuides the reader on producing a health analysis that could be published as a research reportGives an example of hypothesis-driven data analysisProvides examples of plots with a color insert

Applied Surrogate Endpoint Evaluation Methods with SAS and R

An important factor that affects the duration complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice using SAS and R. | Applied Surrogate Endpoint Evaluation Methods with SAS and R

Advanced Regression Models with SAS and R

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression including models for right-skewed categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors. Features: Presents the theoretical framework for each regression. Discusses data that are categorical count proportions right-skewed longitudinal and hierarchical. Uses examples based on real-life consulting projects. Provides complete SAS and R codes for each example. Includes several exercises for every regression. Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required. The Author:Olga Korosteleva is a Professor of Statistics at California State University Long Beach. She teaches a large variety of statistical courses to undergraduate and master’s students. She has published three statistical textbooks. For a number of years she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences nursing kinesiology and other fields.

Structural Equation Modeling Using R/SAS A Step-by-Step Approach with Real Data Analysis

There has been considerable attention to making the methodologies of structural equation modeling available to researchers practitioners and students along with commonly used software. Structural Equation Modelling Using R/SAS aims to bring it all together to provide a concise point-of-reference for the most commonly used structural equation modeling from the fundamental level to the advanced level. This book is intended to contribute to the rapid development in structural equation modeling and its applications to real-world data. Straightforward explanations of the statistical theory and models related to structural equation models are provided using a compilation of a variety of publicly available data to provide an illustration of data analytics in a step-by-step fashion using commonly used statistical software of R and SAS. This book is appropriate for anyone who is interested in learning and practicing structural equation modeling especially in using R and SAS. It is useful for applied statisticians data scientists and practitioners applied statistical analysts and scientists in public health and academic researchers and graduate students in statistics whilst also being of use to R&D professionals/practitioners in industry and governmental agencies. Key Features: Extensive compilation of commonly used structural equation models and methods from fundamental to advanced levels Straightforward explanations of the theory related to the structural equation models Compilation of a variety of publicly available data Step-by-step illustrations of data analysis using commonly used statistical software R and SAS Data and computer programs are available for readers to replicate and implement the new methods to better understand the book contents and for future applications Handbook for applied statisticians and practitioners | Structural Equation Modeling Using R/SAS A Step-by-Step Approach with Real Data Analysis

Design and Analysis of Experiments Classical and Regression Approaches with SAS

Unlike other books on the modeling and analysis of experimental data Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory it also explores regression approaches. Capitalizing on the availability of cutting-edge software the author uses both manual methods and SAS programs to carry out analyses. The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation the completely randomized design the use of orthogonal contrast to test hypotheses and the model adequacy check. With an emphasis on two-factor factorial experiments the author analyzes repeated measures as well as fixed random and mixed effects models. He also describes designs with randomization restrictions before delving into the special cases of the 2k and 3k factorial designs including fractional replication and confounding. In addition the book covers response surfaces balanced incomplete block and hierarchical designs ANOVA ANCOVA and MANOVA. Fortifying the theory and computations with practical exercises and supplemental material this distinctive text provides a modern comprehensive treatment of experimental design and analysis. | Design and Analysis of Experiments Classical and Regression Approaches with SAS

Statistical Analytics for Health Data Science with SAS and R

This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models compilations of a variety of publicly available data and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science applied statistical analysts and scientists in public health academic researchers and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research. | Statistical Analytics for Health Data Science with SAS and R

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Reviewing the theory of the general linear model (GLM) using a general framework Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition presents analyses of simple and complex models both univariate and multivariate that employ data sets from a variety of disciplines such as the social and behavioral sciences. With revised examples that include options available using SAS 9. 0 this expanded edition divides theory from applications within each chapter. Following an overview of the GLM the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis and multivariate GLMs that cover multivariate regression analysis MANOVA MANCOVA and repeated measurement data analyses. The book also analyzes double multivariate linear growth curve seeming unrelated regression (SUR) restricted GMANOVA and hierarchical linear models. New to the Second EditionTwo chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedureExpanded theory of unrestricted general linear multivariate general linear SUR and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithmApplications of MI MIANALYZE TRANSREG and CALIS proceduresA practical introduction to GLMs Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework. | Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Statistical Methods for Mediation Confounding and Moderation Analysis Using R and SAS

Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers. Statistical Methods for Mediation Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous) exposure or third-variables. Using this method multiple third- variables of different types can be considered simultaneously and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis. Key Features: Parametric and nonparametric method in third variable analysis Multivariate and Multiple third-variable effect analysis Multilevel mediation/confounding analysis Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis R packages and SAS macros to implement methods proposed in the book

Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R SAS and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features:-Provides an overview of frequentist as well as Bayesian methods. Include a focus on practical aspects and applications. Extensively illustrates the methods with examples using R SAS and BUGS. Full programs are available on a supplementary website. The authors:Kris Bogaerts is project manager at I-BioStat KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society and editor of Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat KU Leuven. His research interests include Bayesian methods longitudinal data analysis statistical modelling analysis of dental data interval-censored data misclassification issues and clinical trials. He is the founding chair of the Statistical Modelling Society past-president of the International Society for Clinical Biostatistics and fellow of ISI and ASA. | Survival Analysis with Interval-Censored Data A Practical Approach with Examples in R SAS and BUGS

Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement An Applied Approach Using SAS & STATA

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems. Although many books exist that address the theoretical or practical aspects of cost-effectiveness analysis this book differentiates itself from the competition by detailing how to apply health economic evaluation techniques in a clinical trial context from both academic and pharmaceutical/commercial perspectives. It also includes a special chapter for clinical trials in Cancer. Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement is not just about performing cost-effectiveness analyses. It also emphasizes the strategic importance of economic evaluation and offers guidance and advice on the complex factors at play before during and after an economic evaluation. Filled with detailed examples the book bridges the gap between applications of economic evaluation in industry (mainly pharmaceutical) and what students may learn in university courses. It provides readers with access to SAS and STATA code. In addition Windows-based software for sample size and value of information analysis is available free of charge—making it a valuable resource for students considering a career in this field or for those who simply wish to know more about applying economic evaluation techniques. The book includes coverage of trial design case report form design quality of life measures sample sizes submissions to regulatory authorities for reimbursement Markov models cohort models and decision trees. Examples and case studies are provided at the end of each chapter. Presenting first-hand insights into how economic evaluations are performed from a drug development perspective the book supplies readers with the foundation required to succeed in an environment where clinical trials and cost-effectiveness of new treatments are central. It also includes thought-provoking exercises for use in classroom and seminar discussions. | Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement An Applied Approach Using SAS & STATA

Introduction to Mediation Moderation and Conditional Process Analysis A Regression-Based Approach

Acclaimed for its thorough presentation of mediation moderation and conditional process analysis this book has been updated to reflect the latest developments in PROCESS for SPSS SAS and new to this edition R. Using the principles of ordinary least squares regression Andrew F. Hayes illustrates each step in an analysis using diverse examples from published studies and displays SPSS SAS and R code for each example. Procedures are outlined for estimating and interpreting direct indirect and conditional effects; probing and visualizing interactions; testing hypotheses about the moderation of mechanisms; and reporting different types of analyses. Readers gain an understanding of the link between statistics and causality as well as what the data are telling them. The companion website (www. afhayes. com) provides data for all the examples plus the free PROCESS download. New to This Edition *Rewritten Appendix A which provides the only documentation of PROCESS including a discussion of the syntax structure of PROCESS for R compared to SPSS and SAS. *Expanded discussion of effect scaling and the difference between unstandardized completely standardized and partially standardized effects. *Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model using a new PROCESS option. *Discussion of a method for comparing the strength of two specific indirect effects that are different in sign. *Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model. *Discussion of testing for interaction between a causal antecedent variable [ital]X[/ital] and a mediator [ital]M[/ital] in a mediation analysis and how to test this assumption in a new PROCESS feature. | Introduction to Mediation Moderation and Conditional Process Analysis A Regression-Based Approach

A Researcher's Guide to Using Electronic Health Records From Planning to Presentation

In an age when electronic health records (EHRs) are an increasingly important source of data this essential textbook provides both practical and theoretical guidance to researchers conducting epidemiological or clinical analysis through EHRs. Split into three parts the book covers the research journey from start to finish. Part 1 focuses on the challenges inherent when working with EHRs from access to data management and raising issues such as completeness and accuracy which impact the validity of any research project. Part 2 examines the core research process itself with chapters on research design sampling and analysis as well as emerging methodological techniques. Part 3 demonstrates how EHR research can be made meaningful from presentation to publication and includes how findings can be applied to real-world issues of public health. Supported by case studies throughout and applicable across a range of research software programs (including R SPSS and SAS) this is the ideal text for students and researchers engaging with EHRs across epidemiological and clinical research. | A Researcher's Guide to Using Electronic Health Records From Planning to Presentation

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

Statistical Programming in SAS

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields including medical and health outcomes research business analytics and data science ecology fisheries astronomy transportation insurance economics recreation and sports. By harnessing the capabilities of the logistic model analysts can better understand their data make appropriate predictions and classifications and determine the odds of one value of a predictor compared to another. Drawing on his many years of teaching logistic regression using logistic-based models in research and writing about the subject Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers the book explains how to construct a logistic model interpret coefficients and odds ratios predict probabilities and their standard errors based on the model and evaluate the model as to its fit. Using a variety of real data examples mostly from health outcomes the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression. R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers own analyses. All the code is available on the author‘s website.