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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

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.

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.

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.

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

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

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

Analysis of Quantal Response Data

This book takes the standard methods as the starting point and then describes a wide range of relatively new approaches and procedures designed to deal with more complicated data and experiments - including much recent research in the area. Throughout mention is given to the computing requirements - facilities available in large computing packages like BMDP SAS and SPSS are also described. | Analysis of Quantal Response Data

The Theory and Practice of Item Response Theory

Noted for addressing both the "hows" and "whys" of item response theory (IRT) this text has been revised and updated with the latest techniques (multilevel models mixed models and more) and software packages. Simple to more complex models are covered in consistently formatted chapters that build sequentially. The book takes the reader from model development through the fit analysis and interpretation phases that would be performed in practice. To facilitate understanding common data sets are used across chapters with the examples worked through for increasingly complex models. Exemplary model applications include free (BIGSTEPS NOHARM Facets R packages) and commercial (BILOG-MG flexMIRT SAS WINMIRA SPSS SYSTAT) software packages. The companion website provides data files and online-only appendices. New to This Edition *Chapter on multilevel models. *New material on loglinear models mixed models the linear logistic trait model and fit statistics. *Many additional worked-through examples. *Updated guidance on software; now includes R SAS and flexMIRT. | The Theory and Practice of Item Response Theory

Biostatistics for Clinical and Public Health Research

Biostatistics for Clinical and Public Health Research provides a concise overview of statistical analysis methods. Use of SAS and Stata statistical software is illustrated in full including how to interpret results. Focusing on statistical models without all the theory the book is complete with exercises case studies take-away points and data sets. Readers will be able to maximize their statistical abilities in hypothesis testing data interpretation and application while also learning when and how to consult a biostatistician. This book will be an invaluable tool for students and clinical and public health practitioners.

Data Analysis A Model Comparison Approach To Regression ANOVA and Beyond Third Edition

Data Analysis: A Model Comparison Approach to Regression ANOVA and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses such as multiple regression and analysis of variance but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i. e. interactions and nonlinear effects) and to observations that are nonindependent. Indeed the analysis of nonindependent observations is treated in some detail including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets R code and SAS output for all analyses; additional examples and problem sets; and test questions. | Data Analysis A Model Comparison Approach To Regression ANOVA and Beyond Third Edition

Introduction to the Principalship Theory to Practice

The second edition of this textbook from respected author team Kaplan and Owings explores how principals can effectively build a culture around student achievement. Introduction to the Principalship a second edition closely aligned with NELP (2018) standards helps aspiring principals understand how to develop a vision for improvement make decisions and manage conflict build teachers’ capacity communicate monitor the organization’s performance and create a school climate of mutual respect. This important book provides readers with various leadership concepts to inform their practice as well as the cognitive and practical tools to evaluate and prioritize what leadership actions to take. Each chapter offers opportunities for readers to create personal meaning and explore new ways of doing leadership to advance a positive person-focused environment. Providing both the theoretical framework and skills for effective practice Introduction to the Principalship addresses the issues most urgent and relevant for educational leadership graduate students learning how to build a school culture that promotes every student’s success. Fully revised this second edition includes a new chapter on building your capacity for leadership expanded discussion of data-informed accountability equity considerations and crisis management and all chapters updated and revised throughout to reflect the latest developments in the field. Special Features: •Learning Objectives—chapter openers introduce the topic and initiate student thinking. •Reflections and Relevance—interactive exercises role plays class activities and assignments that can be used synchronously and asynchronously to deepen and extend student learning. •Key Takeaways—organized by learning objective these answer readers’ question What about this information is meaningful for me as an aspiring principal? •Suggested Readings—each chapter concludes with annotated suggested readings to extend and deepen discussion of key issues in chapter. •NELP Standards—each chapter is aligned to the latest school leadership licensure standards. •Companion Website—includes links to supplemental material additional readings video clips with related teaching and learning activities and PowerPoints for instructors. | Introduction to the Principalship Theory to Practice

Introduction to Electromagnetism From Coulomb to Maxwell

This edition aims to expand on the first edition and take the reader through to the wave equation on coaxial cable and free-space by using Maxwell’s equations. The new chapters include time varying signals and fundamentals of Maxwell's equations. This book will introduce and discuss electromagnetic fields in an accessible manner. The author explains electroconductive fields and develops ideas relating to signal propagation and develops Maxwell’s equations and applies them to propagation in a planar optical waveguide. The first of the new chapters introduces the idea of a travelling wave by considering the variation of voltage along a coaxial line. This concept will be used in the second new chapter which solves Maxwell’s equations in free-space and then applies them to a planar optical waveguide in the third new chapter. As this is an area that most students find difficult it links back to the earlier chapters to aid understanding. This book is intended for first- and second-year electrical and electronic undergraduates and can also be used for undergraduates in mechanical engineering computing and physics. The book includes examples and homework problems. Introduces and examines electrostatic fields in an accessible manner Explains electroconductive fields Develops ideas relating to signal propagation Examines Maxwell’s equations and relates them to propagation in a planar optical waveguide Martin Sibley recently retired after 33 years of teaching at the University of Huddersfield. He has a PhD from Huddersfield Polytechnic in Preamplifier Design for Optical Receivers. He started his career in academia in 1986 having spent 3 years as a postgraduate student and then 2 years as a British Telecom-funded research fellow. His research work had a strong bias to the practical implementation of research and he taught electromagnetism and communications at all levels since 1986. Dr. Sibley finished his academic career as a Reader in Communications School of Computing and Engineering University of Huddersfield. He has authored five books and published over 80 research papers. | Introduction to Electromagnetism From Coulomb to Maxwell

Introduction to Air Transport Economics From Theory to Applications

Introduction to Air Transport Economics: From Theory to Applications uniquely merges the institutional and technical aspects of the aviation industry with their theoretical economic underpinnings. Its integrative approach offers a fresh point of view that will find favor with many students of aviation. This third edition has been extensively updated throughout. It features new material that stresses the dynamic aspects of demand and supply and the ongoing competitive aspects of the marketplace. It now features an introductory chapter and specific examples to more directly relate management decisions to the economic theory. Also in addition to an expanded coverage of revenue management and pricing decisions the third edition includes case studies that give real-world examples to reflect actual industry practice as well as a discussion of the more up-to-date computer applications that make the new techniques so effective. This book offers a self-contained theory and applications-oriented text for any individual intent on entering the aviation industry as a practicing professional in the management area. It will be of greatest relevance to undergraduate and graduate students interested in obtaining a more complete understanding of the economics of the aviation industry. It will also appeal to many professionals who seek an accessible and practical explanation of the underlying economic forces that shape the industry. | Introduction to Air Transport Economics From Theory to Applications

From Networks to Netflix A Guide to Changing Channels

Now in a second edition this textbook surveys the channels platforms and programming through which television distribution operates with a diverse selection of contributors providing thorough explorations of global media industries in flux. Even as legacy media industries experience significant disruption in the face of streaming and online delivery the power of the television channel persists. Far from disappearing television channels have multiplied and adapted to meet the needs of old and new industry players alike. Television viewers now navigate complex choices among broadcast cable and streaming services across a host of different devices. From Networks to Netflix guides students instructors and scholars through that complex and transformed channel landscape to reveal how these industry changes unfold and why they matter. This second edition features new players like Disney+ HBO Max Crunchyroll Hotstar and more increasing attention to TV services across the world. An ideal resource for students and scholars of media criticism media theory and media industries this book continues to offer a concrete tangible way to grasp the foundations of television—and television studies—even as they continue to be rewritten. | From Networks to Netflix A Guide to Changing Channels

Introduction to Geomorphology

The study of landforms is becoming increasingly scientific. This book first published in 1971 attempts to do justice to the work done in the last few decades but strives to avoid a too uncritical acceptance of contemporary trends. The author first examines the fundamental characteristics and basic postulates of geomorphology. He then seeks to define the systematic stages through which the study of the landforms of a given area might proceed. Examples are drawn from a wide geographical range with emphasis on presenting examples of actual observations and measurements. The final section presents concise descriptions of simple and inexpensive methods of acquiring field data in landform study. | Introduction to Geomorphology

Introduction to Spintronics

Introduction to Spintronics provides an accessible organized and progressive presentation of the quantum mechanical concept of spin and the technology of using it to store process and communicate information. Fully updated and expanded to 18 chapters this Second Edition:Reflects the explosion of study in spin-related physics addressing seven important physical phenomena with spintronic device applicationsDiscusses the recently discovered field of spintronics without magnetism which allows one to manipulate spin currents by purely electrical meansExplores lateral spin-orbit interaction and its many nuances as well as the possibility to implement spin polarizers and analyzers using quantum point contactsIntroduces the concept of single-domain-nanomagnet-based computing an ultra-energy-efficient approach to compute and store information using nanomagnets offering a practical rendition of single-spin logic architecture ideas and an alternative to transistor-based computing hardwareFeatures many new drill problems and includes a solution manual and figure slides with qualifying course adoptionStill the only known spintronics textbook written in English Introduction to Spintronics Second Edition is a must read for those interested in the science and technology of storing processing and communicating information via the spin degree of freedom of electrons.