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Applied Mergers and Acquisitions (Wiley Finance #807)
by Robert F. BrunerA comprehensive guide to the world of mergers and acquisitions Why do so many M&A transactions fail? And what drives the success of those deals that are consummated? Robert Bruner explains that M&A can be understood as a response by managers to forces of turbulence in their environment. Despite the material failure rates of mergers and acquisitions, those pulling the trigger on key strategic decisions can make them work if they spend great care and rigor in the development of their M&A deals. By addressing the key factors of M&A success and failure, Applied Mergers and Acquisitions can help readers do this. Written by one of the foremost thinkers and educators in the field, this invaluable resource teaches readers the art and science of M&A valuation, deal negotiation, and bargaining, and provides a framework for considering tradeoffs in an effort to optimize the value of any M&A deal.
Applied Mergers and Acquisitions Workbook (Wiley Finance #175)
by Robert F. BrunerThe Applied Mergers and Acquisitions Workbook provides a useful self-training study guide for readers of Applied Mergers and Acquisitions who want to review the drivers of M&A success and failure. Useful review questions as well as problems and answers are provided for both professionals and students. Readers will further their knowledge, build practical intuition, and learn the art and science of M&A by using this comprehensive self-study workbook in conjunction with the main text.
Applied Methods for Agriculture and Natural Resource Management: A Festschrift in Honor of Richard E. Howitt (Natural Resource Management and Policy #50)
by Siwa Msangi Duncan MacEwanThis book assesses recent developments in the analysis of agricultural policy and water resource management, and highlights the utility and theoretical rigor of quantitative methods for modeling agricultural production, market dynamics, and natural resource management. In diverse case studies of the intersection between agriculture, environmental quality and natural resource sustainability, the authors analyze economic behavior - both at aggregate as well as at individual agent-level - in order to highlight the practical implications for decision-markers dealing with environmental and agricultural policy. The volume also addresses the challenges of doing robust analysis with limited data, and discusses the appropriate empirical approaches that can be employed. The studies in this book were inspired by the work of Richard E. Howitt, Emeritus Professor of Agricultural Economics at the University of California at Davis, USA, whose career has focused on the application of robust empirical methods to address concrete policy problems.
Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools
by Yannis Dimotikalis Alex Karagrigoriou Christina Parpoula Christos H. SkiadasBIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools
by Yannis Dimotikalis Alex Karagrigoriou Christina Parpoula Christos H SkiadasBIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Applied Modeling Techniques and Data Analysis 2: Financial, Demographic, Stochastic and Statistical Models and Methods
by Yannis Dimotikalis Alex Karagrigoriou Christina Parpoula Christos H. SkiadasBIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Applied Modeling Techniques and Data Analysis 2: Financial, Demographic, Stochastic and Statistical Models and Methods
by Yannis Dimotikalis Alex Karagrigoriou Christina Parpoula Christos H. SkiadasBIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Applied Multivariate Analysis
by Ira H. BernsteinLike most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intel lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to "concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis . . . . (and) . . . who are more interested in the sciences than in mathematics, among other characteristics. " I find the distinction between individuals who are temperamentally "mathe maticians" (whom philosophy students might call "Platonists") and "scientists" ("Aristotelians") useful as long as it is not pushed to the point where one assumes "mathematicians" completely disdain data and "scientists" are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the "scientist" rather than the "mathematician" role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions.
Applied Multivariate Analysis (Springer Texts in Statistics)
by Neil H. TimmThis book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.
Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods (Springer Texts in Statistics)
by J.D. JobsonA Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta.
Applied Multivariate Data Analysis: Regression and Experimental Design (Springer Texts in Statistics)
by J.D. JobsonAn easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
Applied Multivariate Statistical Analysis
by Wolfgang Karl Härdle Léopold SimarFocusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features:A new chapter on Variable Selection (Lasso, SCAD and Elastic Net)All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de.The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.
Applied Multivariate Statistical Analysis
by Wolfgang Karl Härdle Léopold SimarThis textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MATLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.The Quantlet platform, quantlet.de, quantlet.com, quantlet.org, is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding data-driven document-based visualization allow readers to reproduce the tables, pictures and calculations presented in this Springer book.
Applied Multivariate Statistical Analysis
by Wolfgang Karl Härdle Léopold SimarA state of the art presentation of the tools and concepts of multivariate data analysis with a strong focus on applications. The first part is devoted to graphical techniques describing the distributions of the involved variables. The second part deals with multivariate random variables and presents distributions, estimators and tests for various practical situations. The last part covers mulivariate techniques and introduces the reader into the wide variety of tools for multivariate data analysis. The text presents a wide range of examples and 228 exercises.
Applied Multivariate Statistical Analysis
by Wolfgang Karl Härdle Léopold SimarWith a wealth of examples and exercises, this is a brand new edition of a classic work on multivariate data analysis. A key advantage of the work is its accessibility. This is because, in its focus on applications, the book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. In this second edition a wider scope of methods and applications of multivariate statistical analysis is introduced. All quantlets have been translated into the R and Matlab language and are made available online.
Applied Multivariate Statistical Analysis
by Wolfgang Karl Härdle Léopold SimarMost of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields.The third edition of this book on Applied Multivariate Statistical Analysis offers the following new featuresA new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
Applied Multivariate Statistical Concepts
by Debbie L. Hahs-VaughnMore comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader’s master key concepts so they can implement and interpret results generated by today’s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a ‘mathematical snapshot’ that highlights the technical components of each procedure, so only the most crucial equations are included. Highlights include: -Outlines, key concepts, and vignettes related to key concepts preview what’s to come in each chapter -Examples using real data from education, psychology, and other social sciences illustrate key concepts -Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique -Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers -A focus on data screening and power analysis with attention on the special needs of each particular method -Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results -Templates for writing research questions and APA-style write-ups of results which serve as models -Propensity score analysis chapter that demonstrates the use of this increasingly popular technique -A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed) -www.routledge.com/9780415842365 provides the text’s datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors ?
Applied Multivariate Statistical Concepts
by Debbie L. Hahs-VaughnMore comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader’s master key concepts so they can implement and interpret results generated by today’s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a ‘mathematical snapshot’ that highlights the technical components of each procedure, so only the most crucial equations are included. Highlights include: -Outlines, key concepts, and vignettes related to key concepts preview what’s to come in each chapter -Examples using real data from education, psychology, and other social sciences illustrate key concepts -Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique -Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers -A focus on data screening and power analysis with attention on the special needs of each particular method -Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results -Templates for writing research questions and APA-style write-ups of results which serve as models -Propensity score analysis chapter that demonstrates the use of this increasingly popular technique -A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed) -www.routledge.com/9780415842365 provides the text’s datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors ?
Applied OpenStack Design Patterns: Design solutions for production-ready infrastructure with OpenStack components
by Uchit VyasLearn practical and applied OpenStack cloud design solutions to gain maximum control over your infrastructure. You will achieve a complete controlled and customizable platform.Applied OpenStack Design Patterns teaches you how to map your application flow once you set up components and architectural design patterns. Also covered is storage management and computing to map user requests and allocations. Best practices of High Availability and Native Cluster Management are included. Solutions are presented to network components of OpenStack and to reduce latency and enable faster communication gateways between components of OpenStack and native applications.What You Will Learn: Design a modern cloud infrastructureSolve complex infrastructure application problems Understand OpenStack cloud infrastructure componentsAdopt a business impact analysis to support existing/new cloud infrastructureUse specific components to integrate an existing tool-chain set to gain agility and a quick, continuous delivery modelWho This Book Is For:Seasoned solution architects, DevOps, and system engineers and analysts
Applied Operational Research with SAS
by Ali Emrouznejad William HoUsing 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 as
Applied Operations Research and Financial Modelling in Energy: Practical Applications and Implications
by Mehmet Baha Karan André B. Dorsman Kazim Baris Atici Aydin UlucanThis book on Applied Operations Research and Financial Modelling in Energy (AORFME) presents several applications of operations research (OR) and financial modelling. The contributions by a group of OR and Finance researchers focus on a variety of energy decisions, presenting a quantitative perspective, and providing policy implications of the proposed or applied methodologies. The content is divided into three main parts: Applied OR I: Optimization Approaches, Applied OR II: Forecasting Approaches and Financial Modelling: Impacts of Energy Policies and Developments in Energy Markets. The book appeals to scholars in economics, finance and operations research, and to practitioners working in the energy sector. This is the eighth volume in a series of books on energy organized by the Centre for Energy and Value Issues (CEVI). For this volume, CEVI collaborated with Hacettepe University’s Energy Markets Research and Application Center. The previous volumes in the series are: Financial Aspects in Energy (2011), Energy Economics and Financial Markets (2012), Perspectives on Energy Risk (2014), Energy Technology and Valuation Issues (2015), Energy and Finance (2016), Energy Economy, Finance and Geostrategy (2018), and Financial Implications of Regulations in the Energy Industry (2020).
Applied Optimization in the Petroleum Industry
by Hesham K. AlfaresThe book addresses optimization in the petroleum industry from a practical, large-scale-application-oriented point of view. The models and techniques presented help to optimize the limited resources in the industry in order to maximize economic benefits, ensure operational safety, and reduce environmental impact. The book discusses several important real-life applications of optimization in the petroleum industry, ranging from the scheduling of personnel time to the blending of gasoline. It covers a wide spectrum of relevant activities, including drilling, producing, maintenance, and distribution. The text begins with an introductory overview of the petroleum industry and then of optimization models and techniques. The main body of the book details a variety of applications of optimization models and techniques within the petroleum industry.Applied Optimization in the Petroleum Industry helps readers to find effective optimization-based solutions to their own practical problems in a large and important industrial sector, still the main source of the world’s energy and the source of raw materials for a wide variety of industrial and consumer products.
Applied Panel Data Analysis for Economic and Social Surveys
by Hans-Jürgen Andreß Katrin Golsch Alexander W. SchmidtMany economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation.
Applied Pedagogies for Higher Education: Real World Learning and Innovation across the Curriculum
by Dawn A. Morley Md Golam JamilThis open access book critiques real world learning across both the curriculum and extracurricular activities. Drawing on disciplines as diverse as business, health, fashion, sociology and geography, the editors and authors employ a cross-disciplinary approach to examine how this concept is being applied in higher education. Divided into three parts, the authors and contributors analyse broader applications of real world learning, student experience of practicing in a real world setting, and how learning strategies can be employed to engage students in real world learning. The editors and contributors provide up-to-date, cross-disciplinary and international insights into how real world learning could be integrated into the higher education curriculum to support effective, relevant and life-long learning for 21st century students.
Applied Political Economic Modelling (Studies in Contemporary Economics)
by Paul S.A. Renaudproject was the development and application of a model of central and local government behavior. It was carried out in collaboration with the Dutch Department of Home Affairs, during the period September 1985 - April 1987. The project's aim was the study of the determinants of local government decisionmaking in the Netherlands and the implications of so-called intergovernmental relations for local government behavior. During that project I was assisted by Sander Helder and Marc Tigche1aar. The results of that project appeared in a book, co-authored by Frans van Winden, entitled Gemeentefinancien en Gedecentra1iseerde. Bes1uitvorming(Loca1 Public Finance and Decentralized Decisionmaking). This book is, for the time being, the end product of my research on government behavior. Much more research is needed on the study of government behavior and the application of models which, from an economic point of view, focus on the implications of the interaction between economics and politics. The knowledge of this process is not only interesting as such, but also from a practical point of view. without a good positive analysis of the behavior of the government in modern industrialized economies it is neither possible to see through its operations nor to use its policies as an effective instrument in the pursuit of economic targets.