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For one/two semester courses in Engineering and Computer Science at the freshman/sophomore level.
Engineering Problem Solving With C++, Fourth Edition provides a clear, concise introduction to engineering problem solving with C++ as well as the object-oriented features of the C++ programming language. The authors’ proven five-step problem solving methodology is presented and then incorporated in every chapter of the text. The chapters in this text are designed to give the instructor flexibility in the ordering of topics with chapter topics covering the essentials of mathematical computations, character data, control structures, functions, arrays, classes, and pointers. Outstanding engineering and scientific applications are used throughout; all applications are centered around the theme of engineering challenges in the 21st century with an emphasis on incorporating real-world engineering and scientific examples and problems.
"General-equilibrium" refers to an analytical approach which looks at the economy as a complete system of inter-dependent components (industries, households, investors, governments, importers and exporters). "Applied" means that the primary interest is in systems that can be used to provide quantitative analysis of economic policy problems in particular countries. Reflecting the authors' belief in the models as vehicles for practical policy analysis, a considerable amount of material on data and solution techniques as well as on theoretical structures has been included. The sequence of chapters follows what is seen as the historical development of the subject.
The book is directed at graduate students and professional economists who may have an interest in constructing or applying general equilibrium models. The exercises and readings in the book provide a comprehensive introduction to applied general equilibrium modeling. To enable the reader to acquire hands-on experience with computer implementations of the models which are described in the book, a companion set of diskettes is available.
Contemporary societal problems are complex, intractable, and costly. Aiming to ameliorate them, social scientists formulate policies and programs, and conduct research testing the efficacy of the interventions. All too often the results are disappointing; partly because the theories guiding these studies are inappropriate, the study designs are flawed, and the empirical databases covering their research questions are sparse. This book confronts these problems of research by following this process: analyze the roots of the social problem both theoretically and empirically; formulate a study design that captures the nuances of the problem; gather appropriate empirical data operationalizing the study design; model these data using multilevel statistical methods to uncover potential causes and any biases to their implied effects; use the results by refining theory and by formulating evidence-based policy recommendations for implementation and testing.
Applying this process, the chapters focus on these social problems: political extremism; global human development; violence against religious minorities; computerization of work; reform of urban schools; and the utilization and costs of health care. Because these chapters exemplify the usefulness of multilevel modeling for the quantification of effects and causal inference, they can serve as vivid exemplars for the teaching of students. This use of examples reverses the usual procedure for introducing statistical methods. Rather than beginning with a new statistical model bearing on statistical theory and searching for illustrative data, each core chapter begins with a pressing social problem. The specific problem motivates theoretical analysis, gathering of relevant data, and application of appropriate statistical procedures. Readers can use the provided data sets and syntaxes to replicate, critique, and advance the analyses, thereby developing their ability to produce future applications of multilevel modeling.
The chapters address the multilevel data structures of these social problems by grouping observations on the micro units (level-1) by more macro-units (level-2) (e.g., school children are grouped by their classroom), and by conducting multilevel statistical modeling in contextual, longitudinal, and meta-analyses. Each core chapter applies a qualitative typology to nest the variance between the macro units, thereby crafting a "mixed-methods" approach that combines qualitative attributes with quantitative measures
The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed.
Aimed at researchers, graduate students and undergraduates alike, this book presents a unified exposition of all the main areas and methods of the theory of Kleinian groups and the theory of uniformization of manifolds. The past 20 years have seen a rejuvenation of the field, due to the development of powerful new methods in topology, the theory of functions of several complex variables, and the theory of quasiconformal mappings. Thus this new book should provide a valuable resource, listing the basic facts regarding Kleinian groups and serving as a general guide to the primary literature, particularly the Russian literature in the field. In addition, the book includes a large number of examples, problems, and unsolved problems, many of them presented for the first time.
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