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Principles of Economics
by N. Gregory MankiwNow you can master the principles of economics with the help of the most popular, widely-used economics textbook by students worldwide -- Mankiw�s PRINCIPLES OF ECONOMICS, 8E. With its clear and engaging writing style, this book emphasizes only the material that will help you better understand the world in which you live, will make you a more astute participant in the economy, and will give you a better understanding of both the potential and limits of economic policy. The latest relevant economic examples bring principles to life. Acclaimed text author N. Gregory Mankiw explains, "I have tried to put myself in the position of someone seeing economics for the first time. My goal is to emphasize the material that students should and do find interesting about the study of the economy." Powerful student-focused digital resources are available in leading MindTap and Aplia digital learning and homework solutions that reinforce the principles presented in this edition.
Principles of Economics
by N. Gregory MankiwGain a strong foundation in economics with today's most popular economics text, trusted by students like you worldwide -- Mankiw's PRINCIPLES OF ECONOMICS, 10E. Using a clear, inviting writing style, this edition focuses exclusively on content to help you better understand the world and economy in which you live. You become a more astute participant in today's economic environment as you learn the potential and limits of economic policy. The latest examples and learning features clarify and bring economic principles to life as author Gregory Mankiw puts himself in the position of someone seeing economics for the first time. Dr. Mankiw's goal is to emphasize the material you will find most relevant and interesting as you study economics. MindTap digital resources, Aplia digital homework solution and author videos are also available to help you further master key economic principles.
Principles of Financial Accounting
by John Wild Ken ShawPrinciples of Financial Accounting responds to the market’s request for a financial accounting text with a sole-proprietorship approach. Chapter opening vignettes using dynamic entrepreneurs appeal to all students and show the relevance of accounting. Students are encouraged to think like a businessperson and apply what they learn. A wide variety of assignments provide instructors with materials to teach, assess, and challenge students on several levels.
Principles of Fourier Analysis (Textbooks in Mathematics)
by Kenneth B. HowellFourier analysis is one of the most useful and widely employed sets of tools for the engineer, the scientist, and the applied mathematician. As such, students and practitioners in these disciplines need a practical and mathematically solid introduction to its principles. They need straightforward verifications of its results and formulas, and they need clear indications of the limitations of those results and formulas.Principles of Fourier Analysis furnishes all this and more. It provides a comprehensive overview of the mathematical theory of Fourier analysis, including the development of Fourier series, "classical" Fourier transforms, generalized Fourier transforms and analysis, and the discrete theory. Much of the author's development is strikingly different from typical presentations. His approach to defining the classical Fourier transform results in a much cleaner, more coherent theory that leads naturally to a starting point for the generalized theory. He also introduces a new generalized theory based on the use of Gaussian test functions that yields an even more general -yet simpler -theory than usually presented.Principles of Fourier Analysis stimulates the appreciation and understanding of the fundamental concepts and serves both beginning students who have seen little or no Fourier analysis as well as the more advanced students who need a deeper understanding. Insightful, non-rigorous derivations motivate much of the material, and thought-provoking examples illustrate what can go wrong when formulas are misused. With clear, engaging exposition, readers develop the ability to intelligently handle the more sophisticated mathematics that Fourier analysis ultimately requires.
Principles of Harmonic Analysis
by Anton Deitmar Siegfried EchterhoffThis book offers a complete and streamlined treatment of the central principles of abelian harmonic analysis: Pontryagin duality, the Plancherel theorem and the Poisson summation formula, as well as their respective generalizations to non-abelian groups, including the Selberg trace formula. The principles are then applied to spectral analysis of Heisenberg manifolds and Riemann surfaces. This new edition contains a new chapter on p-adic and adelic groups, as well as a complementary section on direct and projective limits. Many of the supporting proofs have been revised and refined. The book is an excellent resource for graduate students who wish to learn and understand harmonic analysis and for researchers seeking to apply it.
Principles of Highway Engineering and Traffic Analysis (5th Edition)
by Fred L. Mannering Scott S. WashburnThe 5th edition of the Mannering's Principles of Highway Engineering and Traffic Analysis continues to offer a concise approach that covers all the necessary fundamental concepts. New features in this edition include updates and more consistency with the latest edition of the Highway Capacity Manual (HCM); the inclusion of sample FE exam questions, call-out of common mistakes; and added coverage on a qualitative description of the mechanistic approach.
Principles of Linear Algebra with Mathematica
by Karl Frinkle Kenneth M. ShiskowskiA hands-on introduction to the theoretical and computational aspects of linear algebra using Mathematica®Many topics in linear algebra are simple, yet computationally intensive, and computer algebra systems such as Mathematica® are essential not only for learning to apply the concepts to computationally challenging problems, but also for visualizing many of the geometric aspects within this field of study. Principles of Linear Algebra with Mathematica uniquely bridges the gap between beginning linear algebra and computational linear algebra that is often encountered in applied settings, and the commands required to solve complex and computationally challenging problems using Mathematica are provided.The book begins with an introduction to the commands and programming guidelines for working with Mathematica. Next, the authors explore linear systems of equations and matrices, applications of linear systems and matrices, determinants, inverses, and Cramer's rule. Basic linear algebra topics, such as vectors, dot product, cross product, and vector projection are explored, as well as a unique variety of more advanced topics including rotations in space, 'rolling' a circle along a curve, and the TNB Frame. Subsequent chapters feature coverage of linear transformations from Rn to Rm, the geometry of linear and affine transformations, with an exploration of their effect on arclength, area, and volume, least squares fits, and pseudoinverses.Mathematica is used to enhance concepts and is seamlessly integrated throughout the book through symbolic manipulations, numerical computations, graphics in two and three dimensions, animations, and programming. Each section concludes with standard problems in addition to problems that were specifically designed to be solved with Mathematica, allowing readers to test their comprehension of the presented material. All related Mathematica code is available on a corresponding website, along with solutions to problems and additional topical resources.Extensively class-tested to ensure an accessible presentation, Principles of Linear Algebra with Mathematica is an excellent book for courses on linear algebra at the undergraduate level. The book is also an ideal reference for students and professionals who would like to gain a further understanding of the use of Mathematica to solve linear algebra problems.
Principles of Machine Learning: The Three Perspectives
by Wenmin WangConducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples. The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction. This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Principles of Managerial Statistics and Data Science
by Roberto RiveraIntroduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.
Principles of Mathematical Modelling: Ideas, Methods, Examples (Numerical Insights)
by Alexander A. Samarskii Alexander P. MikhailovMathematical modeling is becoming increasingly versatile and multi-disciplinary. This text demonstrates the broadness of this field as the authors consider the principles of model construction and use common approaches to build models from a range of subject areas. The book reflects the interests and experiences of the authors, but it explores math
Principles of Mathematics
by Vladimir LepeticPresents a uniquely balanced approach that bridges introductory and advanced topics in modern mathematics An accessible treatment of the fundamentals of modern mathematics, Principles of Mathematics: A Primer provides a unique approach to introductory andadvanced mathematical topics. The book features six main subjects, whichcan be studied independently or in conjunction with each other including: settheory; mathematical logic; proof theory; group theory; theory of functions; andlinear algebra. The author begins with comprehensive coverage of the necessary building blocks in mathematics and emphasizes the need to think abstractly and develop an appreciation for mathematical thinking. Maintaining a useful balance of introductory coverage and mathematical rigor, Principles of Mathematics: A Primer features: Detailed explanations of important theorems and their applications Hundreds of completely solved problems throughout each chapter Numerous exercises at the end of each chapter to encourage further exploration Discussions of interesting and provocative issues that spark readers' curiosity and facilitate a better understanding and appreciation of the field of mathematics Principles of Mathematics: A Primer is an ideal textbook for upper-undergraduate courses in the foundations of mathematics and mathematical logic as well as for graduate-level courses related to physics, engineering, and computer science. The book is also a useful reference for readers interested in pursuing careers in mathematics and the sciences. Vladimir Lepetic, PhD, is Professor in the Department of Mathematical Sciences at DePaul University. His research interests include mathematical physics, set theory, foundations of mathematics, and the philosophy of mathematics.
Principles of Mathematics (Routledge Classics)
by Bertrand RussellFirst published in 1903, Principles of Mathematics was Bertrand Russell’s first major work in print. It was this title which saw him begin his ascent towards eminence. In this groundbreaking and important work, Bertrand Russell argues that mathematics and logic are, in fact, identical and what is commonly called mathematics is simply later deductions from logical premises. Highly influential and engaging, this important work led to Russell’s dominance of analytical logic on western philosophy in the twentieth century.
Principles of Mathematics: Book 1
by Katherine A. LoopThis is Year 1 of a two-year math course designed to give students a firm mathematical foundation, both academically and spiritually. Not only does the curriculum build mathematical thinking and problem-solving skills, it also shows students how a biblical worldview affects our approach to math's various concepts. Students learn to see math, not as an academic exercise, but as a way of exploring and describing consistencies God created and sustains. The worldview is not just an addition to the curriculum, but the starting point. Science, history, and real life are integrated throughout.
Principles of Medical Statistics
by Alvan R. FeinsteinThe get-it-over-with-quickly approach to statistics has been encouraged - and often necessitated - by the short time allotted to it in most curriculums. If included at all, statistics is presented briefly, as a task to be endured mainly because pertinent questions may appear in subsequent examinations for licensure or other certifications. However,
Principles of Microeconomics
by N. Gregory MankiwProvide an inviting, superbly written introduction to microeconomics that allows all types of students to quickly grasp microeconomic concepts and build a strong foundation. The most widely-used learning solution in economics classrooms worldwide, written by acclaimed author and economist Gregory Mankiw, PRINCIPLES OF MICROECONOMICS, 9E focuses on the most important microeconomic principles to avoid overwhelming students with excessive detail. Extensive updates, relevant contemporary examples and carefully crafted content present the understanding your students need at key moments in your course. This edition works seamlessly with the latest MindTap digital course solutions and powerful student-focused resources, featuring the highly acclaimed Graph Builder and enhanced Adaptive Test Prep. A team of award-winning teaching economists have further strengthened this edition's leading ancillary support with more relevance, engagement, and interactive learning opportunities.
Principles of Microeconomics
by N. Gregory MankiwIntroduce microeconomics using the inviting, reader-friendly approach in PRINCIPLES OF MICROECONOMICS, 10E by acclaimed author and economist Gregory Mankiw. The most widely-used learning solution in economics classrooms worldwide, this edition is designed to guide all types of learners to a strong foundation in microeconomics with the latest updates, interactive practice and current examples. Carefully crafted content focuses on the most important microeconomic principles to avoid overwhelming students with excessive detail. The author presents the background students need at key moments in your course. This edition works seamlessly with the latest MindTap digital course solutions and student-focused resources. A leading support package by award-winning teaching economists with Aplia homework solutions and author videos provides additional relevance and interactive learning opportunities.
Principles of Microeconomics: An Integrative Approach (Springer Texts in Business and Economics)
by Martin KolmarThis text provides a comprehensive and unique introduction to modern microeconomics. It pursues an integrative approach by putting the main findings of economics into a broader perspective; theories are critically reflected on from a philosophical standpoint and by comparing them to approaches found in the social sciences, while implications for the design of the legal system and business practices are highlighted throughout. In addition, the book presents brief examples and comprehensive case studies to facilitate an understanding of the theories’ real-world implications.Starting from the question as to why and how societies organize economic activity, the book adopts an institutional perspective to analyze the potential and limitations of different market types with regard to alleviating scarcity and achieving distributive objectives.It not only covers traditional rational-choice models, but also systematically introduces readers to important findings from behavioral economics and psychology.A companion workbook is available which features a wide variety of exercises, ranging from basic multiple-choice questions to challenging mathematical problems and case study scenarios.
Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses
by Wenzhong ShiWhen compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t
Principles of Modeling and Simulation
by Catherine M. Banks John A. SokolowskiExplores wide-ranging applications of modeling and simulation techniques that allow readers to conduct research and ask "What if??"Principles of Modeling and Simulation: A Multidisciplinary Approach is the first book to provide an introduction to modeling and simulation techniques across diverse areas of study. Numerous researchers from the fields of social science, engineering, computer science, and business have collaborated on this work to explore the multifaceted uses of computational modeling while illustrating their applications in common spreadsheets.The book is organized into three succinct parts:Principles of Modeling and Simulation provides a brief history of modeling and simulation, outlines its many functions, and explores the advantages and disadvantages of using models in problem solving. Two major reasons to employ modeling and simulation are illustrated through the study of a specific problem in conjunction with the use of related applications, thus gaining insight into complex concepts.Theoretical Underpinnings examines various modeling techniques and introduces readers to two significant simulation concepts: discrete event simulation and simulation of continuous systems. This section details the two primary methods in which humans interface with simulations, and it also distinguishes the meaning, importance, and significance of verification and validation.Practical Domains delves into specific topics related to transportation, business, medicine, social science, and enterprise decision support. The challenges of modeling and simulation are discussed, along with advanced applied principles of modeling and simulation such as representation techniques, integration into the application infrastructure, and emerging technologies.With its accessible style and wealth of real-world examples, Principles of Modeling and Simulation: A Multidisciplinary Approach is a valuable book for modeling and simulation courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for researchers and practitioners working in statistics, mathematics, engineering, computer science, economics, and the social sciences who would like to further develop their understanding and knowledge of the field.
Principles of Nonlinear Filtering Theory (Algorithms and Computation in Mathematics #33)
by Stephen S.-T. Yau Xiuqiong Chen Xiaopei Jiao Jiayi Kang Zeju Sun Yangtianze TaoThis text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today’s landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come. With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observations—a critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered. An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book. The book is structured into three cohesive parts, each designed to build the reader's understanding of nonlinear filtering theory. In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning.
Principles of Optimal Design
by Douglass J. Wilde Panos Y. PapalambrosPrinciples of Optimal Design puts the concept of optimal design on a rigorous foundation and demonstrates the intimate relationship between the mathematical model that describes a design and the solution methods that optimize it. Since the first edition was published, computers have become ever more powerful, design engineers are tackling more complex systems, and the term optimization is now routinely used to denote a design process with increased speed and quality. This second edition takes account of these developments and brings the original text thoroughly up to date. The book now includes a discussion of trust region and convex approximation algorithms. A new chapter focuses on how to construct optimal design models. Three new case studies illustrate the creation of optimization models. The final chapter on optimization practice has been expanded to include computation of derivatives, interpretation of algorithmic results, and selection of algorithms and software. Both students and practising engineers will find this book a valuable resource for design project work.
Principles of Psychological Assessment: With Applied Examples in R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
by Isaac T. PetersenThis book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments. It discusses psychometrics (reliability and validity), the assessment of various psychological domains (behavior, personality, intellectual functioning), various measurement methods (e.g., questionnaires, observations, interviews, biopsychological assessments, performance-based assessments), and emerging analytical frameworks to evaluate and improve assessment including: generalizability theory, structural equation modeling, item response theory, and signal detection theory. The text also discusses ethics, test bias, and cultural and individual diversity.Key Features Gives analysis examples using free software Helps readers apply principles to research and practice Provides text, analysis code/syntax, R output, figures, and interpretations integrated to guide readers Uses the freely available petersenlab package for R Principles of Psychological Assessment: With Applied Examples in R is intended for use by graduate students, faculty, researchers, and practicing psychologists.
Principles of Scientific Methods
by Mark ChangThis book focuses on the fundamental principles behind scientific methods. The author uses concrete examples to explain principles. He also uses analogies to connect different methods or problems to arrive at a general principle or common notion. The book explores how the principles of scientific methods are not only applicable to scientific research but also in our daily lives. It shows how the scientific method is used to understand how and why things happen, make predictions, prevent mistakes, and solve problems.
Principles of Statistical Inference
by D. R. CoxIn this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.
Principles of Statistics
by M. G. BulmerThere are many textbooks which describe current methods of statistical analysis, while neglecting related theory. There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study. Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.