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Newly revised to specifically address Microsoft Excel 2019, this book shows the capabilities of Excel in teaching engineering statistics effectively. Similar to the previously published Excel 2016 for Engineering Statistics, this volume is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical engineering problems. Excel, a widely available computer program for students and professionals, is also an effective teaching and learning tool for quantitative analyses in engineering courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Engineering Statistics capitalizes on these improvements by teaching readers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand engineering problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned. This new edition features a wealth of new sample problems and solutions, as well as updated chapter content throughout.

Statistics . --- Applied mathematics. --- Engineering mathematics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistics and Computing/Statistics Programs. --- Mathematical and Computational Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics.

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This book provides a systemic view on the digital future from the perspectives of various socio-humanitarian sciences: economics, social sciences, pedagogics and law. Presenting selected papers from the multi-disciplinary international conference “Climate changes and economy of the future: global transformation”, which was held at Pskov State University (Russia) on November 13–14, 2019, it offers a comprehensive overview of the current problems and the future potential of digital transformations of economic activities. This multidisciplinary book includes the latest research on the opportunities of the digital economy and the social and ecological consequences of its implementation, and as such offers a “road map” for development. It also features scientific and practical recommendations to allow effective management of the digitization process according to the current priorities.

Engineering economics. --- Engineering economy. --- Engineering mathematics. --- Engineering Economics, Organization, Logistics, Marketing. --- Engineering Mathematics. --- Engineering --- Engineering analysis --- Mathematical analysis --- Economy, Engineering --- Engineering economics --- Industrial engineering --- Mathematics --- Economics. --- Economic theory --- Political economy --- Social sciences --- Economic man

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This book focuses on the derivation and solution of Maxwell’s equations. The stations along the way include the laws of potential and current density distribution, as well as the laws of electrostatics and stationary magnetic fields. The book is chiefly intended for students of electrical engineering, information technology, and physics; the goal is to prepare them for courses on Electromagnetic Field Theory (EFT). Building on what they have learned in advanced physics and mathematics courses at secondary school or technical college, it is intended to accompany university-level EFT courses. Particular importance is attached to detailed explanations in text form, combined with a wealth of illustrations. All formulas are derived step by step. . Contents • Potential and Current Density Distribution • Electrostatics • The Stationary Magnetic Field • Time-Varying Electric and Magnetic Fields • Wave Propagation • Appendix: Verification of the Calculation Rules for Vector Analysis Target groups Students of electrical engineering and information technology, as well as physics students About the author Prof. Jürgen Donnevert lectures and conducts research in the fields of transmission systems, optical communications engineering, radio relay technology and mobile communications. .

Ampèreś circuital law. --- Antenna parameters. --- Displacement current. --- Electrical engineering. --- Engineering mathematics. --- Magnetism. --- Magnetic materials. --- Electrical Engineering. --- Engineering Mathematics. --- Magnetism, Magnetic Materials. --- Materials --- Mathematical physics --- Physics --- Electricity --- Magnetics --- Engineering --- Engineering analysis --- Mathematical analysis --- Electric engineering --- Mathematics

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Statistics . --- Applied mathematics. --- Engineering mathematics. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Applications of Mathematics. --- Statistics, general. --- Engineering --- Engineering analysis --- Mathematical analysis --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Finance --- Data processing.

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This book discusses the formalization of mathematical theories centering on complex analysis and matrix theory, covering topics such as algebraic systems, complex numbers, gauge integration, the Fourier transformation and its discrete counterpart, matrices and their transformation, inner product spaces, and function matrices. The formalization is performed using the interactive theorem prover HOL4, chiefly developed at the University of Cambridge. Many of the developments presented are now integral parts of the library of this prover. As mathematical developments continue to gain in complexity, sometimes demanding proofs of enormous sizes, formalization has proven to be invaluable in terms of obtaining real confidence in their correctness. This book provides a basis for the computer-aided verification of engineering systems constructed using the principles of complex analysis and matrix theory, as well as building blocks for the formalization of more involved mathematical theories.

Applied mathematics. --- Engineering mathematics. --- Computer science—Mathematics. --- Applications of Mathematics. --- Mathematical and Computational Engineering. --- Mathematics of Computing. --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Mathematical analysis. --- 517.1 Mathematical analysis