Geo.Engineerings

Guy Simpson ... 272 pages - Publisher: Wiley-Blackwell; 1st edition (April, 2017) ... Language: English - ISBN-10: 1119248620 - ISBN-13: 978-1119248620 ... 

Mathematical models have become a crucial way for the Earth scientist to understand and predict how our planet functions and evolves through time and space. The finite element method (FEM) is a remarkably flexible and powerful tool with enormous potential in the Earth Sciences. This pragmatic guide explores how a variety of different Earth science problems can be translated and solved with FEM, assuming only basic programming experience. This book begins with a general introduction to numerical modeling and includes multiple sample Matlab codes to illustrate how FEM is implemented in practice. Textboxes have been included to provide additional detail, such as specialized Matlab usage or advanced topics. Covering all the key aspects, this is essential reading for those looking to master the technique, as well as those simply seeking to increase their basic level of understanding and appreciation of FEM.

The book is divided into 7 chapters containing material carefully selected and tailored to teaching several groups of biotechnology students. The topics are presented in a manner that allows readers to proceed sequentially on the strength of the preceding material. Primary MATLAB® for Life Sciences: A Guide for Beginners is essentially a concise and comprehensive text that provides an easy grasp and to-the-point access to the MATLAB tool to the community of life sciences and bio-engineering undergraduates and specialists.

The Art and Science of Learning from Data: Statistics: The Art and Science of Learning from Data, Fourth Edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data.

Massimiliano Bonamente ... 318 pages - Publisher: Springer; 2nd edition (November, 2016) ... Language: English - ISBN-10: 1493965700 - ISBN-13: 978-1493965700 ... 

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. • end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.