{"title":"Maths for scientists Books","description":"","products":[{"product_id":"schaums-outline-of-advanced-mathematics-for-engineers-and-scientists-9780071635400","title":"Schaums Outline of Advanced Mathematics for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eTough Test Questions? Missed Lectures? Not Enough Time?\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFortunately for you, there's Schaum's.\u003c\/p\u003e\u003cp\u003eMore than 40 million students have trusted Schaum's Outlines to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. \u003c\/p\u003e\u003cp\u003eThis Schaum's Outline gives you:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePractice problems with full explanations that reinforce knowledge \u003c\/li\u003e\n\u003cli\u003eCoverage of the most up-to-date developments in your course field \u003c\/li\u003e\n\u003cli\u003eIn-depth review of practices and applications \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eFully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!\u003c\/p\u003e\u003cp\u003eSchaum's Outlines-Problem Solved.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003cbr\u003e Schaum's Outline of Advanced Mathematics for Engineers and Scientists \u003c\/b\u003e\u003cbr\u003e 1.Review of Fundamental Concepts\u003cbr\u003e 2.Ordinary Differential Equations\u003cbr\u003e 3.Linear Differential Equations\u003cbr\u003e 4.LaPlace Transforms\u003cbr\u003e 5.Vector Analysis\u003cbr\u003e 6.Multiple Line and Surface Integrals and Integral Theorems\u003cbr\u003e 7.Fourier Series\u003cbr\u003e 8.Fourier Integrals\u003cbr\u003e 9.Partial Differential Equations\u003cbr\u003e 10. Complex Variables and Conformal Mapping\u003cbr\u003e 11. Complex Inversion Formula for Laplace Transforms\u003cbr\u003e 12. Matrices\u003cbr\u003e 13. Calculus of Variations\u003c\/p\u003e","brand":"McGraw-Hill Education - Europe","offers":[{"title":"Default Title","offer_id":48732174975319,"sku":"9780071635400","price":16.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780071635400.jpg?v=1719995840"},{"product_id":"what-is-mathematics-9780195105193","title":"What Is Mathematics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFor more than two thousand years a familiarity with mathematics has been regarded as an indispensable part of the intellectual equipment of every cultured person. Today, unfortunately, the traditional place of mathematics in education is in grave danger. The teaching and learning of mathematics has degenerated into the realm of rote memorization, the outcome of which leads to satisfactory formal ability but does not lead to real understanding or to greater intellectual independence. This new edition of Richard Courant''s and Herbert Robbins''s classic work seeks to address this problem. Its goal is to put the meaning back into mathematics.  Written for beginners and scholars, for students and teachers, for philosophers and engineers, What is Mathematics?, Second Edition is a sparkling collection of mathematical gems that offers an entertaining and accessible portrait of the mathematical world. Covering everything from natural numbers and the number system to geometrical constructions and projective geometry, from topology and calculus to matters of principle and the Continuum Hypothesis, this fascinating survey allows readers to delve into mathematics as an organic whole rather than an empty drill in problem solving. With chapters largely independent of one another and sections that lead upward from basic to more advanced discussions, readers can easily pick and choose areas of particular interest without impairing their understanding of subsequent parts. Brought up to date with a new chapter by Ian Stewart, What is Mathematics?, Second Edition offers new insights into recent mathematical developments and describes proofs of the Four-Color Theorem and Fermat''s Last Theorem, problems that were still open when Courant and Robbins wrote this masterpiece, but ones that have since been solved. Formal mathematics is like spelling and grammar--a matter of the correct application of local rules. Meaningful mathematics is like journalism--it tells an interesting story. But unlike some journalism, the story has to be true. The best mathematics is like literature--it brings a story to life before your eyes and involves you in it, intellectually and emotionally. What is Mathematics is like a fine piece of literature--it opens a window onto the world of mathematics for anyone interested to view.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eCan...be read with great profit by anyone desiring general mathematical literacy. * Mathematics Abstracts *\u003cbr\u003eA great book. * Ludwig Otto, Paul Quinn College *\u003cbr\u003eA lucid representation of the fundamental concepts and methods of the whole field of mathematics. It is an easily understandable introduction for the layman and helps to give the mathematical student a general view of the basic principles and methods. * Albert Einstein *\u003cbr\u003eWithout doubt, the work will have great influence. It should be in the hands of everyone, professional or otherwise, who is interested in scientific thinking. * The New York Times *\u003cbr\u003eA work of extraordinary perfection. * Mathematical Reviews *\u003cbr\u003eIt contains an excellent selection of material for students who have no desire to develop mathematical skills but who may be willing to look briefly into this field of intellectual activity....For the inquiring student who wishes to know what real mathematics is about, or for the trained engineer or physicist who has some interest in the justification of procedures he uses, it should prove a source of great pleasure and satisfaction. * Journal of Applied Physics *\u003cbr\u003eThis book is a work of art. * Marston Morse *\u003cbr\u003eThis is not a book in philosophy; but there are probably few philosophers who can not gain instruction and clarification from it. It succeeds brilliantly in conveying the intellectual excitement of mathematical inquiry and in communicating the essential ideas and methods.\"Journal of Philosophy\u003cbr\u003eIt is a work of high perfection, whether judged by aesthetic, pedagogical or scientific standards. It is astonishing to what extent What is Mathematics? has succeeded in making clear by means of the simplest examples all the fundamental ideas and methods which we mathematicians consider the life blood of our science. * Herman Weyl *\u003cbr\u003eStill a book that all prospective mathematics teachers should read and experience. A rare book that has retained its \"freshness\" and readability for more than 50 years....Very readable. * Stephen Krulik, Temple University *","brand":"Oxford University Press Inc","offers":[{"title":"Default Title","offer_id":48732636938583,"sku":"9780195105193","price":19.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"maths-skills-for-a-level-physics-9780198428985","title":"Maths Skills for A Level Physics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe maths needed to succeed in A Level Science is harder now than ever before. Suitable for all awarding bodies, this practical handbook addresses all of the maths skills needed for A Level Physics specifications. Worked examples, practice questions, ''remember points'' and ''stretch yourself'' questions give students the key knowledge and then the opportunity to practise and build confidence.","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732731343191,"sku":"9780198428985","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"probability-9780198709978","title":"Probability","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProbability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters'' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains.A special feature is the authors'' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART A BASIC PROBABILITY; PART B FURTHER PROBABILITY","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732767650135,"sku":"9780198709978","price":37.04,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198709978.jpg?v=1719998311"},{"product_id":"maths-for-chemistry-9780198717324","title":"Maths for Chemistry","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe perfect introduction to the essential mathematical concepts which all chemistry students need to master. Working from foundational principles, the book builds the student's confidence by leading them through the subject in a steady, progressive way from basic algebra to the mathematics of quantum chemistry. mathematics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eA very useful text to gradually guide students through both the fundamental and more advanced aspects of mathematics specifically relevant for a chemistry undergraduate degree. It is particularly useful in allowing students to test their knowledge of mathematical concepts and processes via self-test exercise and additional problems that are directly relevant to chemistry. * Dr Jon Tandy, Senior Lecturer in Physical Chemistry, London Metropolitan University *\u003cbr\u003eThis is an outstanding and carefully thought-out introduction to the mathematical toolkit required for students embarking on a chemistry degree programme. * Dr Robert Johnson, Lecturer, School of Chemistry, University College Dublin *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSection A: Core mathematics: algebra, logarithms and trigonometry 1: The display of numbers 2: Algebra I 3: Algebra II 4: Algebra III 5: Algebra IV 6: Algebra V 7: Algebra VI 8: Algebra VII 9: Powers I 10: Powers II 11: Trigonometry 12: Advanced BODMAS Section B: Calculus 13: Differentiation I 14: Differentiation II 15: Differentiation III 16: Differentiation IV 17: Differentiation V 18: Differentiation VI 19: Integration I 20: Integration II 21: Integration III 22: Integration IV Section C: Matrices, vectors and complex numbers 23: Matrices I 24: Matrices II 25: Complex numbers 26: Vectors Section D: Laboratory mathematics 27: Graphs I 28: Graphs II 29: Graphs III 30: Probability I 31: Probability II 32: Statistics I 33: Statistics II 34: Statistics III 35: Statistics IV 36: Dimensional analysis","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732769026391,"sku":"9780198717324","price":45.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198717324.jpg?v=1719998317"},{"product_id":"biomeasurement-a-students-guide-to-biological-statistics-9780198807483","title":"Biomeasurement A Students Guide to Biological","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA refreshing, student-focused introduction to the use of statistics in the study of the biosciences. Emphasising why statistical techniques are essential tools for bioscientists, Biomeasurement removes the stigma attached to statistics by giving students the confidence to use key techniques for themselves.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eReview from previous edition Biomeasurement does a wonderful job of keeping the biology the focus of analysis, and highlighting the fact that statistics is simply another useful tool to help biological understanding. * Dr Shane Richards, Biological and Biomedical Sciences, University of Durham, UK *\u003cbr\u003eThis book represents the best I have seen for teaching undergraduate biologists statistics. * Dr Chris Venditti, Department of Biological Sciences, University of Hull, UK *\u003cbr\u003eIt demystifies and clarifies topics that students can normally find confusing and challenging. It is a must for biologists! * Dr Maria G. Tuohy, School of Natural Sciences, National University of Ireland, Galway *\u003cbr\u003eThis book was a blessing when I got it for my first year, and I'm still finding it helpful in my second year! It is so easy to navigate; you can read it cover to cover if you are very confused, or just dip into the topics you need. I would definitely recommend it to any students doing a bioscience degree which involves statistical elements. * Bethany Richmond, student at the University of Warwick *\u003cbr\u003eAs a student with limited mathematical ability, new to statistics who believed I would not be able to pass this module, I read this book chapter by chapter prior to my weekly lectures and everything fell into place without a struggle. A 100% necessary purchase. A 100% necessary read. The book is appealing and very easy to navigate. I have tried to read other statistics books aimed at beginners but this was the only book which I clearly understood. * Julie Carter, student at Anglia Ruskin University *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Why am I reading this book?2: Getting to grips with the basics3: Describing a single sample4: Inferring and estimating5: Choosing the right test and graph6: Overview of null hypothesis significance testing7: Tests on frequencies8: Tests of difference: two unrelated samples9: Tests of difference: two related samples10: Tests of difference: more than two samples11: Tests of relationship: regression12: Tests of relationship: correlation13: Introducing the generalized linear model: general linear model14: More on the generalized linear model: logistic and loglinear modelsAppendix I How to enter data into SPSSAppendix II Statistical tables of critical valuesAppendix III Summary guidance on reporting statistical resultsAppendix IV Statistics and experimental designRelated Titles","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732791079255,"sku":"9780198807483","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198807483.jpg?v=1719998414"},{"product_id":"data-handling-and-analysis-9780198812210","title":"Data Handling and Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBiomedical scientists are the foundation of modern healthcare, from cancer screening to diagnosing HIV, from blood transfusion for surgery to food poisoning and infection control. Without biomedical scientists, the diagnosis of disease, the evaluation of the effectiveness of treatment, and research into the causes and cures of disease would not be possible.The Fundamentals of Biomedical Science series has been written to reflect the challenges of practicing biomedical science today. It draws together essential basic science with insights into laboratory practice to show how an understanding of the biology of disease is coupled to the analytical approaches that lead to diagnosis. Assuming only a minimum of prior knowledge, the series reviews the full range of disciplines to which a Biomedical Scientist may be exposed - from microbiology to cytopathology to transfusion science. Data Handling and Analysis is the most relevant and useful statistics and data analysis text for biomedical sci\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eA gem of a find! For once it makes statistics understandable and it also clearly explains research and professional practice. Few books cover this range of topics. It is ideal for students completing research and also introduces student to good clinical practice. The statistical sections are very well explained indeed and simplify complicated concepts in a very readable manner. * Ruth Shiner, University of Wolverhampton *\u003cbr\u003eProvides clear explanations of basic statistical and experimental design concepts which are placed into a practical context that is highly relevant for BMS students * Lewis Bingle, University of Sunderland *\u003cbr\u003eAn easy-to-read book that explains how to use a range of statistical tests that can be applied to data * Amreen Bashir, Aston University *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1: Information in biomedical science 2: Handling quantities: mass, volume, and concentration 3: Obtaining and verifying data 4: Presenting data in graphical form 5: Quality, audit, and good laboratory practice 6: Research 1: Setting the scene 7: Research 2: The analysis of modest data sets 8: Research 3: Large data sets 9: Communication","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732792324439,"sku":"9780198812210","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198812210.jpg?v=1719998421"},{"product_id":"southwoods-ecological-methods-9780198862284","title":"Southwoods Ecological Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProvides a handbook of ecological methods and analytical techniques pertinent to the study of animals, with an emphasis on non-microscopic animals in both terrestrial and aquatic environments.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface to Fifth Edition, 2020 Preface to Fourth Edition, 2015 Preface to Third Edition, 1998 1: Introduction to the Study of Animals 2: The Sampling Programme and the Measurement and Description of Dispersion 3: Absolute Population Estimates Using Capture-Recapture Experiments 4: Absolute Population Estimates by Sampling a Unit of Habitat -Air, Plants, Plant Products and Vertebrate Hosts 5: Absolute Population Estimates by Sampling a Unit of Aquatic Habitat 6: Absolute Population Estimates by Sampling a Unit of Soil or Litter Habitat-Extraction Techniques 7: Relative Methods of Population Measurement and the Derivation of Absolute Estimates 8: Estimates of Species Richness and Population Size Based on Signs, Products and Effects 9: Wildlife Population Estimates by Census and Distance Measuring Techniques 10: Observational and Experimental Methods to Estimate Natality, Mortality, Movement and Dispersal 11: The Construction, Description and Analysis of Age-Specific Life-Tables 12: Age-Grouping, Time-Specific Life-Tables and Predictive Population 13: Species Richness, Diversity and Packing 14: The Estimation of Productivity and the Construction of Energy Budgets 15: Techniques for the Study of Long-Term Dynamics-Analysing Time Series 16: Studies at Large Spatial Scales, Citizen Science and the Classification of Habitats","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732819128663,"sku":"9780198862284","price":50.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198862284.jpg?v=1719998534"},{"product_id":"the-chemistry-maths-book-9780199205356","title":"The Chemistry Maths Book","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe Chemistry Maths Book provides a complete course companion suitable for students at all levels. All the most useful and important topics are covered, with numerous examples of applications in chemistry and the physical sciences. Taking a clear, straightforward approach, the book develops ideas in a logical, coherent way, allowing students progressively to build a thorough working understanding of the subject.Topics are organized into three parts: algebra, calculus, differential equations, and expansions in series; vectors, determinants and matrices; and numerical analysis and statistics. The extensive use of examples illustrates every important concept and method in the text, and are used to demonstrate applications of the mathematics in chemistry and several basic concepts in physics. The exercises at the end of each chapter, are an essential element of the development of the subject, and have been designed to give students a working understanding of the material in the text.Online Resources:The online resources feature the following: - Figures from the book in electronic format, ready to download- Full worked solutions to all end of chapter exercises\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eReview from previous edition It seems well suited both for its stated purpose and as a \"brush-up\" book for undergraduates, graduate students, and others. The mathematics are carried out briskly and with very little dressing ... there is much material to cover here and it works well through Steiner's particularly lucid presentation. The notation is standard and clear ... I am impressed with this book, I am sure that it will remain open on my desk and will become well worn in short order. * C. Michael McCallum, University of the Pacific, Journal of Chemical Education, Vol. 74 No. 12 December 1997 *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Numbers, variables and units ; 2. Algebraic functions ; 3. Transcendental functions ; 4. Differentiation ; 5. Integration ; 6. Methods of integration ; 7. Sequences and series ; 8. Complex numbers ; 9. Functions of several variables ; 10. Functions in 3 dimensions ; 11. First-order differential equations ; 12. Second-order differential equations. Constant coefficients ; 13. Second-order differential equations. Some special functions ; 14. Partial differential equations ; 15. Orthogonal expansions. Fourier analysis ; 16. Vectors ; 17. Determinants ; 18. Matrices and linear transformations ; 19. The matrix eigenvalue problem ; 20. Numerical methods ; 21. Probability and statistics","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732834267479,"sku":"9780199205356","price":50.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199205356.jpg?v=1719998598"},{"product_id":"core-maths-for-the-biosciences-9780199216345","title":"Core Maths for the Biosciences","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCore Maths for the Biosciences introduces the range of mathematical concepts that bioscience students need to master during thier studies. Starting from fundamental concepts, it blends clear explanations and biological examples throughout as it equips the reader with the full range of mathematical tools required by biologists today.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eExactly the sort of thing that will be helpful in showing those with biological problems how mathematics can be very useful - and that what is really important is maintaining an intuitive understanding between the mathematics - which is essentially no more, but no less, than a way of thinking very precisely - and the actual phenomena they are dealing with...Very fine indeed. * Professor Lord May of Oxford, Department of Zoology, University of Oxford *\u003cbr\u003eFantastic. Easy to understand, interactive, biologically relevant and dictated in a way that seemed as though you are almost having a conversation with the author. * James Sleigh, Student, University of Oxford *\u003cbr\u003eCoherent and clear. The best I have seen this kind of material treated. * Stephen Hubbard, University of Dundee *\u003cbr\u003eThis book is by far the best of its kind, a spectacular diamond in the rough. * Helen Smith, student, University of Salford *\u003cbr\u003eThe interactive spreadsheets are a work of genius. * Stuart Fisk, student, University of Essex *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART 1: ARITHMETIC, ALGEBRA \u0026amp; FUNCTIONS; PART 2: CALCULUS AND DIFFERENTIAL EQUATIONS","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732835774807,"sku":"9780199216345","price":50.34,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199216345.jpg?v=1719998603"},{"product_id":"mathematical-techniques-an-introduction-for-the-engineering-physical-and-mathematical-sciences-9780199282012","title":"Mathematical Techniques An Introduction for the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMathematical Techniques provides a complete course in mathematics, covering all the essential topics with which a physical sciences or engineering student should be familiar. It introduces and builds on concepts in a progressive, carefully-layered way, and features over 2000 end of chapter problems, plus additional self-check questions.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eReview from previous edition This textbook offers an accessible and comprehensive grounding in many of the mathematical techniques required in the early stages of an engineering or science degree and also for the routine methods needed by first and second year mathematics students. * Engineering Designer March\/April 2003 *\u003cbr\u003eThere are also significant changes in content in the opening chapter, where the foundation material has been expanded usefully. The authors do not attempt to dodge theoretical hurdles. They are careful to explain many of the less intuitive properties of functions and to highlight generalisations without becoming over abstract. * Times Higher Education Supplement, November 2002 *\u003cbr\u003eThoroughly recommended. * Zentralblatt MATH, 993:2002 *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART 1. ELEMENTARY METHODS, DIFFERENTIATION, COMPLEX NUMBERS; PART 2. MATRIX AND VECTOR ALGEBRA; PART 3. INTEGRATION AND DIFFERENTIAL EQUATIONS; PART 4. TRANSFORMS AND FOURIER SERIES; PART 5. MULTIVARIABLE CALCULUS; PART 6. DISCRETE MATHEMATICS; PART 7. PROBABILITY AND STATISTICS; PART 8. PROJECTS; SELF-TESTS: SELECTED ANSWERS; ANSWERS TO SELECTED PROBLEMS; APPENDICES; FURTHER READING; INDEX","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732839969111,"sku":"9780199282012","price":60.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199282012.jpg?v=1719998620"},{"product_id":"linear-algebra-9780199654444","title":"Linear Algebra","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLinear algebra is a fundamental area of mathematics, and is arguably the most powerful mathematical tool ever developed. It is a core topic of study within fields as diverse as: business, economics, engineering, physics, computer science, ecology, sociology, demography and genetics. For an example of linear algebra at work, one needs to look no further than the Google search engine, which relies upon linear algebra to rank the results of a search with respect to relevance. The strength of the text is in the large number of examples and the step-by-step explanation of each topic as it is introduced. It is compiled in a way that allows distance learning, with explicit solutions to set problems freely available online. The miscellaneous exercises at the end of each chapter comprise questions from past exam papers from various universities, helping to reinforce the reader''s confidence. Also included, generally at the beginning of sections, are short historical biographies of the leading p\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis book gives an introduction to linear algebra for students with limited mathematical preparation. ... The steady pace of the book is so gentle that no student need be left behind. * Peter Macgregor, Mathematical Gazette *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Linear Equations and Matrices ; 2. Euclidean Space ; 3. General Vector Spaces ; 4. Inner Product Spaces ; 5. Linear Transformation ; 6. Determinants ; 7. Eigenvalues and Eigenvectors","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732876439895,"sku":"9780199654444","price":32.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199654444.jpg?v=1719998775"},{"product_id":"mathematical-physics-chicago-lectures-in-physics-9780226288628","title":"Mathematical Physics Chicago Lectures in Physics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMathematical Physics is an introduction to such basic mathematical structures as groups, vector spaces, topological spaces, measure spaces, and Hilbert space. Geroch uses category theory to emphasize both the interrelationships among different structures and the unity of mathematics.","brand":"The University of Chicago Press","offers":[{"title":"Default Title","offer_id":48732904751447,"sku":"9780226288628","price":34.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780226288628.jpg?v=1719998891"},{"product_id":"calculations-for-a-level-physics-9780748767489","title":"Calculations for A Level Physics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA revised edition of the best-selling, most widely used and respected physics calculations book.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eBasic ideas; mechanics; matter; oscillations and waves; geometrical optics; heat; electricity and magnetism; atomic and nuclear physics; calculations involving graphs; special topics; further revision questions.","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48736544555351,"sku":"9780748767489","price":48.68,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780748767489.jpg?v=1723810715"},{"product_id":"the-design-and-statistical-analysis-of-animal-experiments-9781107690943","title":"The Design and Statistical Analysis of Animal","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis is the first book to provide life scientists with a practical guide to using experimental design and statistics when running animal experiments. The chapters cover a range of design types and analysis techniques employed by practitioners, using non-mathematical terms and drawing on real-life examples.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'At last, a readable statistics book focusing solely on preclinical experimental designs, data and its analysis that should form part of an in-vivo scientist's personal library. The author's unique insight into the statistical needs of preclinical scientists has allowed them to compile a non-technical guide that can facilitate sound experimental design, meaningful data analysis and appropriate scientific conclusions. I would also encourage all readers to download and explore 'InVivoStat', a powerful software package that both my group and I use on a daily basis.' Darrel J. Pemberton, Janssen Research and Development\u003cbr\u003e'This book provides an indispensable reference for any in-vivo scientist. It addresses common pitfalls in animal experiments and provides tangible advice to address sources of bias, thus increasing the robustness of the data. … The text links experimental design and statistical analysis in a practical way, easily accessible without any prior statistical knowledge. The statistical concepts are described in plain English, avoiding overuse of mathematical formulas and illustrated with numerous examples relevant to biomedical scientists. … This book will help scientists improve the design of animal experiments and give them the confidence to use more complex designs, enabling more efficient use of animals and reducing the number of experimental animals needed overall.' Nathalie Percie du Sert, National Centre for the Replacement, Refinement and Reduction of Animals in Research\u003cbr\u003e'This book will transform the way biomedical scientists plan their work and interpret their results. Although the subject matter covers complex points, it is easy to read and packed with relevant examples. There are two particularly striking features. First, at no point do the authors resort to mathematical equations as a substitute for explaining the concepts. Secondly, they explain why the choice of experimental design is so important, why the design affects the statistical analysis and how to ensure the choice of the most appropriate statistical test. The final section describes how to use InvivoStat (a software package, assembled by the authors), which enables researchers to put into practice all the points covered in this book. This is an invaluable combination of resources that should be within easy reach of anyone carrying out experiments in the biomedical sciences, especially if their work involves using live animals.' Clare Stanford, University College London\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Acknowledgements; 1. Introduction; 2. Statistical concepts; 3. Experimental design; 4. Randomisation; 5. Statistical analysis; 6. Analysis using InVivoStat; 7. Conclusion; Glossary; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738277032279,"sku":"9781107690943","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107690943.jpg?v=1723811881"},{"product_id":"the-joy-of-abstraction-9781108477222","title":"The Joy of Abstraction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eJourney through the world of abstract mathematics into category theory with popular science author Eugenia Cheng. Featuring humanizing examples and demystification of mathematical thought processes, this book is for fans of How to Bake Pi who want to dig deeper into mathematical concepts and build their mathematical background.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book is an educational tour de force that presents mathematical thinking as a right-brained activity. Most 'left brain\/right brain' education-talk is at best a crude metaphor; but by putting the main focus on the process of (mathematical) abstraction, Eugenia Cheng supplies the reader (whatever their 'brain-type') with the mental tools to make that distinction precise and potentially useful. The book takes the reader along in small steps; but make no mistake, this is a major intellectual journey. Starting not with numbers, but everyday experiences, it develops what is regarded as a very advanced branch of abstract mathematics (category theory, though Cheng really uses this as a proxy for mathematical thinking generally). This is not watered-down math; it's the real thing. And it challenges the reader to think-deeply at times. We 'left-brainers' can learn plenty from it too.' Keith Devlin, Stanford University (Emeritus), author of The Joy of Sets\u003cbr\u003e'Eugenia Cheng loves mathematics—not the ordinary sort that most people encounter, but the most abstract sort that she calls 'the mathematics of mathematics.' And in this lovely excursion through her abstract world of Category Theory, she aims to give those who are willing to join her a glimpse of that world. The journey will change how they view mathematics. Cheng is a brilliant writer, with prose that feels like poetry. Her contagious enthusiasm makes her the perfect guide.' John Ewing, President, Math for America\u003cbr\u003e'Eugenia Cheng's singular contribution is in making abstract mathematics relevant to all through her great ingenuity in developing novel connections between logic and life. Her latest book, The Joy of Abstraction, provides a long awaited fully rigorous yet gentle introduction to the 'mathematics of mathematics,' allowing anyone to experience the joy of learning to think categorically.' Emily Riehl, Johns Hopkins University, author of Category Theory in Context\u003cbr\u003e'Archimedes is quoted as having said once: 'Mathematics reveals its secrets only to those who approach it with pure love, for its own beauty.' In this fascinating book, Eugenia Cheng approaches the abstract mathematical area of Category Theory with pure love, to reveal its beauty to anybody interested in learning something about contemporary mathematics.' Mario Livio, astrophysicist, author of The Golden Ratio and Brilliant Blunders\u003cbr\u003e'Eugenia Cheng's latest book will appeal to a remarkably broad and diverse audience, from non-mathematicians who would like to get a sense of what mathematics is really about, to experienced mathematicians who are not category theorists but would like a basic understanding of category theory. Speaking as one of the latter, I found it a real pleasure to be able to read the book without constantly having to stop and puzzle over the details. I have learnt a lot from it already, including what the famous Yoneda lemma is all about, and I look forward to learning more from it in the future.' Sir Timothy Gowers, Collège de France, Fields Medalist, main editor of The Princeton Companion to Mathematics\u003cbr\u003e'At last: a book that makes category theory as simple as it really is. Cheng explains the subject in a clear and friendly way, in detail, not relying on material that only mathematics majors learn. Category theory – indeed, mathematics as a whole – has been waiting for a book like this.' John Baez, University of California, Riverside\u003cbr\u003e'Many people speak derisively of category theory as the most abstract area of mathematics, but Eugenia Cheng succeeds in redeeming the word 'abstract'. This book is loquacious, conversational, and inviting. Reading this book convinced me I could teach category theory as an introductory course, and that is a real marvel, since it is a subject most people leave for experts.' Francis Su, Harvey Mudd College, author of Mathematics for Human Flourishing\u003cbr\u003e'Finally, a book about category theory that doesn't assume you already know category theory! In this inviting but rigorous introduction to what she calls 'the mathematics of mathematics', Eugenia Cheng brings the subject to us with insight, wit, and a point of view. Her story of finding joy-and advantage-in abstraction will inspire you to find it, too.' Patrick Honner, award-winning high school math teacher, columnist for Quanta Magazine, author of Painless Statistics\u003cbr\u003e'This higher category theory is the mathematics of the twenty-first century (at least my corner of it). If you'd like a taste of it, I recommend Dr. Cheng's book. The first half is an accessible and thought-provoking insight into categorical thinking. The second half climbs into the rarified air of theoretic math, but it is worth a read to get a feel for what some parts of modern mathematics look like.' Jonathan Kujawa, 3 Quarks Daily\u003cbr\u003e'… a successful addition to the literature that I am sure students will use in the future and I would be happy to recommend.' Constanze Roitzheim, Mathematische Semesterberichte\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePrologue; Part I. Building Up to Categories: 1. Categories: the idea; 2. Abstraction; 3. Patterns; 4. Context; 5. Relationships; 6. Formalism; 7. Equivalence relations; 8. Categories: the definition; Interlude: A Tour of Math: 9. Examples we've already seen, secretly; 10. Ordered sets; 11. Small mathematical structures; 12. Sets and functions; 13. Large worlds of mathematical structures; Part II. Doing Category Theory: 14. Isomorphisms; 15. Monics and epics; 16. Universal properties; 17. Duality; 18. Products and coproducts; 19. Pullbacks and pushouts; 20. Functors; 21. Categories of categories; 22. Natural transformations; 23. Yoneda; 24. Higher dimensions; 25. Epilogue: thinking categorically; Appendices: A. Background on alphabets; B. Background on basic logic; C. Background on set theory; D. Background on topological spaces; Glossary; Further reading; Acknowledgements; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738304360791,"sku":"9781108477222","price":19.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108477222.jpg?v=1723811906"},{"product_id":"maths-for-chemists-9781849733595","title":"Maths for Chemists","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe two volumes of Maths for Chemists provide an excellent resource for all undergraduate chemistry students but are particularly focussed on the needs of students who may not have studied mathematics beyond GCSE level (or equivalent). The texts are introductory in nature and adopt a sympathetic approach for students who need support and understanding in working with the diverse mathematical tools required in a typical chemistry degree course. The early chapters of Maths for Chemists Volume I: Numbers, Functions and Calculus provide a succinct introduction to the important mathematical skills of algebraic manipulation, trigonometry, numbers, functions, units and the general grammar of maths. Later chapters build on these basic mathematical principles as a foundation for the development of differential and integral calculus. In spite of the introductory nature of this volume, some of the more important mathematical tools required in quantum chemistry are deliberately included, through a gradual introduction to, and development of, the concept of the eigenvalue problem. Ideal for the needs of undergraduate chemistry students, Tutorial Chemistry Texts is a major series consisting of short, single topic or modular texts concentrating on the fundamental areas of chemistry taught in undergraduate science courses. Each book provides a concise account of the basic principles underlying a given subject, embodying an independent-learning philosophy and including worked examples.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eA useful addition to the resources available for teaching mathematics to chemists.\u003cbr\u003e\"\"\"... Undergraduates in biochemistry and all branches of chemistry, particularly students with a limited background in maths, will find this book essential. \"\"\"\u003cbr\u003e\"\"\"... The importance of mathematics in chemistry can not be under estimated; books aiming to show the many applications of the subject are always very welcome. \"\"\"\u003cbr\u003eThe mathematical ability of chemistry undergraduates continues to be an issue for many departments, so this new edition is a timely update to the resources available for both staff and students.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eNumbers and Algebra; Functions and Equations: Their Form and Use; Limits; Differentiation; Differentials; Integration; Differential Equations; Subject Index","brand":"Royal Society of Chemistry","offers":[{"title":"Default Title","offer_id":48742296518999,"sku":"9781849733595","price":23.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781849733595.jpg?v=1720060819"},{"product_id":"an-introductory-path-to-quantum-theory-using-mathematics-to-understand-the-ideas-of-physics-9783030407698","title":"An Introductory Path to Quantum Theory: Using","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eSince the 17th century, physical theories have been expressed in the language of mathematical equations. This introduction to quantum theory uses that language to enable the reader to comprehend the notoriously non-intuitive ideas of quantum physics. \u003c\/p\u003e\u003cp\u003eThe mathematical knowledge needed for using this book comes from standard undergraduate mathematics courses and is described in detail in the section Prerequisites. This text is especially aimed at advanced undergraduate and graduate students of mathematics, computer science, engineering and chemistry among other disciplines, provided they have the math background even though lacking preparation in physics. In fact, no previous formal study of physics is assumed.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The target audience is ‘advanced undergraduate mathematics students who had no or only very little prior knowledge of physics’. It would indeed be a rare variety of mathematics advanced undergraduates who would fit this bill. … an interesting supplement for students with a mathematical bent.” (Amitava Raychaudhuri, zbMATH 1458.81002, 2021)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction to this Path.- Viewpoint.- Neither Particle nor Wave.- Schrödinger's Equation.- Operators and Canonical Quantization.- The Harmonic Oscillator.- Interpreting: Mathematics.- Interpreting: Physics.- The Language of Hilbert Space.- Interpreting: Measurement.- The Hydrogen Atom.- Angular Momentum.- The Rotation Group SO(3).- Spin and SU(2).- Bosons and Fermions.- Classical and Quantum Probability.- The Heisenberg Picture.- Uncertainty (Optional).- Speaking of Quantum Theory (Optional).- Complementarity (Optional).- Axioms (Optional).- And Gravity?.- Measure Theory: A Crash Course.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743035306327,"sku":"9783030407698","price":49.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"hands-on-signal-analysis-with-python-an-introduction-9783030579050","title":"Hands-on Signal Analysis with Python: An","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides the tools for analyzing data in Python: different types of filters are introduced and explained, such as FIR-, IIR- and morphological filters, as well as their application to one- and two-dimensional data. The required mathematics are kept to a minimum, and numerous examples and working Python programs are included for a quick start. The goal of the book is to enable also novice users to choose appropriate methods and to complete real-world tasks such as differentiation, integration, and smoothing of time series, or simple edge detection in images. An introductory section provides help and tips for getting Python installed and configured on your computer. More advanced chapters provide a practical introduction to the Fourier transform and its applications such as sound processing, as well as to the solution of equations of motion with the Laplace transform. A brief excursion into machine learning shows the powerful tools that are available with Python. This book also provides tips for an efficient programming work flow: from the use of a debugger for finding mistakes, code-versioning with git to avoid the loss of working programs, to the construction of graphical user interfaces (GUIs) for the visualization of data. Working, well-documented Python solutions are included for all exercises, and IPython\/Jupyter notebooks provide additional help to get people started and outlooks for the interested reader.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Python.- Data Input.- Data Display.- Data Filtering.- Event- and Feature-Finding.- Statistics.- Parameter Fitting.- Spectral Signal Analysis.- Solving Equations of Motion.- Machine Learning.- Useful Programming Tools.\u003cp\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743041040727,"sku":"9783030579050","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030579050.jpg?v=1720063851"},{"product_id":"machine-learning-for-engineers-using-data-to-solve-problems-for-physical-systems-9783030703905","title":"Machine Learning for Engineers: Using data to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAll engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,  demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart I Fundamentals\u003c\/p\u003e  \u003cp\u003e1.0  Introduction\u003c\/p\u003e  \u003cp\u003e1.1.   Where machine learning can help engineers\u003c\/p\u003e  1.2.   Where machine learning \u003ci\u003ecannot\u003c\/i\u003e help engineers\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e1.3.   Machine learning to correct idealized models\u003c\/p\u003e  \u003cp\u003e2.      The Landscape of machine learning\u003c\/p\u003e  \u003cp\u003e2.1.   Supervised learning\u003c\/p\u003e  \u003cp\u003e2.1.1.      Regression\u003c\/p\u003e  \u003cp\u003e2.1.2.      Classification\u003c\/p\u003e  \u003cp\u003e2.1.3.      Time series\u003c\/p\u003e  \u003cp\u003e2.1.4.      Reinforcement\u003c\/p\u003e  2.2.   Unsupervised Learning\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e2.3.   Optimization\u003c\/p\u003e  \u003cp\u003e2.4.   Bayesian statistics\u003c\/p\u003e  2.5.   Cross-validation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e3.      Linear Models\u003c\/p\u003e  \u003cp\u003e3.1.   Linear regression\u003c\/p\u003e  \u003cp\u003e3.2.   Logistic regression\u003c\/p\u003e  \u003cp\u003e3.3.   Regularized regression\u003c\/p\u003e  \u003cp\u003e3.4.   Case Study: Determining physical laws using regularized regression\u003c\/p\u003e  \u003cp\u003e4.      Tree-Based Models\u003c\/p\u003e  \u003cp\u003e4.1.   Decision Trees\u003c\/p\u003e  4.2.   Random Forests\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e4.3.   BART\u003c\/p\u003e  \u003cp\u003e4.4.   Case Study: Modeling an experiment using random forest models\u003c\/p\u003e  \u003cp\u003e5.      Clustering data\u003c\/p\u003e  \u003cp\u003e5.1.   Singular value decomposition\u003c\/p\u003e  \u003cp\u003e5.2.   Case Study: SVD to standardize several time series\u003c\/p\u003e  \u003cp\u003e5.3.   K-means\u003c\/p\u003e  \u003cp\u003e5.4.   K-nearest neighbors\u003c\/p\u003e  \u003cp\u003e5.5.   t-SNE\u003c\/p\u003e  \u003cp\u003e5.6.   Case Study: The reflectance spectrum of different foliage\u003c\/p\u003e  \u003cp\u003ePart II Deep Neural Networks\u003c\/p\u003e  \u003cp\u003e6.      Feed-Forward Neural Networks\u003c\/p\u003e  6.1.   Neurons\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.2.   Dropout\u003c\/p\u003e  \u003cp\u003e6.3.   Backpropagation\u003c\/p\u003e  6.4.   Initialization\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.5.   Regression\u003c\/p\u003e  \u003cp\u003e6.6.   Classification\u003c\/p\u003e  6.7.   Case Study: The strength of concrete as a function of age and ingredients\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e7.      Convolutional Neural Networks\u003c\/p\u003e  \u003cp\u003e7.1.   Convolutions\u003c\/p\u003e  \u003cp\u003e7.2.   Pooling\u003c\/p\u003e  \u003cp\u003e7.3.   Residual networks\u003c\/p\u003e  \u003cp\u003e7.4.   Case Study: Finding volcanoes on Venus\u003c\/p\u003e  \u003cp\u003e8.      Recurrent neural networks for time series data\u003c\/p\u003e  \u003cp\u003e8.1.   Basic Recurrent neural networks\u003c\/p\u003e  8.2.   Long-term, Short-Term memory\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e8.3.   Attention networks\u003c\/p\u003e  \u003cp\u003e8.4.   Case Study: Predicting future system performance\u003c\/p\u003e  Part III Advanced Topics in Machine Learning\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.      Unsupervised Learning with Neural Networks\u003c\/p\u003e  \u003cp\u003e9.1.   Auto-encoders\u003c\/p\u003e  9.2.   Boltzmann machines\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.3.   Case study: Optimization using Inverse models\u003c\/p\u003e  \u003cp\u003e10.  Reinforcement learning\u003c\/p\u003e  \u003cp\u003e10.1.                    Case study: controlling a mechanical gantry\u003c\/p\u003e  \u003cp\u003e11.  Transfer learning\u003c\/p\u003e  11.1.                    Case study: Transfer learning a simulation emulator for experimental measurements\u003cp\u003e\u003c\/p\u003e  \u003cp\u003ePart IV Appendices\u003c\/p\u003e  \u003cp\u003eA.      SciKit-Learn\u003c\/p\u003e  \u003cp\u003eB.      Tensorflow\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743044677975,"sku":"9783030703905","price":49.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"machine-learning-for-engineers-using-data-to-solve-problems-for-physical-systems-9783030703875","title":"Machine Learning for Engineers: Using data to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAll engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,  demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart I Fundamentals\u003c\/p\u003e  \u003cp\u003e1.0  Introduction\u003c\/p\u003e  \u003cp\u003e1.1.   Where machine learning can help engineers\u003c\/p\u003e  1.2.   Where machine learning \u003ci\u003ecannot\u003c\/i\u003e help engineers\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e1.3.   Machine learning to correct idealized models\u003c\/p\u003e  \u003cp\u003e2.      The Landscape of machine learning\u003c\/p\u003e  \u003cp\u003e2.1.   Supervised learning\u003c\/p\u003e  \u003cp\u003e2.1.1.      Regression\u003c\/p\u003e  \u003cp\u003e2.1.2.      Classification\u003c\/p\u003e  \u003cp\u003e2.1.3.      Time series\u003c\/p\u003e  \u003cp\u003e2.1.4.      Reinforcement\u003c\/p\u003e  2.2.   Unsupervised Learning\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e2.3.   Optimization\u003c\/p\u003e  \u003cp\u003e2.4.   Bayesian statistics\u003c\/p\u003e  2.5.   Cross-validation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e3.      Linear Models\u003c\/p\u003e  \u003cp\u003e3.1.   Linear regression\u003c\/p\u003e  \u003cp\u003e3.2.   Logistic regression\u003c\/p\u003e  \u003cp\u003e3.3.   Regularized regression\u003c\/p\u003e  \u003cp\u003e3.4.   Case Study: Determining physical laws using regularized regression\u003c\/p\u003e  \u003cp\u003e4.      Tree-Based Models\u003c\/p\u003e  \u003cp\u003e4.1.   Decision Trees\u003c\/p\u003e  4.2.   Random Forests\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e4.3.   BART\u003c\/p\u003e  \u003cp\u003e4.4.   Case Study: Modeling an experiment using random forest models\u003c\/p\u003e  \u003cp\u003e5.      Clustering data\u003c\/p\u003e  \u003cp\u003e5.1.   Singular value decomposition\u003c\/p\u003e  \u003cp\u003e5.2.   Case Study: SVD to standardize several time series\u003c\/p\u003e  \u003cp\u003e5.3.   K-means\u003c\/p\u003e  \u003cp\u003e5.4.   K-nearest neighbors\u003c\/p\u003e  \u003cp\u003e5.5.   t-SNE\u003c\/p\u003e  \u003cp\u003e5.6.   Case Study: The reflectance spectrum of different foliage\u003c\/p\u003e  \u003cp\u003ePart II Deep Neural Networks\u003c\/p\u003e  \u003cp\u003e6.      Feed-Forward Neural Networks\u003c\/p\u003e  6.1.   Neurons\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.2.   Dropout\u003c\/p\u003e  \u003cp\u003e6.3.   Backpropagation\u003c\/p\u003e  6.4.   Initialization\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.5.   Regression\u003c\/p\u003e  \u003cp\u003e6.6.   Classification\u003c\/p\u003e  6.7.   Case Study: The strength of concrete as a function of age and ingredients\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e7.      Convolutional Neural Networks\u003c\/p\u003e  \u003cp\u003e7.1.   Convolutions\u003c\/p\u003e  \u003cp\u003e7.2.   Pooling\u003c\/p\u003e  \u003cp\u003e7.3.   Residual networks\u003c\/p\u003e  \u003cp\u003e7.4.   Case Study: Finding volcanoes on Venus\u003c\/p\u003e  \u003cp\u003e8.      Recurrent neural networks for time series data\u003c\/p\u003e  \u003cp\u003e8.1.   Basic Recurrent neural networks\u003c\/p\u003e  8.2.   Long-term, Short-Term memory\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e8.3.   Attention networks\u003c\/p\u003e  \u003cp\u003e8.4.   Case Study: Predicting future system performance\u003c\/p\u003e  Part III Advanced Topics in Machine Learning\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.      Unsupervised Learning with Neural Networks\u003c\/p\u003e  \u003cp\u003e9.1.   Auto-encoders\u003c\/p\u003e  9.2.   Boltzmann machines\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.3.   Case study: Optimization using Inverse models\u003c\/p\u003e  \u003cp\u003e10.  Reinforcement learning\u003c\/p\u003e  \u003cp\u003e10.1.                    Case study: controlling a mechanical gantry\u003c\/p\u003e  \u003cp\u003e11.  Transfer learning\u003c\/p\u003e  11.1.                    Case study: Transfer learning a simulation emulator for experimental measurements\u003cp\u003e\u003c\/p\u003e  \u003cp\u003ePart IV Appendices\u003c\/p\u003e  \u003cp\u003eA.      SciKit-Learn\u003c\/p\u003e  \u003cp\u003eB.      Tensorflow\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743046152535,"sku":"9783030703875","price":64.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"novel-mathematics-inspired-by-industrial-challenges-9783030961756","title":"Novel Mathematics Inspired by Industrial","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis contributed volume convenes a rich selection of works with a focus on innovative mathematical methods with applications in real-world, industrial problems. Studies included in this book are all motivated by a relevant industrial challenge, and demonstrate that mathematics for industry can be extremely rewarding, leading to new mathematical methods and sometimes even to entirely new fields within mathematics.\u003cbr\u003eThe book is organized into two parts: Computational Sciences and Engineering, and Data Analysis and Finance. In every chapter, readers will find a brief description of why such work fits into this volume; an explanation on which industrial challenges have been instrumental for their inspiration; and which methods have been developed as a result. All these contribute to a greater unity of the text, benefiting not only practitioners and professionals seeking information on novel techniques but also graduate students in applied mathematics, engineering, and related fields.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I:  Computational Science and Engineering.- Multirate Schemes — An Answer of Numerical Analysis to a Demand from Applications.- Electronic Circuit Simulation and the Development of New Krylov-Subspace Methods.- Modular time integration of coupled problems in system dynamics.- Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems.- Fast Numerical Methods to Compute Periodic Solutions of Electromagnetic Models.- Challenges in the Simulation of Radio Frequency Circuits.- An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics.- From rotating fluid masses and Ziegler’s paradox to Pontryagin- and Krein spaces and bifurcation theory.- Part II: Data Analysis and finance.- Topological Data Analysis.- Prediction Models with Functional Data for Variables related with Energy Production.- Quantization Methods for Stochastic Differential Equations.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743061586263,"sku":"9783030961756","price":63.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030961756.jpg?v=1720063941"},{"product_id":"an-introduction-to-mathematical-statistics-9789462985100","title":"An Introduction to Mathematical Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In 'An Introduction to Mathematical Statistics' the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. \u003cbr\u003e\u003cbr\u003e The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model.Each chapter ends with a case study in which the described statistical methods are applied. \u003cbr\u003e\u003cbr\u003e This book assumes a basic knowledge of probability theory, calculus, and linear algebra.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1[-]1.1. WhatIsStatistics? . . . . . . . . . . . . . . . . . . . . . 1[-]1.2. StatisticalModels . . . . . . . . . . . . . . . . . . . . . 2[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 12[-]Application: Cox Regression . . . . . . . . . . . . . . . . . 15[-]2. DescriptiveStatistics . . . . . . . . . . . . . . . . . . . . . . 21[-]2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 21[-]2.2. UnivariateSamples . . . . . . . . . . . . . . . . . . . . . 21[-]2.3. Correlation . . . . . . . . . . . . . . . . . . . . . . . . 32[-]2.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 38[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 39[-]Application: Benford's Law . . . . . . . . . . . . . . . . . 41[-]3. Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . 45[-]3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 45[-]3.2. MeanSquareError . . . . . . . . . . . . . . . . . . . . . 46[-]3.3. Maximum Likelihood Estimators . . . . . . . . . . . . . . . 54[-]3.4. MethodofMomentsEstimators . . . . . . . . . . . . . . . . 72[-]3.5. BayesEstimators . . . . . . . . . . . . . . . . . . . . . . 75[-]3.6. M-Estimators . . . . . . . . . . . . . . . . . . . . . . . 88[-]3.7. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 93[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 94[-]Application: Twin Studies . . . . . . . . . . . . . . . . . 100[-]4. Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . 105[-]4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 105[-]4.2. Null Hypothesis and Alternative Hypothesis . . . . . . . . . . 105[-]4.3. SampleSizeandCriticalRegion . . . . . . . . . . . . . . 107[-]4.4. Testing with p-Values . . . . . . . . . . . . . . . . . . . 121[-]4.5. StatisticalSignificance . . . . . . . . . . . . . . . . . . 126[-]4.6. SomeStandardTests . . . . . . . . . . . . . . . . . . . 127[-]4.7. Likelihood Ratio Tests . . . . . . . . . . . . . . . . . . 143[-]4.8. ScoreandWaldTests . . . . . . . . . . . . . . . . . . . 150[-]4.9. Multiple Testing . . . . . . . . . . . . . . . . . . . . . 153[-]4.10. Summary . . . . . . . . . . . . . . . . . . . . . . . . 159[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 160[-]Application: Shares According to Black-Scholes . . . . . . . . 169[-]5. ConfidenceRegions . . . . . . . . . . . . . . . . . . . . . 174[-]5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 174[-]5.2. Interpretation of a Confidence Region . . . . . . . . . . . . 174[-]5.3. PivotsandNear-Pivots . . . . . . . . . . . . . . . . . . 177[-]5.4. Maximum Likelihood Estimators as Near-Pivots . . . . . . . . 181[-]5.5. ConfidenceRegionsandTests . . . . . . . . . . . . . . . 195[-]5.6. Likelihood Ratio Regions . . . . . . . . . . . . . . . . . 198[-]5.7. BayesianConfidenceRegions . . . . . . . . . . . . . . . . 201[-]5.8. Summary . . . . . . . . . . . . . . . . . . . . . . . . 205[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 206[-]Application: The Salk Vaccine . . . . . . . . . . . . . . . 209[-]6. Optimality Theory . . . . . . . . . . . . . . . . . . . . . . 212[-]6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 212[-]6.2. SufficientStatistics . . . . . . . . . . . . . . . . . . . . 212[-]6.3. EstimationTheory . . . . . . . . . . . . . . . . . . . . 219[-]6.4. TestingTheory . . . . . . . . . . . . . . . . . . . . . 231[-]6.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . 245[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 246[-]Application: High Water in Limburg . . . . . . . . . . . . . 250[-]7. RegressionModels . . . . . . . . . . . . . . . . . . . . . . 259[-]7.1. Introduction","brand":"Amsterdam University Press","offers":[{"title":"Default Title","offer_id":48743262323031,"sku":"9789462985100","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789462985100.jpg?v=1720064829"},{"product_id":"thinking-matters-critical-thinking-as-creative-problem-solving-9789811216244","title":"Thinking Matters: Critical Thinking As Creative","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe ancient Roman orator Horace (65 B.C.-8 B.C.) wrote, 'Control your mind or it will control you.' In today's society we are faced with more information, and more complex information, than ever. Faced with making decisions, we can feel overwhelmed and helpless. One way to become less helpless — to gain control over our lives — is to gain control over our own thinking. We can feel helpless when faced with this barrage of information, opinions, data, and conflicting arguments if we lack the skills to quickly grasp and critically evaluate them. This book is designed to impart these kinds of skills.Any course in a university should do more than teach information — in nearly every field, 'facts' become obsolete quickly. The goals of Thinking Matters are to help you: The text is punctuated with exercises or 'personal experiments' to challenge and stimulate your curiosity.  These exercises may take the form of an inventory to be taken, a puzzle to be solved, or some thoughts to ponder.The first module Thinking Matters: Critical Thinking as Creative Problem Solving introduces the student to all the above topics — logic, probability, argument forms and fallacies, ethical reasoning, algorithms, and computational thinking — through logic puzzles and games and mathematical magic tricks.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743276904791,"sku":"9789811216244","price":42.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811216244.jpg?v=1720064884"},{"product_id":"handbook-of-mathematical-science-communication-9789811253065","title":"Handbook Of Mathematical Science Communication","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMathematical science communication, as well as the field of science communication in general, has gained momentum over the last few decades. Mathematical science communication aims to inform the public about contemporary research, enhance factual and methodological knowledge, and foster a greater interest and support for the science of mathematics. This enables the public to apply it to their practical life, and to decision-making on a greater scale. These objectives are met in the various formats and media through which mathematical science communication is brought to the public.The first 13 chapters of the book consist of best-practice examples from the areas of informal math education, museums and exhibitions, and the arts. The final 5 chapters discuss the structural aspects of mathematical science communication and contribute to the basis for its theoretical framework.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743284638039,"sku":"9789811253065","price":112.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811253065.jpg?v=1720064922"},{"product_id":"error-and-the-growth-of-experimental-knowledge-9780226511986","title":"Error and the Growth of Experimental Knowledge","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.","brand":"The University of Chicago Press","offers":[{"title":"Default Title","offer_id":48864235585879,"sku":"9780226511986","price":42.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780226511986.jpg?v=1722271010"},{"product_id":"mathematical-methods-for-physics-and-engineering-9780521679718","title":"Mathematical Methods for Physics and Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis highly acclaimed undergraduate textbook teaches all the mathematics for undergraduate courses in the physical sciences. Containing over 800 exercises, half come with hints and answers and, in a separate manual, complete worked solutions. The remaining exercises are intended for unaided homework; full solutions are available to instructors.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eFrom reviews of previous editions: '…a great scientific textbook. It is a tour de force … to write mathematical sections that are both complete and at an appropriate academic level. The authors have clearly succeeded in this challenge, making this a remarkable pedagogical book … The choice of exercises is excellent and possibly the best feature of the book. In summary, this textbook is a great reference at undergraduate levels, particularly for those who like to teach or learn using lots of examples and exercises.'  R. Botet, European Journal of Physics\u003cbr\u003e'… the book provides scientists who need to use the tool of mathematics for practical purposes with a single, comprehensive book. I recommend this book not only to students in physics and engineering sciences, but also to students in other fields of natural sciences.' P. Steward, Optik\u003cbr\u003e'… suitable as a textbook for undergraduate use … this is a book that in view of its content and its modest softcover price, will find its way on to many bookshelves.' Nigel Steele, The Times Higher Education Supplement\u003cbr\u003e'Riley et al. has clear, thorough and straightforward explanations of the subjects treated. It rigorously adopts a three-stage approach throughout the book: first a heuristic, intuitive introduction, then a formal treatment, and finally one or two examples. This consistent presentation, the layout, and the print quality make the book most attractive … and value for money. It contains a thousand pages, there are plenty of exercises with each chapter.' J. M. Thijssen, European Journal of Physics\u003cbr\u003eThis is a valuable book with great potential use in present-day university physics courses. Furthermore, the book will be useful for graduate too, and researchers will find it useful for looking up material which they have forgotten since their undergraduate days.' J. M. Thijssen, European Journal of Physics\u003cbr\u003e'This textbook is a well-written, modern, comprehensive, and complete collection of topics in mathematical methods ranging from a review of differential and integral calculus to group and representation theory, probability, the calculus of variations, and tensors.' Science Books and Films\u003cbr\u003e'This is a very comprehensive textbook suitable for most students enrolling on undergraduate degree courses in engineering. It contains 31 stand-alone chapters of mathematical methods which enable the students to understand the principles of the basic mathematical techniques and the authors have produced a clear, thorough and straightforward explanation of each subject. … finding a single textbook which covers the engineering student's need throughout their entire course is by no means an easy task. I believe the authors have achieved it … complete fully worked solutions … which I think is a useful asset for both students and lecturers.' Civil Engineering\u003cbr\u003e' ... this highly acclaimed undergraduate textbook is suitable for teaching all the mathematics ever likely to be needed for an undergraduate course in any of the physical sciences. As well as lucid descriptions of all the topics covered and many worked examples, it contains more than 800 exercises.' L'enseignement mathematique\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePrefaces; 1. Preliminary algebra; 2. Preliminary calculus; 3. Complex numbers and hyperbolic functions; 4. Series and limits; 5. Partial differentiation; 6. Multiple integrals; 7. Vector algebra; 8. Matrices and vector spaces; 9. Normal modes; 10. Vector calculus; 11. Line, surface and volume integrals; 12. Fourier series; 13. Integral transforms; 14. First-order ordinary differential equations; 15. Higher-order ordinary differential equations; 16. Series solutions of ordinary differential equations; 17. Eigenfunction methods for differential equations; 18. Special functions; 19. Quantum operators; 20. Partial differential equations: general and particular; 21. Partial differential equations: separation of variables; 22. Calculus of variations; 23. Integral equations; 24. Complex variables; 25. Application of complex variables; 26. Tensors; 27. Numerical methods; 28. Group theory; 29. Representation theory; 30. Probability; 31. Statistics; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48864968278359,"sku":"9780521679718","price":43.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780521679718.jpg?v=1722273372"},{"product_id":"mathematical-ecology-of-populations-and-ecosystems-9781405177955","title":"Mathematical Ecology of Populations and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis is a major new introductory textbook on mathematical ecology bridging the subdisciplines of population ecology and ecosystem ecology. The book is ideal for beginning graduate and advanced undergraduate students, with some background in basic calculus, linear algebra, and basic ecology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Nevertheless, it is an excellent summary which will sweep away the cobwebs from the mind of someone who has learnt this stuff at some time in the past. . . It would be ideal as a text for a course taught in a mathematics department, to convince mathematics students that their skills in differential equations can be applied to ecological problems.\" (Austral Ecology, 2011)\u003cbr\u003e \u003cbr\u003e \"Its best feature a the scientific soundness t hat permeates the whole book, founded on a robust mathematical treatment of most of the arguments.\" (\u003ci\u003eEcoscience\u003c\/i\u003e, June 2010)\"Pastor (Univ. of Minnesota, Duluth) does an admirable job of bridging the gap, providing a work that should quickly become a popular choice for upper-level undergraduate or graduate courses in both disciplines.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, January 2009)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePrologue. \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Preliminaries\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e1. What is Mathematical Ecology and Why Should We Do It?.\u003c\/p\u003e \u003cp\u003e2. Mathematical Toolbox.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Populations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3. Homogeneous Populations: Exponential and Geometric Growth and Decay.\u003c\/p\u003e \u003cp\u003e4. Age- and Stage-structured Linear Models: Relaxing The Assumption Of Population Homogeneity.\u003c\/p\u003e \u003cp\u003e5. Nonlinear Models of Single Populations: The Continuous Time Logistic Model.\u003c\/p\u003e \u003cp\u003e6. Discrete Logistic Growth, Oscillations, and Chaos.\u003c\/p\u003e \u003cp\u003e7. Harvesting and the Logistic Model.\u003c\/p\u003e \u003cp\u003e8. Predators and their Prey.\u003c\/p\u003e \u003cp\u003e9. Competition between Two Species, Mutualism, and Species Invasions.\u003c\/p\u003e \u003cp\u003e10. Multispecies Community and Food Web Models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Ecosystems\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e11. Inorganic Resources, Mass Balance, Resource Uptake, and Resource Use Efficiency.\u003c\/p\u003e \u003cp\u003e12. Litter Return, Nutrient Cycling, and Ecosystem Stability.\u003c\/p\u003e \u003cp\u003e13. Consumer Regulation of Nutrient Cycling.\u003c\/p\u003e \u003cp\u003e14. Stoichiometry and Linked Element Cycles.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Populations and Ecosystems in Space and Time.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15. Transitions between Populations and States in Landscapes.\u003c\/p\u003e \u003cp\u003e16. Diffusion, Advection, the Spread of Populations and Resources, and the Emergence of Spatial Patterns.\u003c\/p\u003e \u003cp\u003eAppendix: MatLab Commands for Equilibrium and Stability Analysis of Multi-compartment Models by Solving the Jacobian and its Eigenvalues.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":48866714485079,"sku":"9781405177955","price":61.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405177955.jpg?v=1722279873"},{"product_id":"ise-principles-of-statistics-for-engineers-and-scientists-9781260570731","title":"ISE Principles of Statistics for Engineers and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAvailable for the first time in McGraw-Hill''s Connect! \u003ci\u003ePrinciples of Statistics for Engineers and Scientists\u003c\/i\u003e emphasizes statistical methods and how they can be applied to problems in science and engineering. The book contains many examples that feature real, contemporary data sets, both to motivate students and to show connections to industry and scientific research. Because statistical analyses are done on computers, the book contains exercises and examples that involve interpreting, as well as generating, computer output. This book may be used effectively with any software package.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Summarizing Univariate Data2 Summarizing Bivariate Data3 Probability4 Commonly Used Distributions5 Point and Interval Estimation for a Single Sample6 Hypothesis Tests for a Single Sample7 Inferences for Two Samples8 Inference in Linear Models9 Factorial Experiments10 Statistical Quality Control","brand":"McGraw-Hill Education","offers":[{"title":"Default Title","offer_id":48885314388311,"sku":"9781260570731","price":53.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781260570731.jpg?v=1722535864"},{"product_id":"statistics-for-engineers-and-scientists-ise-9781266115837","title":"Statistics for Engineers and Scientists ISE","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eStatistics for Engineers and Scientists\u003c\/i\u003e stands out for its clear presentation of applied statistics. The book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. This edition features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets, to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.\u003cbr\u003e\u003cbr\u003eThe new edition of \u003ci\u003eStatistics for Engineers and Scientists\u003c\/i\u003e is also available in McGraw Hill Connect, featuring SmartBook 2.0, Adaptive Learning Assignments, and more!\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: Sampling and Descriptive Statistics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 2: Probability\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 3: Propagation of Error\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 4: Commonly Used Distributions\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 5: Confidence Intervals\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 6: Hypothesis Testing\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 7: Correlation and Simple Linear Regression\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 8: Multiple Regression\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eChapter 9: Factorial Experiments\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eChapter 10: Statistical Quality Control","brand":"McGraw-Hill Education","offers":[{"title":"Default Title","offer_id":48885327200599,"sku":"9781266115837","price":56.04,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781266115837.jpg?v=1722535924"},{"product_id":"advances-in-grid-generation-9781594542732","title":"Advances in Grid Generation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eGrid generation deals with the use of grids (meshes) in the numerical solution of partial differential equations by finite elements, finite volume, finite differences and boundary elements. Grid generation is applied in the aerospace, mechanical engineering and scientific computing fields. This book presents new research in the field.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886516121943,"sku":"9781594542732","price":176.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781594542732.jpg?v=1722540412"},{"product_id":"new-trends-in-cryptographic-systems-9781594549779","title":"New Trends in Cryptographic Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCryptography is the study of methods to transform information from its original comprehensible form into a scrambled incomprehensible form, such that its content can only be disclosed to some qualified persons. In the past, cryptography helped ensure secrecy in important communications, such as those of spies, military leaders, and diplomats. In recent decades, it has expanded in two main ways: firstly, it provides mechanisms for more than just keeping secrets through schemes like digital signatures, digital cash, etc; secondly, cryptography is used by almost all computer users as it is embedded into the infrastructure for computing and telecommunications. Cryptography ensures secure communications through confidentiality, integrity, authenticity and non-repudiation. Cryptography has evolved over the years from Julius Cesar''s cipher, which simply shifts the letters of the words a fixed number of times, to the sophisticated RSA algorithm, which was invented by Ronald L. Rivest, Adi Shamir and Leonard M. Adleman, and the elegant AES cipher (Advanced Encryption Standard), which was invented by Joan Daemen and Vincent Rijmen. The need for fast but secure cryptographic systems is growing bigger. Therefore, dedicated hardware for cryptography is becoming a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not safe. Attacks based on power consumption and electromagnetic Analysis, such as SPA, DPA and EMA have been successfully used to retrieve secret information stored in cryptographic devices. Besides performance in terms of area and throughput, designer of embedded cryptographic hardware must worry about the leakage of their implementations. The content of this book is divided into three main parts, which are focused on new trends in cryptographic hardware, arithmetic and factoring.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886537716055,"sku":"9781594549779","price":173.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781594549779.jpg?v=1722540507"},{"product_id":"techniques-of-scientific-computing-for-the-energy-environment-9781600219214","title":"Techniques of Scientific Computing for the Energy","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eResearch and development in scientific computing and computational science has considerably increased the power of numerical simulation. Engineers and researchers are now able to solve large and complex problems which were impossible to solve in the past. This new book presents some techniques, methods and algorithms for solving engineering problems arising in energy and environment applications.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886611181911,"sku":"9781600219214","price":73.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781600219214.jpg?v=1722540858"},{"product_id":"scientific-computing-for-scientists-and-engineers-9783110999617","title":"Scientific Computing: For Scientists and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003cem\u003eScientific Computing for Scientists and Engineers\u003c\/em\u003e is designed to teach undergraduate students relevant numerical methods and required fundamentals in scientific computing. \u003c\/p\u003e \u003cp\u003e Most problems in science and engineering require the solution of mathematical problems, most of which can only be done on a computer. Accurately approximating those problems requires solving differential equations and linear systems with millions of unknowns, and smart algorithms can be used on computers to reduce calculation times from years to minutes or even seconds. This book explains: How can we approximate these important mathematical processes? How accurate are our approximations? How efficient are our approximations? \u003c\/p\u003e \u003cp\u003e \u003cem\u003eScientific Computing for Scientists and Engineers\u003c\/em\u003e covers: \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003e An introduction to a wide range of numerical methods for linear systems, eigenvalue problems, differential equations, numerical integration, and nonlinear problems; \u003c\/li\u003e\n\u003cli\u003e Scientific computing fundamentals like floating point representation of numbers and convergence; \u003c\/li\u003e\n\u003cli\u003e Analysis of accuracy and efficiency; \u003c\/li\u003e\n\u003cli\u003e Simple programming examples in MATLAB to illustrate the algorithms and to solve real life problems; \u003c\/li\u003e\n\u003cli\u003e Exercises to reinforce all topics. \u003c\/li\u003e\n\u003c\/ul\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889051480407,"sku":"9783110999617","price":17.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110999617.jpg?v=1722552453"},{"product_id":"engineering-mathematics-v-1-9788120308046","title":"Engineering Mathematics: v. 1","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"PHI Learning","offers":[{"title":"Default Title","offer_id":48889452003671,"sku":"9788120308046","price":10.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9788120308046.jpg?v=1722554431"},{"product_id":"a-textbook-on-dynamics-9788121903424","title":"A Textbook on Dynamics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"S Chand \u0026 Co Ltd","offers":[{"title":"Default Title","offer_id":48889488408919,"sku":"9788121903424","price":7.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9788121903424.jpg?v=1722554585"},{"product_id":"schaums-outline-of-laplace-transforms-9780070602311","title":"Schaums Outline of Laplace Transforms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eConfusing Textbooks? Missed Lectures? Not Enough Time?\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThis Schaum's Outline gives you\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePractice problems with full explanations that reinforce knowledge\u003c\/li\u003e\n\u003cli\u003eCoverage of the most up-to-date developments in your course field\u003c\/li\u003e\n\u003cli\u003eIn-depth review of practices and applications\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eFully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!\u003c\/p\u003e\u003cp\u003eSchaum's Outlines-Problem Solved.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eThe Laplace Transform.The Inverse Laplace Transform.Applications to Differential Equations.Applications to Integral and Difference Equations.Complex Variable Theory.Fourier Series and Integrals.The Complex Inversion Formula.Applications to Boundary-Value Problems.Appendix A: Table of General Properties of Laplace Transforms.Appendix B: Table of Special Laplace Transforms.Appendix C: Table of Special Functions.","brand":"McGraw-Hill Education - Europe","offers":[{"title":"Default Title","offer_id":49083346780503,"sku":"9780070602311","price":23.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780070602311.jpg?v=1725548622"},{"product_id":"mathematical-methods-and-physical-insights-9781107156418","title":"Mathematical Methods and Physical Insights","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMathematics instruction is often more effective when presented in a physical context. Schramm uses this insight to help develop students'' physical intuition as he guides them through the mathematical methods required to study upper-level physics. Based on the undergraduate Math Methods course he has taught for many years at Occidental College, the text encourages a symbiosis through which the physics illuminates the math, which in turn informs the physics. Appropriate for both classroom and self-study use, the text begins with a review of useful techniques to ensure students are comfortable with prerequisite material. It then moves on to cover vector fields, analytic functions, linear algebra, function spaces, and differential equations. Written in an informal and engaging style, it also includes short supplementary digressions (''By the Ways'') as optional boxes showcasing directions in which the math or physics may be explored further. Extensive problems are included throughout, man\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Schramm's Mathematical Methods and Physical Insights is a very welcome new textbook in the area of pedagogical mathematical physics. The book contains numerous insightful and helpful examples from classical and modern physics, as well as unusual and interesting applications of the presented mathematical concepts within and beyond physics. I find the 'BTW' inserts, and the lively, unpretentious style of the book both exciting and entertaining. The material discussed in Schramm's textbook covers entirely the scope of our three-trimester-long Mathematical Methods offering, and additionally provides useful background material to 'even out' the often inhomogeneous preparation of students in these classes; I will definitely consider adopting this textbook for my next offerings of Mathematical Methods for Physics here at the University of California, Santa Cruz.' Professor Stefano Profumo, University of California, Santa Cruz\u003cbr\u003e'As the title suggests, Schramm's book distinguishes itself from traditional mathematical methods texts in its thematic approach that builds from unit to unit, using rich examples from physical systems that elucidate each topic.  A must-read for physicists wanting to expand their mathematical toolkit as well as for mathematicians hoping to gain new insights from the physical world.' Professor Jason Detwiler, University of Washington\u003cbr\u003e'For students taking physics courses, one of the difficulties is how to apply appropriate mathematical skills in problem solving (e.g., using integration to find the electric field produced by a continuous charge distribution). This book introduces commonly used mathematical skills from the perspective of a physicist. Focusing on the topics in upper-level physics courses, it provides the mathematical skills for solving problems in each topic. The book is easy to read, and the problems at the end of each chapter offer plenty of exercises for students. The book is a valuable resource for undergraduate students taking upper-level physics courses, and instructors teaching such courses. It could also be a useful reference for graduate students.' Professor Hong Lin, Bates College\u003cbr\u003e'Physics and engineering students often struggle with mathematics texts that present the material in an abstract fashion, disconnected from practical applications. Schramm's text represents a refreshing and much needed change. Providing context and intuition throughout, with many worked examples, and in engaging prose, it does more than just explain mathematical methods; it infuses them with meaning and relevance.' Dr. Jochen Rau, RheinMain University of Applied Sciences, Germany\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Part I. Things You Just Gotta' Know: 1. Prelude: symbiosis; 2. Coordinating coordinates; 3. Complex numbers; 4. Index algebra; 5. Brandishing binomials; 6. Infinite series; 7. Interlude: orbits in a central potential; 8. Ten integration techniques and tricks; 9. The Dirac delta function; 10. Coda: statistical mechanics; Part II. The Calculus of Vector Fields: 11. Prelude: visualizing vector fields; 12. grad, div \u0026amp; curl; 13. Interlude: irrotational and incompressible; 14. Integrating scalar \u0026amp; vector fields; 15. The theorems of Gauss \u0026amp; Stokes; 16. Simply connected regions; 17. Coda: mostly Maxwell; Part III. Calculus in the Complex Plane: 18. Prelude: path independence in the complex plane; 19. Series, singularities \u0026amp; branches; 20. Interlude: conformal mapping; 21. The calculus of residues; 22. Coda: analyticity \u0026amp; causality; Part IV. Linear Algebra: 23. Prelude: superposition; 24. Vector space; 25. The inner product; 26. Interlude: rotations; 27. The Eigenvalue problem; 28. Coda: normal modes; Entr'acte: Tensors; 29. Cartesian tensors; 30. Beyond cartesian; Part V. Orthogonal Functions: 31. Prelude: 1 2 3 . . . infinity; 32. Eponymous polynomials; 33. Fourier series; 34. Convergence and completeness; 35. Interlude: beyond the straight \u0026amp; narrow; 36. Fourier transforms; 37. Coda: of time intervals and frequency bands; Part VI. Differential Equations: 38. Prelude: first order first; 39. Second-order ODEs; 40. Interlude: the Sturm-Liouville Eigenvalue problem; 41. Partial differential equations; 42. Green's functions; 43. Coda: quantum scattering; Appendix A. Curvilinear coordinates; Appendix B. Rotations in R3; Appendix C. The Bessel family of functions; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49083821326679,"sku":"9781107156418","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107156418.jpg?v=1725550123"},{"product_id":"a-students-guide-to-numerical-methods-9781107479500","title":"A Students Guide to Numerical Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWritten for senior undergraduates in all disciplines of physical science and engineering, the plain language style of this concise guide to numerical methods concentrates on developing computational skills and avoids potentially intimidating formal mathematical proofs. Including numerous worked examples and exercises, this textbook explains the practical realities of numerical techniques.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; 1. Fitting functions to data; 2. Ordinary differential equations; 3. Two-point boundary conditions; 4. Partial differential equations; 5. Diffusion: parabolic PDEs; 6. Elliptic problems and iterative matrix solution; 7. Fluid dynamics and hyperbolic equations; 8. Boltzmann's equation and its solution; 9. Energy-resolved diffusive transport; 10. Atomistic and particle-in-cell simulation; 11. Monte Carlo techniques; 12. Monte Carlo radiation transport; 13. Next steps; Appendix A. Summary of matrix algebra; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":49083822014807,"sku":"9781107479500","price":23.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107479500.jpg?v=1725550125"},{"product_id":"probability-and-statistics-for-scientists-and-engineers-9781906574833","title":"Probability and Statistics for Scientists and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"New Academic Science Ltd","offers":[{"title":"Default Title","offer_id":49084603203927,"sku":"9781906574833","price":33.25,"currency_code":"GBP","in_stock":true}]},{"product_id":"collins-gcse-revision-and-practice-new-2015-curriculum-edition-gcse-maths-foundation-tier-allinone-revision-and-practice-9780008112547","title":"Collins GCSE Revision and Practice  New 2015","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExam Board: Edexcel, AQA, OCR \u0026amp; WJEC EduqasLevel: GCSE 9-1Subject: Maths FoundationSuitable for the 2025 examsComplete revision and practice to fully prepare for the GCSE grade 9-1 examsRevision that Sticks! Collins GCSE 9-1 Maths Foundation Complete All-in-One Revision and Practice uses a revision method that really works: repeated practice throughout.A revision guide, workbook and practice paper in one book!With clear and concise revision for every topic, plus seven practice opportunities, Collins offers the best revision at the best price.Includes:quick tests as you goend-of-topic practice questionstopic review questions later in the bookmixed practice questions at the end of the bookmore topic-by-topic practice in the workbooka complete exam-style paperfree Q\u0026amp;A flashcards to download onlinefree ebook version","brand":"HarperCollins Publishers","offers":[{"title":"Default Title","offer_id":49399411802455,"sku":"9780008112547","price":10.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780008112547.jpg?v=1730467497"},{"product_id":"applied-chemometrics-for-scientists-9780470016862","title":"Applied Chemometrics for Scientists","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe book introduces most of the basic tools of chemometrics including experimental design, signal analysis, statistical methods for analytical chemistry and multivariate methods.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"…useful for introducing chemometrics in undergraduate classes…a valuable encyclopedia for researchers…\" (\u003ci\u003eJournal of Chemical Education\u003c\/i\u003e, December 2007)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Development of Chemometrics.\u003c\/p\u003e \u003cp\u003e1.2 Application Areas.\u003c\/p\u003e \u003cp\u003e1.3 How to Use this Book.\u003c\/p\u003e \u003cp\u003e1.4 Literature and Other Sources of Information.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Experimental Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Why Design Experiments in Chemistry?\u003c\/p\u003e \u003cp\u003e2.2 Degrees of Freedom and Sources of Error.\u003c\/p\u003e \u003cp\u003e2.3 Analysis of Variance and Interpretation of Errors.\u003c\/p\u003e \u003cp\u003e2.4 Matrices, Vectors and the Pseudoinverse.\u003c\/p\u003e \u003cp\u003e2.5 Design Matrices.\u003c\/p\u003e \u003cp\u003e2.6 Factorial Designs.\u003c\/p\u003e \u003cp\u003e2.7 An Example of a Factorial Design.\u003c\/p\u003e \u003cp\u003e2.8 Fractional Factorial Designs.\u003c\/p\u003e \u003cp\u003e2.9 Plackett–Burman and Taguchi Designs.\u003c\/p\u003e \u003cp\u003e2.10 The Application of a Plackett–Burman Design to the Screening of Factors Influencing a Chemical Reaction.\u003c\/p\u003e \u003cp\u003e2.11 Central Composite Designs.\u003c\/p\u003e \u003cp\u003e2.12 Mixture Designs.\u003c\/p\u003e \u003cp\u003e2.13 A Four Component Mixture Design Used to Study Blending of Olive Oils.\u003c\/p\u003e \u003cp\u003e2.14 Simplex Optimization.\u003c\/p\u003e \u003cp\u003e2.15 Leverage and Confidence in Models.\u003c\/p\u003e \u003cp\u003e2.16 Designs for Multivariate Calibration.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Statistical Concepts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Statistics for Chemists.\u003c\/p\u003e \u003cp\u003e3.2 Errors.\u003c\/p\u003e \u003cp\u003e3.3 Describing Data.\u003c\/p\u003e \u003cp\u003e3.4 The Normal Distribution.\u003c\/p\u003e \u003cp\u003e3.5 Is a Distribution Normal?\u003c\/p\u003e \u003cp\u003e3.6 Hypothesis Tests.\u003c\/p\u003e \u003cp\u003e3.7 Comparison of Means: the \u003ci\u003et\u003c\/i\u003e-Test.\u003c\/p\u003e \u003cp\u003e3.8 \u003ci\u003eF\u003c\/i\u003e-Test for Comparison of Variances.\u003c\/p\u003e \u003cp\u003e3.9 Confidence in Linear Regression.\u003c\/p\u003e \u003cp\u003e3.10 More about Confidence.\u003c\/p\u003e \u003cp\u003e3.11 Consequences of Outliers and How to Deal with Them.\u003c\/p\u003e \u003cp\u003e3.12 Detection of Outliers.\u003c\/p\u003e \u003cp\u003e3.13 Shewhart Charts.\u003c\/p\u003e \u003cp\u003e3.14 More about Control Charts.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sequential Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Sequential Data.\u003c\/p\u003e \u003cp\u003e4.2 Correlograms.\u003c\/p\u003e \u003cp\u003e4.3 Linear Smoothing Functions and Filters.\u003c\/p\u003e \u003cp\u003e4.4 Fourier Transforms.\u003c\/p\u003e \u003cp\u003e4.5 Maximum Entropy and Bayesian Methods.\u003c\/p\u003e \u003cp\u003e4.6 Fourier Filters.\u003c\/p\u003e \u003cp\u003e4.7 Peakshapes in Chromatography and Spectroscopy.\u003c\/p\u003e \u003cp\u003e4.8 Derivatives in Spectroscopy and Chromatography.\u003c\/p\u003e \u003cp\u003e4.9 Wavelets.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Pattern Recognition.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Principal Components Analysis.\u003c\/p\u003e \u003cp\u003e5.3 Graphical Representation of Scores and Loadings.\u003c\/p\u003e \u003cp\u003e5.4 Comparing Multivariate Patterns.\u003c\/p\u003e \u003cp\u003e5.5 Preprocessing.\u003c\/p\u003e \u003cp\u003e5.6 Unsupervised Pattern Recognition: Cluster Analysis.\u003c\/p\u003e \u003cp\u003e5.7 Supervised Pattern Recognition.\u003c\/p\u003e \u003cp\u003e5.8 Statistical Classification Techniques.\u003c\/p\u003e \u003cp\u003e5.9 K Nearest Neighbour Method.\u003c\/p\u003e \u003cp\u003e5.10 How Many Components Characterize a Dataset?\u003c\/p\u003e \u003cp\u003e5.11 Multiway Pattern Recognition.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Calibration.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Univariate Calibration.\u003c\/p\u003e \u003cp\u003e6.3 Multivariate Calibration and the Spectroscopy of Mixtures.\u003c\/p\u003e \u003cp\u003e6.4 Multiple Linear Regression.\u003c\/p\u003e \u003cp\u003e6.5 Principal Components Regression.\u003c\/p\u003e \u003cp\u003e6.6 Partial Least Squares.\u003c\/p\u003e \u003cp\u003e6.7 How Good is the Calibration and What is the Most Appropriate Model?\u003c\/p\u003e \u003cp\u003e6.8 Multiway Calibration.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Coupled Chromatography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Preparing the Data.\u003c\/p\u003e \u003cp\u003e7.3 Chemical Composition of Sequential Data.\u003c\/p\u003e \u003cp\u003e7.4 Univariate Purity Curves.\u003c\/p\u003e \u003cp\u003e7.5 Similarity Based Methods.\u003c\/p\u003e \u003cp\u003e7.6 Evolving and Window Factor Analysis.\u003c\/p\u003e \u003cp\u003e7.7 Derivative Based Methods.\u003c\/p\u003e \u003cp\u003e7.8 Deconvolution of Evolutionary Signals.\u003c\/p\u003e \u003cp\u003e7.9 Noniterative Methods for Resolution.\u003c\/p\u003e \u003cp\u003e7.10 Iterative Methods for Resolution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Equilibria, Reactions and Process Analytics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 The Study of Equilibria using Spectroscopy.\u003c\/p\u003e \u003cp\u003e8.2 Spectroscopic Monitoring of Reactions.\u003c\/p\u003e \u003cp\u003e8.3 Kinetics and Multivariate Models for the Quantitative Study of Reactions\u003c\/p\u003e \u003cp\u003e8.4 Developments in the Analysis of Reactions using On-line Spectroscopy.\u003c\/p\u003e \u003cp\u003e8.5 The Process Analytical Technology Initiative.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Improving Yields and Processes Using Experimental Designs.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Use of Statistical Designs for Improving the Performance of Synthetic Reactions.\u003c\/p\u003e \u003cp\u003e9.3 Screening for Factors that Influence the Performance of a Reaction.\u003c\/p\u003e \u003cp\u003e9.4 Optimizing the Process Variables.\u003c\/p\u003e \u003cp\u003e9.5 Handling Mixture Variables using Simplex Designs.\u003c\/p\u003e \u003cp\u003e9.6 More about Mixture Variables.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Biological and Medical Applications of Chemometrics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Taxonomy.\u003c\/p\u003e \u003cp\u003e10.3 Discrimination.\u003c\/p\u003e \u003cp\u003e10.4 Mahalanobis Distance.\u003c\/p\u003e \u003cp\u003e10.5 Bayesian Methods and Contingency Tables.\u003c\/p\u003e \u003cp\u003e10.6 Support Vector Machines.\u003c\/p\u003e \u003cp\u003e10.7 Discriminant Partial Least Squares.\u003c\/p\u003e \u003cp\u003e10.8 Micro-organisms.\u003c\/p\u003e \u003cp\u003e10.9 Medical Diagnosis using Spectroscopy.\u003c\/p\u003e \u003cp\u003e10.10 Metabolomics using Coupled Chromatography and Nuclear Magnetic Resonance.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Biological Macromolecules.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Sequence Alignment and Scoring Matches.\u003c\/p\u003e \u003cp\u003e11.3 Sequence Similarity.\u003c\/p\u003e \u003cp\u003e11.4 Tree Diagrams.\u003c\/p\u003e \u003cp\u003e11.5 Phylogenetic Trees.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multivariate Image Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Scaling Images.\u003c\/p\u003e \u003cp\u003e12.3 Filtering and Smoothing the Image.\u003c\/p\u003e \u003cp\u003e12.4 Principal Components for the Enhancement of Images.\u003c\/p\u003e \u003cp\u003e12.5 Regression of Images.\u003c\/p\u003e \u003cp\u003e12.6 Alternating Least Squares as Employed in Image Analysis.\u003c\/p\u003e \u003cp\u003e12.7 Multiway Methods In Image Analysis.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Food.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 How to Determine the Origin of a Food Product using Chromatography.\u003c\/p\u003e \u003cp\u003e13.3 Near Infrared Spectroscopy.\u003c\/p\u003e \u003cp\u003e13.4 Other Information.\u003c\/p\u003e \u003cp\u003e13.5 Sensory Analysis: Linking Composition to Properties.\u003c\/p\u003e \u003cp\u003e13.6 Varimax Rotation.\u003c\/p\u003e \u003cp\u003e13.7 Calibrating Sensory Descriptors to Composition.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402255802711,"sku":"9780470016862","price":84.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470016862.jpg?v=1730479852"},{"product_id":"brownian-motion-calculus-9780470021705","title":"Brownian Motion Calculus","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThere are not many calculus books that are very accessible to students without a strong mathematical background and the large majority of financial derivatives students do not have a strong quantitative background. This book provides a short introduction to the subject with examples of its use in mathematical finance e. g pricing of derivatives.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Brownian Motion 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Origins 1\u003c\/p\u003e \u003cp\u003e1.2 Brownian Motion Specification 2\u003c\/p\u003e \u003cp\u003e1.3 Use of Brownian Motion in Stock Price Dynamics 4\u003c\/p\u003e \u003cp\u003e1.4 Construction of Brownian Motion from a Symmetric Random Walk 6\u003c\/p\u003e \u003cp\u003e1.5 Covariance of Brownian Motion 12\u003c\/p\u003e \u003cp\u003e1.6 Correlated Brownian Motions 14\u003c\/p\u003e \u003cp\u003e1.7 Successive Brownian Motion Increments 16\u003c\/p\u003e \u003cp\u003e1.7.1 Numerical Illustration 17\u003c\/p\u003e \u003cp\u003e1.8 Features of a Brownian Motion Path 19\u003c\/p\u003e \u003cp\u003e1.8.1 Simulation of Brownian Motion Paths 19\u003c\/p\u003e \u003cp\u003e1.8.2 Slope of Path 20\u003c\/p\u003e \u003cp\u003e1.8.3 Non-Differentiability of Brownian Motion Path 21\u003c\/p\u003e \u003cp\u003e1.8.4 Measuring Variability 24\u003c\/p\u003e \u003cp\u003e1.9 Exercises 26\u003c\/p\u003e \u003cp\u003e1.10 Summary 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Martingales 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Simple Example 31\u003c\/p\u003e \u003cp\u003e2.2 Filtration 32\u003c\/p\u003e \u003cp\u003e2.3 Conditional Expectation 33\u003c\/p\u003e \u003cp\u003e2.3.1 General Properties 34\u003c\/p\u003e \u003cp\u003e2.4 Martingale Description 36\u003c\/p\u003e \u003cp\u003e2.4.1 Martingale Construction by Conditioning 36\u003c\/p\u003e \u003cp\u003e2.5 Martingale Analysis Steps 37\u003c\/p\u003e \u003cp\u003e2.6 Examples of Martingale Analysis 37\u003c\/p\u003e \u003cp\u003e2.6.1 Sum of Independent Trials 37\u003c\/p\u003e \u003cp\u003e2.6.2 Square of Sum of Independent Trials 38\u003c\/p\u003e \u003cp\u003e2.6.3 Product of Independent Identical Trials 39\u003c\/p\u003e \u003cp\u003e2.6.4 Random Process \u003ci\u003eB(t)\u003c\/i\u003e 39\u003c\/p\u003e \u003cp\u003e2.6.5 Random Process exp[\u003ci\u003eB(t)\u003c\/i\u003e – \u003ci\u003et\u003c\/i\u003e] 40\u003c\/p\u003e \u003cp\u003e2.6.6 Frequently Used Expressions 40\u003c\/p\u003e \u003cp\u003e2.7 Process of Independent Increments 41\u003c\/p\u003e \u003cp\u003e2.8 Exercises 42\u003c\/p\u003e \u003cp\u003e2.9 Summary 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Itō Stochastic Integral 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 How a Stochastic Integral Arises 45\u003c\/p\u003e \u003cp\u003e3.2 Stochastic Integral for Non-Random Step-Functions 47\u003c\/p\u003e \u003cp\u003e3.3 Stochastic Integral for Non-Anticipating Random Step-Functions 49\u003c\/p\u003e \u003cp\u003e3.4 Extension to Non-Anticipating General Random Integrands 52\u003c\/p\u003e \u003cp\u003e3.5 Properties of an Itō Stochastic Integral 57\u003c\/p\u003e \u003cp\u003e3.6 Significance of Integrand Position 59\u003c\/p\u003e \u003cp\u003e3.7 Itō integral of Non-Random Integrand 61\u003c\/p\u003e \u003cp\u003e3.8 Area under a Brownian Motion Path 62\u003c\/p\u003e \u003cp\u003e3.9 Exercises 64\u003c\/p\u003e \u003cp\u003e3.10 Summary 67\u003c\/p\u003e \u003cp\u003e3.11 A Tribute to Kiyosi Itō 68\u003c\/p\u003e \u003cp\u003eAcknowledgment 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Itō Calculus 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Stochastic Differential Notation 73\u003c\/p\u003e \u003cp\u003e4.2 Taylor Expansion in Ordinary Calculus 74\u003c\/p\u003e \u003cp\u003e4.3 Itō’s Formula as a Set of Rules 75\u003c\/p\u003e \u003cp\u003e4.4 Illustrations of Itō’s Formula 78\u003c\/p\u003e \u003cp\u003e4.4.1 Frequent Expressions for Functions of Two Processes 78\u003c\/p\u003e \u003cp\u003e4.4.2 Function of Brownian Motion \u003ci\u003ef\u003c\/i\u003e [\u003ci\u003eB(t)\u003c\/i\u003e] 80\u003c\/p\u003e \u003cp\u003e4.4.3 Function of Time and Brownian Motion \u003ci\u003ef \u003c\/i\u003e[\u003ci\u003et, B(t)\u003c\/i\u003e]82\u003c\/p\u003e \u003cp\u003e4.4.4 Finding an Expression for 83\u003c\/p\u003e \u003cp\u003e4.4.5 Change of Numeraire 84\u003c\/p\u003e \u003cp\u003e4.4.6 Deriving an Expectation via an ODE 85\u003c\/p\u003e \u003cp\u003e4.5 Lévy Characterization of Brownian Motion 87\u003c\/p\u003e \u003cp\u003e4.6 Combinations of Brownian Motions 89\u003c\/p\u003e \u003cp\u003e4.7 Multiple Correlated Brownian Motions 92\u003c\/p\u003e \u003cp\u003e4.8 Area under a Brownian Motion Path – Revisited 95\u003c\/p\u003e \u003cp\u003e4.9 Justification of Itō’s Formula 96\u003c\/p\u003e \u003cp\u003e4.10 Exercises 100\u003c\/p\u003e \u003cp\u003e4.11 Summary 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Stochastic Differential Equations 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Structure of a Stochastic Differential Equation 103\u003c\/p\u003e \u003cp\u003e5.2 Arithmetic Brownian Motion SDE 104\u003c\/p\u003e \u003cp\u003e5.3 Geometric Brownian Motion SDE 105\u003c\/p\u003e \u003cp\u003e5.4 Ornstein–Uhlenbeck SDE 108\u003c\/p\u003e \u003cp\u003e5.5 Mean-Reversion SDE 110\u003c\/p\u003e \u003cp\u003e5.6 Mean-Reversion with Square-Root Diffusion SDE 112\u003c\/p\u003e \u003cp\u003e5.7 Expected Value of Square-Root Diffusion Process 112\u003c\/p\u003e \u003cp\u003e5.8 Coupled SDEs 114\u003c\/p\u003e \u003cp\u003e5.9 Checking the Solution of a SDE 115\u003c\/p\u003e \u003cp\u003e5.10 General Solution Methods for Linear SDEs 115\u003c\/p\u003e \u003cp\u003e5.11 Martingale Representation 120\u003c\/p\u003e \u003cp\u003e5.12 Exercises 123\u003c\/p\u003e \u003cp\u003e5.13 Summary 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Option Valuation 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Partial Differential Equation Method 128\u003c\/p\u003e \u003cp\u003e6.2 Martingale Method in One-Period Binomial Framework 130\u003c\/p\u003e \u003cp\u003e6.3 Martingale Method in Continuous-Time Framework 135\u003c\/p\u003e \u003cp\u003e6.4 Overview of Risk-Neutral Method 138\u003c\/p\u003e \u003cp\u003e6.5 Martingale Method Valuation of Some European Options 139\u003c\/p\u003e \u003cp\u003e6.5.1 Digital Call 139\u003c\/p\u003e \u003cp\u003e6.5.2 Asset-or-Nothing Call 141\u003c\/p\u003e \u003cp\u003e6.5.3 Standard European Call 142\u003c\/p\u003e \u003cp\u003e6.6 Links between Methods 144\u003c\/p\u003e \u003cp\u003e6.6.1 Feynman-Kač Link between PDE Method and Martingale Method 144\u003c\/p\u003e \u003cp\u003e6.6.2 Multi-Period Binomial Link to Continuous 146\u003c\/p\u003e \u003cp\u003e6.7 Exercise 147\u003c\/p\u003e \u003cp\u003e6.8 Summary 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Change of Probability 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Change of Discrete Probability Mass 151\u003c\/p\u003e \u003cp\u003e7.2 Change of Normal Density 153\u003c\/p\u003e \u003cp\u003e7.3 Change of Brownian Motion 154\u003c\/p\u003e \u003cp\u003e7.4 Girsanov Transformation 155\u003c\/p\u003e \u003cp\u003e7.5 Use in Stock Price Dynamics – Revisited 160\u003c\/p\u003e \u003cp\u003e7.6 General Drift Change 162\u003c\/p\u003e \u003cp\u003e7.7 Use in Importance Sampling 163\u003c\/p\u003e \u003cp\u003e7.8 Use in Deriving Conditional Expectations 167\u003c\/p\u003e \u003cp\u003e7.9 Concept of Change of Probability 172\u003c\/p\u003e \u003cp\u003e7.9.1 Relationship between Expected Values under Equivalent Probabilities 174\u003c\/p\u003e \u003cp\u003e7.10 Exercises 174\u003c\/p\u003e \u003cp\u003e7.11 Summary 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Numeraire 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Change of Numeraire 179\u003c\/p\u003e \u003cp\u003e8.1.1 In Discrete Time 179\u003c\/p\u003e \u003cp\u003e8.1.2 In Continuous Time 182\u003c\/p\u003e \u003cp\u003e8.2 Forward Price Dynamics 184\u003c\/p\u003e \u003cp\u003e8.2.1 Dynamics of Forward Price of a Bond 184\u003c\/p\u003e \u003cp\u003e8.2.2 Dynamics of Forward Price of any Traded Asset 185\u003c\/p\u003e \u003cp\u003e8.3 Option Valuation under most Suitable Numeraire 187\u003c\/p\u003e \u003cp\u003e8.3.1 Exchange Option 187\u003c\/p\u003e \u003cp\u003e8.3.2 Option on Bond 188\u003c\/p\u003e \u003cp\u003e8.3.3 European Call under Stochastic Interest Rate 188\u003c\/p\u003e \u003cp\u003e8.4 Relating Change of Numeraire to Change of Probability 190\u003c\/p\u003e \u003cp\u003e8.5 Change of Numeraire for Geometric Brownian Motion 192\u003c\/p\u003e \u003cp\u003e8.6 Change of Numeraire in LIBOR Market Model 194\u003c\/p\u003e \u003cp\u003e8.7 Application in Credit Risk Modelling 198\u003c\/p\u003e \u003cp\u003e8.8 Exercises 200\u003c\/p\u003e \u003cp\u003e8.9 Summary 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAnnexes\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Annex A: Computations with Brownian Motion 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA. 1 Moment Generating Function and Moments of Brownian Motion 205\u003c\/p\u003e \u003cp\u003eA. 2 Probability of Brownian Motion Position 208\u003c\/p\u003e \u003cp\u003eA. 3 Brownian Motion Reflected at the Origin 208\u003c\/p\u003e \u003cp\u003eA. 4 First Passage of a Barrier 214\u003c\/p\u003e \u003cp\u003eA. 5 Alternative Brownian Motion Specification 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Annex B: Ordinary Integration 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB. 1 Riemann Integral 221\u003c\/p\u003e \u003cp\u003eB. 2 Riemann–Stieltjes Integral 226\u003c\/p\u003e \u003cp\u003eB. 3 Other Useful Properties 231\u003c\/p\u003e \u003cp\u003eB. 4 References 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Annex C: Brownian Motion Variability 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC. 1 Quadratic Variation 235\u003c\/p\u003e \u003cp\u003eC. 2 First Variation 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Annex D: Norms 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD. 1 Distance between Points 239\u003c\/p\u003e \u003cp\u003eD. 2 Norm of a Function 242\u003c\/p\u003e \u003cp\u003eD. 3 Norm of a Random Variable 244\u003c\/p\u003e \u003cp\u003eD. 4 Norm of a Random Process 244\u003c\/p\u003e \u003cp\u003eD. 5 Reference 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Annex E: Convergence Concepts 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE. 1 Central Limit Theorem 247\u003c\/p\u003e \u003cp\u003eE. 2 Mean-Square Convergence 248\u003c\/p\u003e \u003cp\u003eE. 3 Almost Sure Convergence 249\u003c\/p\u003e \u003cp\u003eE. 4 Convergence in Probability 250\u003c\/p\u003e \u003cp\u003eE. 5 Summary 250\u003c\/p\u003e \u003cp\u003eAnswers to Exercises 253\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003eIndex 303\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402259243351,"sku":"9780470021705","price":35.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470021705.jpg?v=1730479862"},{"product_id":"modern-engineering-statistics-9780470081877","title":"Modern Engineering Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of tools rather than endless streams of seemingly unrelated methods and formulae.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Overall this is an excellent book, which defines a broader mandate than many of its competing texts. By providing, clear, understandable discussion of the basics of statistics through to more advanced methods commonly used by engineers, this book is an essential reference for practitioners, and an ideal text for a two semester course introducing engineers to the power and utility of statistics.\" (\u003ci\u003eThe American Statistician,\u003c\/i\u003e August \u003ci\u003e2008)\u003c\/i\u003e  \u003cp\u003e\"In this book on modern engineering statistics, Ryan does an excellent job of providing the appropriate statistical concepts and tools using engineering resources.... Highly recommended. Lower- and upper-division undergraduates\" (\u003ci\u003eCHOICE\u003c\/i\u003e, April 2008)\u003c\/p\u003e \u003cp\u003e\"This self-contained volume motivates an appreciation of statistical techniques within the context of engineering; many datasets that are used in the chapters and exercises are from engineering sources. This book is ideal for either a one- or two-semester course in engineering statistics.\" (\u003ci\u003eComputing Reviews\u003c\/i\u003e, April 2008)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Methods of Collecting and Presenting Data 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Observational Data and Data from Designed Experiments 3\u003c\/p\u003e \u003cp\u003e1.2 Populations and Samples 5\u003c\/p\u003e \u003cp\u003e1.3 Variables 6\u003c\/p\u003e \u003cp\u003e1.4 Methods of Displaying Small Data Sets 7\u003c\/p\u003e \u003cp\u003e1.5 Methods of Displaying Large Data Sets 16\u003c\/p\u003e \u003cp\u003e1.6 Outliers 22\u003c\/p\u003e \u003cp\u003e1.7 Other Methods 22\u003c\/p\u003e \u003cp\u003e1.8 Extremely Large Data Sets: Data Mining 23\u003c\/p\u003e \u003cp\u003e1.9 Graphical Methods: Recommendations 23\u003c\/p\u003e \u003cp\u003e1.10 Summary 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003eExercises 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Measures of Location and Dispersion 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Estimating Location Parameters 46\u003c\/p\u003e \u003cp\u003e2.2 Estimating Dispersion Parameters 50\u003c\/p\u003e \u003cp\u003e2.3 Estimating Parameters from Grouped Data 55\u003c\/p\u003e \u003cp\u003e2.4 Estimates from a Boxplot 57\u003c\/p\u003e \u003cp\u003e2.5 Computing Sample Statistics with MINITAB 58\u003c\/p\u003e \u003cp\u003e2.6 Summary 58\u003c\/p\u003e \u003cp\u003eReference 58\u003c\/p\u003e \u003cp\u003eExercises 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Probability and Common Probability Distributions 68\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Probability: From the Ethereal to the Concrete 68\u003c\/p\u003e \u003cp\u003e3.3 Common Discrete Distributions 76\u003c\/p\u003e \u003cp\u003e3.4 Common Continuous Distributions 92\u003c\/p\u003e \u003cp\u003e3.5 General Distribution Fitting 106\u003c\/p\u003e \u003cp\u003e3.6 How to Select a Distribution 107\u003c\/p\u003e \u003cp\u003e3.7 Summary 108\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003eExercises 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Point Estimation 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Point Estimators and Point Estimates 121\u003c\/p\u003e \u003cp\u003e4.2 Desirable Properties of Point Estimators 121\u003c\/p\u003e \u003cp\u003e4.3 Distributions of Sampling Statistics 125\u003c\/p\u003e \u003cp\u003e4.4 Methods of Obtaining Estimators 128\u003c\/p\u003e \u003cp\u003e4.5 Estimating σ\u003csub\u003eθ\u003c\/sub\u003e 132\u003c\/p\u003e \u003cp\u003e4.6 Estimating Parameters \u003ci\u003eWithout\u003c\/i\u003e Data 133\u003c\/p\u003e \u003cp\u003e4.7 Summary 133\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003eExercises 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Confidence Intervals and Hypothesis Tests—One Sample 140\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Confidence Interval for \u003ci\u003eμ\u003c\/i\u003e: Normal Distribution σ Not Estimated from Sample Data 140\u003c\/p\u003e \u003cp\u003e5.2 Confidence Interval for \u003ci\u003eμ\u003c\/i\u003e: Normal Distribution σ Estimated from Sample Data 146\u003c\/p\u003e \u003cp\u003e5.3 Hypothesis Tests for \u003ci\u003eμ\u003c\/i\u003e: Using Z and \u003ci\u003et\u003c\/i\u003e 147\u003c\/p\u003e \u003cp\u003e5.4 Confidence Intervals and Hypothesis Tests for a Proportion 157\u003c\/p\u003e \u003cp\u003e5.5 Confidence Intervals and Hypothesis Tests for σ\u003csup\u003e2\u003c\/sup\u003e and σ 161\u003c\/p\u003e \u003cp\u003e5.6 Confidence Intervals and Hypothesis Tests for the Poisson Mean 164\u003c\/p\u003e \u003cp\u003e5.7 Confidence Intervals and Hypothesis Tests When Standard Error Expressions are Not Available 166\u003c\/p\u003e \u003cp\u003e5.8 Type I and Type II Errors 168\u003c\/p\u003e \u003cp\u003e5.9 Practical Significance and Narrow Intervals: The Role of \u003ci\u003en\u003c\/i\u003e 172\u003c\/p\u003e \u003cp\u003e5.10 Other Types of Confidence Intervals 173\u003c\/p\u003e \u003cp\u003e5.11 Abstract of Main Procedures 174\u003c\/p\u003e \u003cp\u003e5.12 Summary 175\u003c\/p\u003e \u003cp\u003eAppendix: Derivation 176\u003c\/p\u003e \u003cp\u003eReferences 176\u003c\/p\u003e \u003cp\u003eExercises 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Confidence Intervals and Hypothesis Tests—Two Samples 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Confidence Intervals and Hypothesis Tests for Means: Independent Samples 189\u003c\/p\u003e \u003cp\u003e6.2 Confidence Intervals and Hypothesis Tests for Means: Dependent Samples 197\u003c\/p\u003e \u003cp\u003e6.3 Confidence Intervals and Hypothesis Tests for Two Proportions 200\u003c\/p\u003e \u003cp\u003e6.4 Confidence Intervals and Hypothesis Tests for Two Variances 202\u003c\/p\u003e \u003cp\u003e6.5 Abstract of Procedures 204\u003c\/p\u003e \u003cp\u003e6.6 Summary 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003eExercises 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Tolerance Intervals and Prediction Intervals 214\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Tolerance Intervals: Normality Assumed 215\u003c\/p\u003e \u003cp\u003e7.2 Tolerance Intervals and Six Sigma 219\u003c\/p\u003e \u003cp\u003e7.3 Distribution-Free Tolerance Intervals 219\u003c\/p\u003e \u003cp\u003e7.4 Prediction Intervals 221\u003c\/p\u003e \u003cp\u003e7.5 Choice Between Intervals 227\u003c\/p\u003e \u003cp\u003e7.6 Summary 227\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003eExercises 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Simple Linear Regression Correlation and Calibration 232\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 232\u003c\/p\u003e \u003cp\u003e8.2 Simple Linear Regression 232\u003c\/p\u003e \u003cp\u003e8.3 Correlation 254\u003c\/p\u003e \u003cp\u003e8.4 Miscellaneous Uses of Regression 256\u003c\/p\u003e \u003cp\u003e8.5 Summary 264\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003eExercises 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Multiple Regression 276\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 How Do We Start? 277\u003c\/p\u003e \u003cp\u003e9.2 Interpreting Regression Coefficients 278\u003c\/p\u003e \u003cp\u003e9.3 Example with Fixed Regressors 279\u003c\/p\u003e \u003cp\u003e9.4 Example with Random Regressors 281\u003c\/p\u003e \u003cp\u003e9.5 Example of Section 8.2.4 Extended 291\u003c\/p\u003e \u003cp\u003e9.6 Selecting Regression Variables 293\u003c\/p\u003e \u003cp\u003e9.7 Transformations 299\u003c\/p\u003e \u003cp\u003e9.8 Indicator Variables 300\u003c\/p\u003e \u003cp\u003e9.9 Regression Graphics 300\u003c\/p\u003e \u003cp\u003e9.10 Logistic Regression and Nonlinear Regression Models 301\u003c\/p\u003e \u003cp\u003e9.11 Regression with Matrix Algebra 302\u003c\/p\u003e \u003cp\u003e9.12 Summary 302\u003c\/p\u003e \u003cp\u003eReferences 303\u003c\/p\u003e \u003cp\u003eExercises 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Mechanistic Models 314\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Mechanistic Models 315\u003c\/p\u003e \u003cp\u003e10.2 Empirical–Mechanistic Models 316\u003c\/p\u003e \u003cp\u003e10.3 Additional Examples 324\u003c\/p\u003e \u003cp\u003e10.4 Software 325\u003c\/p\u003e \u003cp\u003e10.5 Summary 326\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003eExercises 327\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Control Charts and Quality Improvement 330\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Basic Control Chart Principles 330\u003c\/p\u003e \u003cp\u003e11.2 Stages of Control Chart Usage 331\u003c\/p\u003e \u003cp\u003e11.3 Assumptions and Methods of Determining Control Limits 334\u003c\/p\u003e \u003cp\u003e11.4 Control Chart Properties 335\u003c\/p\u003e \u003cp\u003e11.5 Types of Charts 336\u003c\/p\u003e \u003cp\u003e11.6 Shewhart Charts for Controlling a Process Mean and Variability (Without Subgrouping) 336\u003c\/p\u003e \u003cp\u003e11.7 Shewhart Charts for Controlling a Process Mean and Variability (With Subgrouping) 344\u003c\/p\u003e \u003cp\u003e11.8 Important Use of Control Charts for Measurement Data 349\u003c\/p\u003e \u003cp\u003e11.9 Shewhart Control Charts for Nonconformities and Nonconforming Units 349\u003c\/p\u003e \u003cp\u003e11.10 Alternatives to Shewhart Charts 356\u003c\/p\u003e \u003cp\u003e11.11 Finding Assignable Causes 359\u003c\/p\u003e \u003cp\u003e11.12 Multivariate Charts 362\u003c\/p\u003e \u003cp\u003e11.13 Case Study 362\u003c\/p\u003e \u003cp\u003e11.14 Engineering Process Control 364\u003c\/p\u003e \u003cp\u003e11.15 Process Capability 365\u003c\/p\u003e \u003cp\u003e11.16 Improving Quality with Designed Experiments 366\u003c\/p\u003e \u003cp\u003e11.17 Six Sigma 367\u003c\/p\u003e \u003cp\u003e11.18 Acceptance Sampling 368\u003c\/p\u003e \u003cp\u003e11.19 Measurement Error 368\u003c\/p\u003e \u003cp\u003e11.20 Summary 368\u003c\/p\u003e \u003cp\u003eReferences 369\u003c\/p\u003e \u003cp\u003eExercises 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Design and Analysis of Experiments 382\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Processes Must be in Statistical Control 383\u003c\/p\u003e \u003cp\u003e12.2 One-Factor Experiments 384\u003c\/p\u003e \u003cp\u003e12.3 One Treatment Factor and at Least One Blocking Factor 392\u003c\/p\u003e \u003cp\u003e12.4 More Than One Factor 395\u003c\/p\u003e \u003cp\u003e12.5 Factorial Designs 396\u003c\/p\u003e \u003cp\u003e12.6 Crossed and Nested Designs 405\u003c\/p\u003e \u003cp\u003e12.7 Fixed and Random Factors 406\u003c\/p\u003e \u003cp\u003e12.8 ANOM for Factorial Designs 407\u003c\/p\u003e \u003cp\u003e12.9 Fractional Factorials 409\u003c\/p\u003e \u003cp\u003e12.10 Split-Plot Designs 413\u003c\/p\u003e \u003cp\u003e12.11 Response Surface Designs 414\u003c\/p\u003e \u003cp\u003e12.12 Raw Form Analysis Versus Coded Form Analysis 415\u003c\/p\u003e \u003cp\u003e12.13 Supersaturated Designs 416\u003c\/p\u003e \u003cp\u003e12.14 Hard-to-Change Factors 416\u003c\/p\u003e \u003cp\u003e12.15 One-Factor-at-a-Time Designs 417\u003c\/p\u003e \u003cp\u003e12.16 Multiple Responses 418\u003c\/p\u003e \u003cp\u003e12.17 Taguchi Methods of Design 419\u003c\/p\u003e \u003cp\u003e12.18 Multi-Vari Chart 420\u003c\/p\u003e \u003cp\u003e12.19 Design of Experiments for Binary Data 420\u003c\/p\u003e \u003cp\u003e12.20 Evolutionary Operation (EVOP) 421\u003c\/p\u003e \u003cp\u003e12.21 Measurement Error 422\u003c\/p\u003e \u003cp\u003e12.22 Analysis of Covariance 422\u003c\/p\u003e \u003cp\u003e12.23 Summary of MINITAB and Design-Expert\u003csup\u003e®\u003c\/sup\u003e Capabilities for Design of Experiments 422\u003c\/p\u003e \u003cp\u003e12.24 Training for Experimental Design Use 423\u003c\/p\u003e \u003cp\u003e12.25 Summary 423\u003c\/p\u003e \u003cp\u003eAppendix A Computing Formulas 424\u003c\/p\u003e \u003cp\u003eAppendix B Relationship Between Effect Estimates and\u003c\/p\u003e \u003cp\u003eRegression Coefficients 426\u003c\/p\u003e \u003cp\u003eReferences 426\u003c\/p\u003e \u003cp\u003eExercises 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Measurement System Appraisal 441\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Terminology 442\u003c\/p\u003e \u003cp\u003e13.2 Components of Measurement Variability 443\u003c\/p\u003e \u003cp\u003e13.3 Graphical Methods 449\u003c\/p\u003e \u003cp\u003e13.4 Bias and Calibration 449\u003c\/p\u003e \u003cp\u003e13.5 Propagation of Error 454\u003c\/p\u003e \u003cp\u003e13.6 Software 455\u003c\/p\u003e \u003cp\u003e13.7 Summary 456\u003c\/p\u003e \u003cp\u003eReferences 456\u003c\/p\u003e \u003cp\u003eExercises 457\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Reliability Analysis and Life Testing 460\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Basic Reliability Concepts 461\u003c\/p\u003e \u003cp\u003e14.2 Nonrepairable and Repairable Populations 463\u003c\/p\u003e \u003cp\u003e14.3 Accelerated Testing 463\u003c\/p\u003e \u003cp\u003e14.4 Types of Reliability Data 466\u003c\/p\u003e \u003cp\u003e14.5 Statistical Terms and Reliability Models 467\u003c\/p\u003e \u003cp\u003e14.6 Reliability Engineering 473\u003c\/p\u003e \u003cp\u003e14.7 Example 474\u003c\/p\u003e \u003cp\u003e14.8 Improving Reliability with Designed Experiments 474\u003c\/p\u003e \u003cp\u003e14.9 Confidence Intervals 477\u003c\/p\u003e \u003cp\u003e14.10 Sample Size Determination 478\u003c\/p\u003e \u003cp\u003e14.11 Reliability Growth and Demonstration Testing 479\u003c\/p\u003e \u003cp\u003e14.12 Early Determination of Product Reliability 480\u003c\/p\u003e \u003cp\u003e14.13 Software 480\u003c\/p\u003e \u003cp\u003e14.14 Summary 481\u003c\/p\u003e \u003cp\u003eReferences 481\u003c\/p\u003e \u003cp\u003eExercises 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Analysis of Categorical Data 487\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Contingency Tables 487\u003c\/p\u003e \u003cp\u003e15.2 Design of Experiments: Categorical Response Variable 497\u003c\/p\u003e \u003cp\u003e15.3 Goodness-of-Fit Tests 498\u003c\/p\u003e \u003cp\u003e15.4 Summary 500\u003c\/p\u003e \u003cp\u003eReferences 500\u003c\/p\u003e \u003cp\u003eExercises 501\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Distribution-Free Procedures 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 507\u003c\/p\u003e \u003cp\u003e16.2 One-Sample Procedures 508\u003c\/p\u003e \u003cp\u003e16.3 Two-Sample Procedures 512\u003c\/p\u003e \u003cp\u003e16.4 Nonparametric Analysis of Variance 514\u003c\/p\u003e \u003cp\u003e16.5 Exact Versus Approximate Tests 519\u003c\/p\u003e \u003cp\u003e16.6 Nonparametric Regression 519\u003c\/p\u003e \u003cp\u003e16.7 Nonparametric Prediction Intervals and Tolerance Intervals 521\u003c\/p\u003e \u003cp\u003e16.8 Summary 521\u003c\/p\u003e \u003cp\u003eReferences 521\u003c\/p\u003e \u003cp\u003eExercises 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Tying It All Together 525\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Review of Book 525\u003c\/p\u003e \u003cp\u003e17.2 The Future 527\u003c\/p\u003e \u003cp\u003e17.3 Engineering Applications of Statistical Methods 528\u003c\/p\u003e \u003cp\u003eReference 528\u003c\/p\u003e \u003cp\u003eExercises 528\u003c\/p\u003e \u003cp\u003eAnswers to Selected Excercises 533\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Statistical Tables 562\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTable A Random Numbers 562\u003c\/p\u003e \u003cp\u003eTable B Normal Distribution 564\u003c\/p\u003e \u003cp\u003eTable C \u003ci\u003et\u003c\/i\u003e-Distribution 566\u003c\/p\u003e \u003cp\u003eTable D \u003ci\u003eF\u003c\/i\u003e-Distribution 567\u003c\/p\u003e \u003cp\u003eTable E Factors for Calculating Two-Sided 99% Statistical Intervals for a Normal Population to Contain at Least 100\u003ci\u003ep\u003c\/i\u003e% of the Population 570\u003c\/p\u003e \u003cp\u003eTable F Control Chart Constants 571\u003c\/p\u003e \u003cp\u003eAuthor Index 573\u003c\/p\u003e \u003cp\u003eSubject Index 579\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402280739159,"sku":"9780470081877","price":147.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470081877.jpg?v=1730479932"},{"product_id":"mathematical-methods-in-biology-pure-and-applied-mathematics-a-wiley-series-of-texts-monographs-and-tracts-9780470525876","title":"Mathematical Methods in Biology Pure and Applied","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMathematical Methods in Biology  uniquely covers both deterministic and probabilistic models, including algorithms in the MATLAB platform. The book focuses mostly in one area of the life sciences, focusing mainly on theoretical ecology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Admirably, the volume is written with bits of MATLAB code inserted at appropriate places and has exercises interspersed throughout the text (as well as hints for solutions to the exercises at the end of the book).\" The Quarterly Review of Biology, June 2010)  \u003cp\u003e \"The mathematical and reasoning sophistication increases as the chapters proceed.\" \u003ci\u003e(Book News,\u003c\/i\u003e December 2009)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e\u003cb\u003e1. Introduction To Ecological Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Mathematical Models.\u003c\/p\u003e \u003cp\u003e1.2 Rates of Change.\u003c\/p\u003e \u003cp\u003e1.3 Balance Laws.\u003c\/p\u003e \u003cp\u003e1.4 Temperature in the Environment.\u003c\/p\u003e \u003cp\u003e1.5 Dimensionless Variables.\u003c\/p\u003e \u003cp\u003e1.6 Descriptive Statistics.\u003c\/p\u003e \u003cp\u003e1.7 Regression and Curve Fitting.\u003c\/p\u003e \u003cp\u003e1.8 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Population Dynamics for Single Species.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Laws of Population Dynamics.\u003c\/p\u003e \u003cp\u003e2.2 Continuous Time Models.\u003c\/p\u003e \u003cp\u003e2.3 Qualitative Analysis of Population Models.\u003c\/p\u003e \u003cp\u003e2.4 Dynamics of Predation.\u003c\/p\u003e \u003cp\u003e2.5 Discrete Time Models.\u003c\/p\u003e \u003cp\u003e2.6 Equilibria, Stability, and Chaos.\u003c\/p\u003e \u003cp\u003e2.7 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Structure and Interacting Populations\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e3.1 Structure--Juveniles and Adults.\u003c\/p\u003e \u003cp\u003e3.2 Structured Linear Models.\u003c\/p\u003e \u003cp\u003e3.3 Nonlinear Interactions.\u003c\/p\u003e \u003cp\u003e3.4 Appendix--Matrices.\u003c\/p\u003e \u003cp\u003e3.5 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Interactions in Continuous Time.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Interacting Populations.\u003c\/p\u003e \u003cp\u003e4.2 Phase Plane Analysis.\u003c\/p\u003e \u003cp\u003e4.3 Linear Systems.\u003c\/p\u003e \u003cp\u003e4.4 Nonlinear Systems.\u003c\/p\u003e \u003cp\u003e4.5 Bifurcation.\u003c\/p\u003e \u003cp\u003e4.6 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Concepts of Probability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introductory Examples and Definitions.\u003c\/p\u003e \u003cp\u003e5.2 The Hardy-Weinberg Law.\u003c\/p\u003e \u003cp\u003e5.3 Continuous Random Variables.\u003c\/p\u003e \u003cp\u003e5.4 Discrete Random Variables.\u003c\/p\u003e \u003cp\u003e5.5 Joint Probability Distributions.\u003c\/p\u003e \u003cp\u003e5.6 Covariance and Correlation.\u003c\/p\u003e \u003cp\u003e5.7 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Statistical Inference\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Interval Analysis.\u003c\/p\u003e \u003cp\u003e6.3 Estimating Proportions.\u003c\/p\u003e \u003cp\u003e6.4 The Chi-Squared Test.\u003c\/p\u003e \u003cp\u003e6.5 Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e6.6 Bootstrap Methods.\u003c\/p\u003e \u003cp\u003e6.7 Reference Notes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Stochastic Processes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Randomizing Discrete Dynamics.\u003c\/p\u003e \u003cp\u003e7.3 Random Walk.\u003c\/p\u003e \u003cp\u003e7.4 Birth Processes.\u003c\/p\u003e \u003cp\u003e7.5 Stochastic Differential Equations.\u003c\/p\u003e \u003cp\u003e7.6 SDEs from Markov Models.\u003c\/p\u003e \u003cp\u003e7.7 Solving SDEs.\u003c\/p\u003e \u003cp\u003e7.8 The Fokker-Planck Equation.\u003c\/p\u003e \u003cp\u003e7.9 Reference Notes.\u003c\/p\u003e \u003cp\u003eA. Hints and Solutions to Exercises\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402354630999,"sku":"9780470525876","price":79.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470525876.jpg?v=1730480158"},{"product_id":"statistical-methods-in-practice-9780470746646","title":"Statistical Methods in Practice","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis is a practical book on how to apply statistical methods successfully. The Authors have deliberately kept formulae to a minimum to enable the reader to concentrate on how to use the methods and to understand what the methods are for. Each method is introduced and used in a real situation from industry or research.  \u003cp\u003eEach chapter features situations based on the authors' experience and looks at statistical methods for analysing data and, where appropriate, discusses the assumptions of these methods.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eProvides a practical hands-on manual for workplace applications.\u003c\/li\u003e \u003cli\u003eIntroduces a broad range of statistical methods from confidence intervals to trend analysis.\u003c\/li\u003e \u003cli\u003eCombines realistic case studies and examples with a practical approach to statistical analysis.\u003c\/li\u003e \u003cli\u003eFeatures examples drawn from a wide range of industries including chemicals, petrochemicals, nuclear power, food and pharmaceuticals.\u003c\/li\u003e \u003cli\u003eIncludes a supporting \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Overall, the book could be a clear introduction to a set of useful tools either in self study or used as an aid for instruction for those with no previous exposure.\" (The American Statistician, 1 February 2011) \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Samples and populations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eWhat a lottery!\u003c\/p\u003e \u003cp\u003eNo can do.\u003c\/p\u003e \u003cp\u003eNobody is listening to me.\u003c\/p\u003e \u003cp\u003eHow clean is my river?\u003c\/p\u003e \u003cp\u003eDiscussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 What is the true mean?\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003ePresenting data.\u003c\/p\u003e \u003cp\u003eAverages.\u003c\/p\u003e \u003cp\u003eMeasures of variability.\u003c\/p\u003e \u003cp\u003eRelative standard deviation .\u003c\/p\u003e \u003cp\u003eDegrees of freedom.\u003c\/p\u003e \u003cp\u003eConfidence interval for the population mean.\u003c\/p\u003e \u003cp\u003eSample sizes.\u003c\/p\u003e \u003cp\u003eHow much moisture is in the raw material?\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Exploratory data analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eHistograms: is the process capable of meeting specifications?\u003c\/p\u003e \u003cp\u003eBox plots: how long before the lights go out?\u003c\/p\u003e \u003cp\u003eThe box plot in practice.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Significance testing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eThe one-sample \u003ci\u003et\u003c\/i\u003e -test.\u003c\/p\u003e \u003cp\u003eThe significance testing procedure.\u003c\/p\u003e \u003cp\u003eConfidence intervals as an alternative to significance testing.\u003c\/p\u003e \u003cp\u003eConfidence interval for the population standard deviation.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eF\u003c\/i\u003e-test for ratio of standard deviations.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The normal distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eProperties of the normal distribution.\u003c\/p\u003e \u003cp\u003eExample.\u003c\/p\u003e \u003cp\u003eSetting the process mean.\u003c\/p\u003e \u003cp\u003eChecking for normality.\u003c\/p\u003e \u003cp\u003eUses of the normal distribution.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Tolerance intervals.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eExample.\u003c\/p\u003e \u003cp\u003eConfidence intervals and tolerance intervals.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Outliers.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eGrubbs’ test.\u003c\/p\u003e \u003cp\u003eWarning.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Significance tests for comparing two means.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eExample: watching paint lose its gloss.\u003c\/p\u003e \u003cp\u003eThe two-sample \u003ci\u003et\u003c\/i\u003e -test for independent samples.\u003c\/p\u003e \u003cp\u003eAn alternative approach: a confidence intervals for the difference between population means.\u003c\/p\u003e \u003cp\u003eSample size to estimate the difference between two means.\u003c\/p\u003e \u003cp\u003eA production example.\u003c\/p\u003e \u003cp\u003eConfidence intervals for the difference between the two suppliers.\u003c\/p\u003e \u003cp\u003eSample size to estimate the difference between two means.\u003c\/p\u003e \u003cp\u003eConclusions.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Significance tests for comparing paired measurements.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eComparing two fabrics.\u003c\/p\u003e \u003cp\u003eThe wrong way.\u003c\/p\u003e \u003cp\u003eThe paired sample \u003ci\u003et\u003c\/i\u003e -test.\u003c\/p\u003e \u003cp\u003ePresenting the results of significance tests.\u003c\/p\u003e \u003cp\u003eOne-sided significance tests.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Regression and correlation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eObtaining the best straight line.\u003c\/p\u003e \u003cp\u003eConfidence intervals for the regression statistics.\u003c\/p\u003e \u003cp\u003eExtrapolation of the regression line.\u003c\/p\u003e \u003cp\u003eCorrelation coefficient.\u003c\/p\u003e \u003cp\u003eIs there a significant relationship between the variables?\u003c\/p\u003e \u003cp\u003eHow good a fit is the line to the data?\u003c\/p\u003e \u003cp\u003eAssumptions.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The binomial distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eExample.\u003c\/p\u003e \u003cp\u003eAn exact binomial test.\u003c\/p\u003e \u003cp\u003eA quality assurance example.\u003c\/p\u003e \u003cp\u003eWhat is the effect of the batch size?\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Poisson distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eFitting a Poisson distribution.\u003c\/p\u003e \u003cp\u003eAre the defects random? The Poisson distribution.\u003c\/p\u003e \u003cp\u003ePoisson dispersion test.\u003c\/p\u003e \u003cp\u003eConfidence intervals for a Poisson count.\u003c\/p\u003e \u003cp\u003eA significance test for two Poisson counts.\u003c\/p\u003e \u003cp\u003eHow many black specks are in the batch?\u003c\/p\u003e \u003cp\u003eHow many pathogens are there in the batch?\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 The chi-squared test for contingency tables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eTwo-sample test for percentages.\u003c\/p\u003e \u003cp\u003eComparing several percentages.\u003c\/p\u003e \u003cp\u003eWhere are the differences?\u003c\/p\u003e \u003cp\u003eAssumptions.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Non-parametric statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eDescriptive statistics.\u003c\/p\u003e \u003cp\u003eA test for two independent samples: Wilcoxon–Mann–Whitney test.\u003c\/p\u003e \u003cp\u003eA test for paired data: Wilcoxon matched-pairs sign test.\u003c\/p\u003e \u003cp\u003eWhat type of data can be used?\u003c\/p\u003e \u003cp\u003eExample: cracking shoes.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Analysis of variance: Components of variability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eOverall variability.\u003c\/p\u003e \u003cp\u003eAnalysis of variance.\u003c\/p\u003e \u003cp\u003eA practical example.\u003c\/p\u003e \u003cp\u003eTerminology.\u003c\/p\u003e \u003cp\u003eCalculations.\u003c\/p\u003e \u003cp\u003eSignificance test.\u003c\/p\u003e \u003cp\u003eVariation less than chance?\u003c\/p\u003e \u003cp\u003eWhen should the above methods \u003ci\u003enot\u003c\/i\u003e be used?\u003c\/p\u003e \u003cp\u003eBetween- and within-batch variability.\u003c\/p\u003e \u003cp\u003eHow many batches and how many prawns should be sampled?\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Cusum analysis for detecting process changes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eAnalysing past data.\u003c\/p\u003e \u003cp\u003eIntensity.\u003c\/p\u003e \u003cp\u003eLocalised standard deviation.\u003c\/p\u003e \u003cp\u003eSignificance test.\u003c\/p\u003e \u003cp\u003eYield.\u003c\/p\u003e \u003cp\u003eConclusions from the analysis.\u003c\/p\u003e \u003cp\u003eProblem.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Rounding of results.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eChoosing the rounding scale.\u003c\/p\u003e \u003cp\u003eReporting purposes: deciding the amount of rounding.\u003c\/p\u003e \u003cp\u003eReporting purposes: rounding of means and standard deviations.\u003c\/p\u003e \u003cp\u003eRecording the original data and using means and standard deviations in statistical analysis.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSolutions to Problems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eStatistical Tables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex\u003c\/b\u003e.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402424623447,"sku":"9780470746646","price":36.05,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470746646.jpg?v=1730480359"},{"product_id":"sensitivity-analysis-in-practice-9780470870938","title":"Sensitivity Analysis in Practice","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB  a widely distributed freely-available sensitivity analysis software package developed by the authors  for solving problems in sensitivity analysis of statistical models.  \u003cp\u003eOther key features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides an accessible overview of the current most widely used methods for sensitivity analysis.\u003c\/li\u003e \u003cli\u003eOpens with a detailed worked example to explain the motivation behind the book.\u003c\/li\u003e \u003cli\u003eIncludes a range of examples to help illustrate the c\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...an interesting and informative book...\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, May 2005)  \u003cp\u003e\"...provides an accessible overview of the most widely used sensitivity analysis methods.\" (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol.1049, 2004)\u003c\/p\u003e \u003cp\u003e\"...well written...\" (Statistical Methods in Medical Research, Vol 14 2005)\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePREFACE.  \u003cp\u003e\u003cb\u003e1. A WORKED EXAMPLE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A simple model.\u003c\/p\u003e \u003cp\u003e1.2 Modulus version of the simple model.\u003c\/p\u003e \u003cp\u003e1.3 Six-factor version of the simple model.\u003c\/p\u003e \u003cp\u003e1.4 The simple model ‘by groups’.\u003c\/p\u003e \u003cp\u003e1.5 The (less) simple correlated-input model.\u003c\/p\u003e \u003cp\u003e1.6 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. GLOBAL SENSITIVITY ANALYSIS FOR IMPORTANCE ASSESSMENT.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Examples at a glance.\u003c\/p\u003e \u003cp\u003e2.2 What is sensitivity analysis?\u003c\/p\u003e \u003cp\u003e2.3 Properties of an ideal sensitivity analysis method.\u003c\/p\u003e \u003cp\u003e2.4 Defensible settings for sensitivity analysis.\u003c\/p\u003e \u003cp\u003e2.5 Caveats.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. TEST CASES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The jumping man. Applying variance-based methods.\u003c\/p\u003e \u003cp\u003e3.2 Handling the risk of a financial portfolio: the problem of hedging. Applying Monte Carlo filtering and variance-based methods.\u003c\/p\u003e \u003cp\u003e3.3 A model of fish population dynamics. Applying the method of Morris.\u003c\/p\u003e \u003cp\u003e3.4 The Level E model. Radionuclide migration in the geosphere. Applying variance-based methods and Monte Carlo filtering.\u003c\/p\u003e \u003cp\u003e3.5 Two spheres. Applying variance based methods in estimation\/calibration problems.\u003c\/p\u003e \u003cp\u003e3.6 A chemical experiment. Applying variance based methods in estimation\/calibration problems.\u003c\/p\u003e \u003cp\u003e3.7 An analytical example. Applying the method of Morris.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. THE SCREENING EXERCISE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 The method of Morris.\u003c\/p\u003e \u003cp\u003e4.3 Implementing the method.\u003c\/p\u003e \u003cp\u003e4.4 Putting the method to work: an analytical example.\u003c\/p\u003e \u003cp\u003e4.5 Putting the method to work: sensitivity analysis of a fish population model.\u003c\/p\u003e \u003cp\u003e4.6 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. METHODS BASED ON DECOMPOSING THE VARIANCE OF THE OUTPUT.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The settings.\u003c\/p\u003e \u003cp\u003e5.2 Factors Prioritisation Setting.\u003c\/p\u003e \u003cp\u003e5.3 First-order effects and interactions.\u003c\/p\u003e \u003cp\u003e5.4 Application of \u003ci\u003eS\u003c\/i\u003e\u003ci\u003ei\u003c\/i\u003e to Setting ‘Factors Prioritisation’.\u003c\/p\u003e \u003cp\u003e5.5 More on variance decompositions.\u003c\/p\u003e \u003cp\u003e5.6 Factors Fixing (FF) Setting.\u003c\/p\u003e \u003cp\u003e5.7 Variance Cutting (VC) Setting.\u003c\/p\u003e \u003cp\u003e5.8 Properties of the variance based methods.\u003c\/p\u003e \u003cp\u003e5.9 How to compute the sensitivity indices: the case of orthogonal input.\u003c\/p\u003e \u003cp\u003e5.9.1 A digression on the Fourier Amplitude Sensitivity Test (FAST).\u003c\/p\u003e \u003cp\u003e5.10 How to compute the sensitivity indices: the case of non-orthogonal input.\u003c\/p\u003e \u003cp\u003e5.11 Putting the method to work: the Level E model.\u003c\/p\u003e \u003cp\u003e5.11.1 Case of orthogonal input factors.\u003c\/p\u003e \u003cp\u003e5.11.2 Case of correlated input factors.\u003c\/p\u003e \u003cp\u003e5.12 Putting the method to work: the bungee jumping model.\u003c\/p\u003e \u003cp\u003e5.13 Caveats.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. SENSITIVITY ANALYSIS IN DIAGNOSTIC MODELLING: MONTE CARLO FILTERING AND REGIONALISED SENSITIVITY ANALYSIS, BAYESIAN UNCERTAINTY ESTIMATION AND GLOBAL SENSITIVITY ANALYSIS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Model calibration and Factors Mapping Setting.\u003c\/p\u003e \u003cp\u003e6.2 Monte Carlo filtering and regionalised sensitivity analysis.\u003c\/p\u003e \u003cp\u003e6.2.1 Caveats.\u003c\/p\u003e \u003cp\u003e6.3 Putting MC filtering and RSA to work: the problem of hedging a financial portfolio.\u003c\/p\u003e \u003cp\u003e6.4 Putting MC filtering and RSA to work: the Level E test case.\u003c\/p\u003e \u003cp\u003e6.5 Bayesian uncertainty estimation and global sensitivity analysis.\u003c\/p\u003e \u003cp\u003e6.5.1 Bayesian uncertainty estimation.\u003c\/p\u003e \u003cp\u003e6.5.2 The GLUE case.\u003c\/p\u003e \u003cp\u003e6.5.3 Using global sensitivity analysis in the Bayesian uncertainty estimation.\u003c\/p\u003e \u003cp\u003e6.5.4 Implementation of the method.\u003c\/p\u003e \u003cp\u003e6.6 Putting Bayesian analysis and global SA to work: two spheres.\u003c\/p\u003e \u003cp\u003e6.7 Putting Bayesian analysis and global SA to work: a chemical experiment.\u003c\/p\u003e \u003cp\u003e6.7.1 Bayesian uncertainty analysis (GLUE case).\u003c\/p\u003e \u003cp\u003e6.7.2 Global sensitivity analysis.\u003c\/p\u003e \u003cp\u003e6.7.3 Correlation analysis.\u003c\/p\u003e \u003cp\u003e6.7.4 Further analysis by varying temperature in the data set: fewer interactions in the model.\u003c\/p\u003e \u003cp\u003e6.8 Caveats.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. HOW TO USE SIMLAB.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 How to obtain and install SIMLAB.\u003c\/p\u003e \u003cp\u003e7.3 SIMLAB main panel.\u003c\/p\u003e \u003cp\u003e7.4 Sample generation.\u003c\/p\u003e \u003cp\u003e7.4.1 FAST.\u003c\/p\u003e \u003cp\u003e7.4.2 Fixed sampling.\u003c\/p\u003e \u003cp\u003e7.4.3 Latin hypercube sampling (LHS).\u003c\/p\u003e \u003cp\u003e7.4.4 The method of Morris.\u003c\/p\u003e \u003cp\u003e7.4.5 Quasi-Random LpTau.\u003c\/p\u003e \u003cp\u003e7.4.6 Random.\u003c\/p\u003e \u003cp\u003e7.4.7 Replicated Latin Hypercube (r-LHS).\u003c\/p\u003e \u003cp\u003e7.4.8 The method of Sobol’.\u003c\/p\u003e \u003cp\u003e7.4.9 How to induce dependencies in the input factors.\u003c\/p\u003e \u003cp\u003e7.5 How to execute models.\u003c\/p\u003e \u003cp\u003e7.6 Sensitivity analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. FAMOUS QUOTES: SENSITIVITY ANALYSIS IN THE SCIENTIFIC DISCOURSE.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eREFERENCES.\u003c\/p\u003e \u003cp\u003eINDEX.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402449166679,"sku":"9780470870938","price":67.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470870938.jpg?v=1730480430"},{"product_id":"finite-mixture-models-299-wiley-series-in-probability-and-statistics-9780471006268","title":"Finite Mixture Models 299 Wiley Series in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFinite mixture models are typically used where the population being studied is heterogeneous in composition. This work aims to offer an up-to-date account of the major issues involved with finite modelling. There is a practical emphasis on the applications of mixture models.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This is an excellent book.... I enjoyed reading this book. I recommend it highly to both mathematical and applied statisticians.\" (Technometrics, February 2002)\u003cbr\u003e \"This book will become popular to many researchers...the material covered is so wide that it will make this book a standard reference for the forthcoming years.\" (Zentralblatt MATH, Vol. 963, 2001\/13)\u003cbr\u003e \"the material covered is so wide that it will make this book a standard reference for the forthcoming years.\" (Zentralblatt MATH, Vol.963, No.13, 2001)\u003cbr\u003e \"This book is excellent reading...should also serve as an excellent handbook on mixture modelling...\" (Mathematical Reviews, 2002b)\u003cbr\u003e \"...contains valuable information about mixtures for researchers...\" (Journal of Mathematical Psychology, 2002)\u003cbr\u003e \"...a masterly overview of the area...It is difficult to ask for more and there is no doubt that McLachlan and Peel's book will be the standard reference on mixture models for many years to come.\" (Statistical Methods in Medical Research, Vol. 11, 2002)\u003cbr\u003e \"...they are to be congratulated on the extent of their achievement...\" (The Statistician, Vol.51, No.3)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eGeneral Introduction.\u003cbr\u003e \u003cbr\u003e ML Fitting of Mixture Models.\u003cbr\u003e \u003cbr\u003e Multivariate Normal Mixtures.\u003cbr\u003e \u003cbr\u003e Bayesian Approach to Mixture Analysis.\u003cbr\u003e \u003cbr\u003e Mixtures with Nonnormal Components.\u003cbr\u003e \u003cbr\u003e Assessing the Number of Components in Mixture Models.\u003cbr\u003e \u003cbr\u003e Multivariate t Mixtures.\u003cbr\u003e \u003cbr\u003e Mixtures of Factor Analyzers.\u003cbr\u003e \u003cbr\u003e Fitting Mixture Models to Binned Data.\u003cbr\u003e \u003cbr\u003e Mixture Models for Failure-Time Data.\u003cbr\u003e \u003cbr\u003e Mixture Analysis of Directional Data.\u003cbr\u003e \u003cbr\u003e Variants of the EM Algorithm for Large Databases.\u003cbr\u003e \u003cbr\u003e Hidden Markov Models.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Indexes.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402476200279,"sku":"9780471006268","price":150.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471006268.jpg?v=1730480520"},{"product_id":"applied-population-ecology-9780471135869","title":"Applied Population Ecology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides applied biologists and ecologists with the mathematical tools they need to understand the ever increasingly mathematical and complex area of population ecology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSampling in Applied Population Ecology.\u003cbr\u003e \u003cbr\u003e The Role of Abiotic Factors.\u003cbr\u003e \u003cbr\u003e Life Tables.\u003cbr\u003e \u003cbr\u003e Resource Acquisition in Predator-Prey Systems.\u003cbr\u003e \u003cbr\u003e Resource Acquisition and Allocation.\u003cbr\u003e \u003cbr\u003e MODELING: A PREVIEW.\u003cbr\u003e \u003cbr\u003e Simple Single-Species Models.\u003cbr\u003e \u003cbr\u003e Simple Models of Multitropic Interactions.\u003cbr\u003e \u003cbr\u003e Single-Species Models with Age Structure.\u003cbr\u003e \u003cbr\u003e Realistic Age-Structured Multitrophic Models.\u003cbr\u003e \u003cbr\u003e Regional Dynamics.\u003cbr\u003e \u003cbr\u003e Ecosystem Sustainability.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Indexes.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402495631703,"sku":"9780471135869","price":197.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471135869.jpg?v=1730480585"},{"product_id":"mathematical-methods-for-oceanographers-an-introduction-9780471162216","title":"Mathematical Methods for Oceanographers An","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOceanography calls for a wide variety of mathematical and statistical techniques. This title provides the basics oceanographers need to know, including: practical ways to deal with chemical, geological, and biological oceanographic data; and instructions on detecting the existence of patterns in what appears to be noise.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"...It presents many well discussed and illustrative examples...\" (Zentralblatt Math, Vol.988, No.13, 2002)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eCalculus Review.\u003cbr\u003e \u003cbr\u003e Model I Linear Regression.\u003cbr\u003e \u003cbr\u003e Correlation.\u003cbr\u003e \u003cbr\u003e Model II Linear Regression.\u003cbr\u003e \u003cbr\u003e Polynomial Curve Fitting, Linear Multiple Regression Analysis, andNonlinear Least Squares.\u003cbr\u003e \u003cbr\u003e Numerical Integration.\u003cbr\u003e \u003cbr\u003e Box Models.\u003cbr\u003e \u003cbr\u003e Time Series Analysis.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Answers to Exercises.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402506051927,"sku":"9780471162216","price":148.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471162216.jpg?v=1730480607"}],"url":"https:\/\/bookcurl.com\/collections\/maths-for-scientists.oembed?page=3","provider":"Book Curl","version":"1.0","type":"link"}