Mathematics Books

Book SynopsisTable of ContentsPreface. To the Student. Diagnostic Tests. A Preview of Calculus. 1. FUNCTIONS AND MODELS. Four Ways to Represent a Function. Mathematical Models: A Catalog of Essential Functions. New Functions from Old Functions. Exponential Functions. Inverse Functions and Logarithms. Review. Principles of Problem Solving. 2. LIMITS AND DERIVATIVES. The Tangent and Velocity Problems. The Limit of a Function. Calculating Limits Using the Limit Laws. The Precise Definition of a Limit. Continuity. Limits at Infinity; Horizontal Asymptotes. Derivatives and Rates of Change. Writing Project: Early Methods for Finding Tangents. The Derivative as a Function. Review. Problems Plus. 3. DIFFERENTIATION RULES. Derivatives of Polynomials and Exponential Functions. Applied Project: Building a Better Roller Coaster. The Product and Quotient Rules. Derivatives of Trigonometric Functions. The Chain Rule. Applied Project: Where Should a Pilot Start Descent? Implicit Differentiation. Discovery Project: Families of Implicit Curves. Derivatives of Logarithmic Functions and Inverse Trigonometric Functions. Rates of Change in the Natural and Social Sciences. Exponential Growth and Decay. Applied Project: Controlling Red Blood Cell Loss During Surgery. Related Rates. Linear Approximations and Differentials. Discovery Project: Taylor Polynomials. Hyperbolic Functions. Review. Problems Plus. 4. APPLICATIONS OF DIFFERENTIATION. Maximum and Minimum Values. Applied Project: The Calculus of Rainbows. The Mean Value Theorem. What Derivatives Tell Us about the Shape of a Graph. Indeterminate Forms and l'Hospital's Rule. Writing Project: The Origins of l'Hospital's Rule. Summary of Curve Sketching. Graphing with Calculus and Technology. Optimization Problems. Applied Project: The Shape of a Can. Applied Project: Planes and Birds: Minimizing Energy. Newton's Method. Antiderivatives. Review. Problems Plus. 5. INTEGRALS. The Area and Distance Problems. The Definite Integral. Discovery Project: Area Functions. The Fundamental Theorem of Calculus. Indefinite Integrals and the Net Change Theorem. Writing Project: Newton, Leibniz, and the Invention of Calculus. The Substitution Rule. Review. Problems Plus. 6. APPLICATIONS OF INTEGRATION. Areas Between Curves. Applied Project: The Gini Index. Volumes. Volumes by Cylindrical Shells. Work. Average Value of a Function. Applied Project: Calculus and Baseball. Applied Project: Where to Sit at the Movies. Review. Problems Plus. 7. TECHNIQUES OF INTEGRATION. Integration by Parts. Trigonometric Integrals. Trigonometric Substitution. Integration of Rational Functions by Partial Fractions. Strategy for Integration. Integration Using Tables and Technology. Discovery Project: Patterns in Integrals. Approximate Integration. Improper Integrals. Review. Problems Plus. 8. FURTHER APPLICATIONS OF INTEGRATION. Arc Length. Discovery Project: Arc Length Contest. Area of a Surface of Revolution. Discovery Project: Rotating on a Slant. Applications to Physics and Engineering. Discovery Project: Complementary Coffee Cups. Applications to Economics and Biology. Probability. Review. Problems Plus. 9. DIFFERENTIAL EQUATIONS. Modeling with Differential Equations. Direction Fields and Euler's Method. Separable Equations. Applied Project: How Fast Does a Tank Drain? Models for Population Growth. Linear Equations. Applied Project: Which is Faster, Going Up or Coming Down? Predator-Prey Systems. Review. Problems Plus. 10. PARAMETRIC EQUATIONS AND POLAR COORDINATES. Curves Defined by Parametric Equations. Discovery Project: Running Circles Around Circles. Calculus with Parametric Curves. Discovery Project: B��zier Curves. Polar Coordinates. Discovery Project: Families of Polar Curves. Calculus in Polar Coordinates. Conic Sections. Conic Sections in Polar Coordinates. Review. Problems Plus. 11. SEQUENCES, SERIES, AND POWER SERIES. Sequences. Discovery Project: Logistic Sequences. Series. The Integral Test and Estimates of Sums. The Comparison Tests. Alternating Series and Absolute Convergence. The Ratio and Root Tests. Strategy for Testing Series. Power Series. Representations of Functions as Power Series. Taylor and Maclaurin Series. Discovery Project: An Elusive Limit. Writing Project: How Newton Discovered the Binomial Series. Applications of Taylor Polynomials. Applied Project: Radiation from the Stars. Review. Problems Plus. APPENDIXES. A: Numbers, Inequalities, and Absolute Values. B: Coordinate Geometry and Lines. C: Graphs of Second-Degree Equations. D: Trigonometry. E: Sigma Notation. F: Proofs of Theorems. G: The Logarithm Defined as an Integral. H: Complex Numbers. I: Answers to Odd-Numbered Exercises. INDEX.

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Book SynopsisA must-read book on the quantitative value investment strategy Warren Buffett and Ed Thorp represent two spectrums of investing: one value driven, one quantitative. Where they align is in their belief that the market is beatable. This book seeks to take the best aspects of value investing and quantitative investing as disciplines and apply them to a completely unique approach to stock selection. Such an approach has several advantages over pure value or pure quantitative investing. This new investing strategy framed by the book is known as quantitative value, a superior, market-beating method to investing in stocks. Quantitative Value provides practical insights into an investment strategy that links the fundamental value investing philosophy of Warren Buffett with the quantitative value approach of Ed Thorp. It skillfully combines the best of Buffett and Ed Thorpweaving their investment philosophies into a winning, market-beating investment strategy. Table of ContentsPreface xi Acknowledgments xiii PART ONE The Foundation of Quantitative Value 1 CHAPTER 1 The Paradox of Dumb Money 3 Value Strategies Beat the Market 9 How Quantitative Investing Protects against Behavioral Errors 23 The Power of Quantitative Value Investing 30 Notes 32 CHAPTER 2 A Blueprint to a Better Quantitative Value Strategy 35 Greenblatt’s Magic Formula 36 It’s All Academic: Improving Quality and Price 45 Strategy Implementation: Investors Behaving Badly 54 Notes 59 PART TWO Margin of Safety—How to Avoid a Permanent Loss of Capital 61 CHAPTER 3 Hornswoggled! Eliminating Earnings Manipulators and Outright Frauds 63 Accruals and the Art of Earnings Manipulation 64 Predicting PROBMs 72 Notes 79 CHAPTER 4 Measuring the Risk of Financial Distress: How to Avoid the Sick Men of the Stock Market 81 A Brief History of Bankruptcy Prediction 83 Improving Bankruptcy Prediction 85 How We Calculate the Risk of Financial Distress 86 Scrubbing the Universe 89 Notes 91 PART THREE Quality—How to Find a Wonderful Business 93 CHAPTER 5 Franchises—The Archetype of High Quality 95 The Chairman’s Secret Recipe 96 How to Find a Franchise 99 Notes 112 CHAPTER 6 Financial Strength: Foundations Built on Rock 113 The Piotroski Fundamentals Score (F_SCORE) 114 Our Financial Strength Score (FS_SCORE) 119 Comparing the Performance of Piotroski’s F_SCORE and Our FS_SCORE 122 Case Study: Lubrizol Corporation 123 Notes 126 PART FOUR The Secret to Finding Bargain Prices 127 CHAPTER 7 Price Ratios: A Horse Race 129 The Horses in the Race 130 Rules of the Race 133 The Race Call 134 A Price Ratio for All Seasons 141 The Offi cial Winner 142 Notes 143 CHAPTER 8 Alternative Price Measures—Normalized Earning Power and Composite Ratios 145 Normalized Earning Power 147 Compound Price Ratios: Is the Whole Greater than the Sum of Its Parts? 150 Notes 163 PART FIVE Corroborative Signals 165 CHAPTER 9 Blue Horseshoe Loves Anacott Steel: Follow the Signals from the Smart Money 167 Stock Buybacks, Issuance, and Announcements 169 Insider Traders Beat the Market 173 Activism and Cloning 176 Short Money Is Smart Money 179 Notes 182 PART SIX Building and Testing the Model 185 CHAPTER 10 Bangladeshi Butter Production Predicts the S&P 500 Close 187 Sustainable Alpha: A Framework for Assessing Past Results 189 What’s the Big Idea? 191 Rigorously Test the Big Idea 196 The Parameters of the Universe 206 Notes 208 CHAPTER 11 Problems with the Magic Formula 211 Glamour Is Always a Bad Bet 216 Improving the Structure of a Quantitative Value Strategy 218 Our Final Quantitative Value Checklist 222 Notes 228 CHAPTER 12 Quantitative Value Beats the Market 229 Risk and Return 231 Robustness 239 A Peek Inside the Black Box 249 Man versus Machine 257 Beating the Market with Quantitative Value 262 Notes 264 Appendix: Analysis Legend 265 About the Authors 267 About the Companion Website 269 Index 271

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Book SynopsisCalculus For Dummies, 2nd Edition (9781119293491) was previously published as Calculus For Dummies, 2nd Edition (9781118791295). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product.Table of ContentsIntroduction 1 Part 1: An Overview of Calculus 5 Chapter 1: What Is Calculus? 7 Chapter 2: The Two Big Ideas of Calculus: Differentiation and Integration — plus Infinite Series 13 Chapter 3: Why Calculus Works 21 Part 2: Warming Up with Calculus Prerequisites 27 Chapter 4: Pre-Algebra and Algebra Review 29 Chapter 5: Funky Functions and Their Groovy Graphs 43 Chapter 6: The Trig Tango 61 Part 3: Limits 73 Chapter 7: Limits and Continuity 75 Chapter 8: Evaluating Limits 89 Part 4: Differentiation 105 Chapter 9: Differentiation Orientation 107 Chapter 10: Differentiation Rules — Yeah, Man, It Rules 127 Chapter 11: Differentiation and the Shape of Curves 147 Chapter 12: Your Problems Are Solved: Differentiation to the Rescue! 171 Chapter 13: More Differentiation Problems: Going Off on a Tangent 193 Part 5: Integration and Infinite Series 207 Chapter 14: Intro to Integration and Approximating Area 209 Chapter 15: Integration: It’s Backwards Differentiation 233 Chapter 16: Integration Techniques for Experts 263 Chapter 17: Forget Dr Phil: Use the Integral to Solve Problems 285 Chapter 18: Taming the Infinite with Improper Integrals 303 Chapter 19: Infinite Series 315 Part 6: The Part of Tens 339 Chapter 20: Ten Things to Remember 341 Chapter 21: Ten Things to Forget 345 Chapter 22: Ten Things You Can’t Get Away With 349 Index 353

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Book SynopsisThis book is for instructors who think that most calculus textbooks are too long. In writing the book, James Stewart asked himself: What is essential for a three-semester calculus course for scientists and engineers? ESSENTIAL CALCULUS, Second Edition, offers a concise approach to teaching calculus that focuses on major concepts, and supports those concepts with precise definitions, patient explanations, and carefully graded problems. The book is only 900 pages--two-thirds the size of Stewart's other calculus texts, and yet it contains almost all of the same topics. The author achieved this relative brevity primarily by condensing the exposition and by putting some of the features on the book's website, www.StewartCalculus.com. Despite the more compact size, the book has a modern flavor, covering technology and incorporating material to promote conceptual understanding, though not as prominently as in Stewart's other books. ESSENTIAL CALCULUS features the same attention to detail, eye Table of Contents1. FUNCTIONS AND LIMITS. Functions and Their Representations. A Catalog of Essential Functions. The Limit of a Function. Calculating Limits. Continuity. Limits Involving Infinity. 2. DERIVATIVES. Derivatives and Rates of Change. The Derivative as a Function. Basic Differentiation Formulas. The Product and Quotient Rules. The Chain Rule. Implicit Differentiation. Related Rates. Linear Approximations and Differentials. 3. APPLICATIONS OF DIFFERENTIATION. Maximum and Minimum Values. The Mean Value Theorem. Derivatives and the Shapes of Graphs. Curve Sketching. Optimization Problems. Newton's Method. Antiderivatives. 4. INTEGRALS. Areas and Distances. The Definite Integral. Evaluating Definite Integrals. The Fundamental Theorem of Calculus. The Substitution Rule. 5. INVERSE FUNCTIONS. Inverse Functions. The Natural Logarithmic Function. The Natural Exponential Function. General Logarithmic and Exponential Functions. Exponential Growth and Decay. Inverse Trigonometric Functions. Hyperbolic Functions. Indeterminate Forms and l'Hospital's Rule. 6. TECHNIQUES OF INTEGRATION. Integration by Parts. Trigonometric Integrals and Substitutions. Partial Fractions. Integration with Tables and Computer Algebra Systems. Approximate Integration. Improper Integrals. 7. APPLICATIONS OF INTEGRATION. Areas between Curves. Volumes. Volumes by Cylindrical Shells. Arc Length. Area of a Surface of Revolution. Applications to Physics and Engineering. Differential Equations. 8. SERIES. Sequences. Series. The Integral and Comparison Tests. Other Convergence Tests. Power Series. Representing Functions as Power Series. Taylor and Maclaurin Series. Applications of Taylor Polynomials. 9. PARAMETRIC EQUATIONS AND POLAR COORDINATES. Parametric Curves. Calculus with Parametric Curves. Polar Coordinates. Areas and Lengths in Polar Coordinates. Conic Sections in Polar Coordinates. 10. VECTORS AND THE GEOMETRY OF SPACE. Three-Dimensional Coordinate Systems. Vectors. The Dot Product. The Cross Product. Equations of Lines and Planes. Cylinders and Quadric Surfaces. Vector Functions and Space Curves. Arc Length and Curvature. Motion in Space: Velocity and Acceleration. 11. PARTIAL DERIVATIVES. Functions of Several Variables. Limits and Continuity. Partial Derivatives. Tangent Planes and Linear Approximations. The Chain Rule. Directional Derivatives and the Gradient Vector. Maximum and Minimum Values. Lagrange Multipliers. 12. MULTIPLE INTEGRALS. Double Integrals over Rectangles. Double Integrals over General Regions. Double Integrals in Polar Coordinates. Applications of Double Integrals. Triple Integrals. Triple Integrals in Cylindrical Coordinates. Triple Integrals in Spherical Coordinates. Change of Variables in Multiple Integrals. 13. VECTOR CALCULUS. Vector Fields. Line Integrals. The Fundamental Theorem for Line Integrals. Green's Theorem. Curl and Divergence. Parametric Surfaces and Their Areas. Surface Integrals. Stokes' Theorem. The Divergence Theorem. Appendix A. Trigonometry. Appendix B. Proofs. Appendix C. Sigma Notation.

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Book SynopsisTable of Contents1. Introduction to Statistics and Data Analysis 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 1.2 Sampling Procedures; Collection of Data 1.3 Measures of Location: The Sample Mean and Median Exercises 1.4 Measures of Variability Exercises 1.5 Discrete and Continuous Data 1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods 1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study Exercises 2. Probability 2.1 Sample Space 2.2 Events Exercises 2.3 Counting Sample Points Exercises 2.4 Probability of an Event 2.5 Additive Rules Exercises 2.6 Conditional Probability, Independence and Product Rules Exercises 2.7 Bayes' Rule Exercises Review Exercises 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 3. Random Variables and Probability Distributions 3.1 Concept of a Random Variable 3.2 Discrete Probability Distributions 3.3 Continuous Probability Distributions Exercises 3.4 Joint Probability Distributions Exercises Review Exercises 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 4. Mathematical Expectation 4.1 Mean of a Random Variable Exercises 4.2 Variance and Covariance of Random Variables Exercises 4.3 Means and Variances of Linear Combinations of Random Variables 4.4 Chebyshev's Theorem Exercises Review Exercises 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 5. Some Discrete Probability Distributions 5.1 Introduction and Motivation 5.2 Binomial and Multinomial Distributions Exercises 5.3 Hypergeometric Distribution Exercises 5.4 Negative Binomial and Geometric Distributions 5.5 Poisson Distribution and the Poisson Process Exercises Review Exercises 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 6. Some Continuous Probability Distributions 6.1 Continuous Uniform Distribution 6.2 Normal Distribution 6.3 Areas under the Normal Curve 6.4 Applications of the Normal Distribution Exercises 6.5 Normal Approximation to the Binomial Exercises 6.6 Gamma and Exponential Distributions 6.7 Chi-Squared Distribution 6.8 Beta Distribution 6.9 Lognormal Distribution (Optional) 6.10 Weibull Distribution (Optional) Exercises Review Exercises 6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 7. Functions of Random Variables (Optional) 7.1 Introduction 7.2 Transformations of Variables 7.3 Moments and Moment-Generating Functions Exercises 8. Sampling Distributions and More Graphical Tools 8.1 Random Sampling and Sampling Distributions 8.2 Some Important Statistics Exercises 8.3 Sampling Distributions 8.4 Sampling Distribution of Means and the Central Limit Theorem Exercises 8.5 Sampling Distribution of S2 8.6 t-Distribution 8.8 Quantile and Probability Plots Exercises Review Exercises 8.9 Potential Misconceptions and Hazards; Relationship to Mate

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Book SynopsisExam Board: AQALevel: AS/A-levelSubject: MathematicsFirst Teaching: September 2017First Exam: June 2018AQA ApprovedGive students the confidence to identify connections between topics and apply their reasoning to mathematical problems, so as to develop a deeper understanding of mathematical concepts and their applications, with resources developed with subject specialists and MEI (Mathematics in Education and Industry).- Prepare students for assessment with plenty of practice questions, worked examples and skill-focused exercises. - Help build connections between topics with points of interest and things to notice such as links to real world examples and noticing patterns in the mathematics.- Enhance understanding of problem-solving, proof and modelling with dedicated sections on these key areas.- Address the new statistics requirements with five dedicated statistics chapters and question

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Book SynopsisExam Board: OCRLevel: A-levelSubject: MathematicsFirst Teaching: September 2017First Exam: June 2018An OCR endorsed textbookBoost your students'' knowledge, skills and understanding so that they can reason and apply mathematical techniques in solving problems; with resources developed specifically for the OCR specification by subject experts and in conjunction with MEI (Mathematics in Education and Industry).- Boosts students'' confidence approaching assessment with plenty of practice questions and skill-focused exercises.- Build connections between topics with points of interest and things to notice such as links to real world examples and noticing patterns in the mathematics. - Ensure targeted development of problem-solving, proof and modelling with dedicated sections on these key areas.- Help students to overcome misconceptions and develop insight into problem-solving with annotated w

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Book SynopsisIn fall 2000, the Notre Dame logic community hosted Greg Hjorth, Rodney G. Downey, Zoé Chatzidakis, and Paola D'Aquino as visiting lecturers. Each of them presented a month long series of expository lectures at the graduate level. The articles in this volume are refinements of these excellent lectures.

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Book SynopsisAchieve the best grades in 2025 and 2026 with this all-in-one book from CGP - including study notes, examples and practice questions!This CGP Complete Revision & Practice book is a fantastic all-in-one guide to AS & A-Level Edexcel Further Maths. It's bursting with crystal-clear revision notes and worked examples for the Core Pure topics as well as the Further Pure 1, Further Statistics 1, Further Mechanics 1 and Decision Mathematics 1 options. There are also plenty of exam-style questions to test students on what they've learned (including step-by-step answers at the back). And to top things off, the book comes with a free Online Edition - just use the code printed inside the book to read it on a PC, Mac or tablet.

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Book SynopsisThere's so much talk about the threat posed by intelligent machines that it sometimes seems as though we should surrender to our robot overlords. But Junaid Mubeen isn't ready to throw in the towel just yet. As far as he is concerned, we have the creative edge over machines, because of a remarkable system of thought that humans have developed over the millennia. It's familiar to us all, but often badly taught in schools and misrepresented in popular discourse - maths. Computers are, of course, brilliant at totting up sums, pattern-seeking and performing mindless tasks of, well, computation. For all things calculation, machines reign supreme. But Junaid identifies seven areas of intelligence where humans can retain a crucial edge. And in exploring these areas, he opens up a fascinating world where we can develop our uniquely human mathematical superpowers.

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Book SynopsisWhich famous proof did Archimedes inscribe on his tombstone? How and why do knots make perfect pentagons? Have you ever seen a proof so completely that it is just obvious? In this delicious little book, top down-under mathemagician Dr. Polster presents many of the most visually intuitive and exciting proofs from the dusty annuals of mathematical history. You can test your ability to follow the logic, leap into mathemagnosis and experience eureka-moment after eureka-moment. This is the first UK edition of this original classic from Wooden Books, highly successful in the US. WOODEN BOOKS are small but packed with information. "Fascinating" FINANCIAL TIMES. "Beautiful" LONDON REVIEW OF BOOKS. "Rich and Artful" THE LANCET. "Genuinely mind-expanding" FORTEAN TIMES. "Excellent" NEW SCIENTIST. "Stunning" NEW YORK TIMES. Small books, big ideas.Trade Review"The fascinating, informative Wooden Books series blends ancient wisdom and modern knowledge. The books are thin, impeccably designed, and will stimulate more thinking and subsequent 'Eureka' moments than a dozen novels laced with narrative innovation and postmodern stylings."

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Book SynopsisThis book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.Table of ContentsLinear Equations.- Least Squares Estimation.- Linear Regression.- Eigen Decomposition.- Singular Value Decomposition.- Generalized Least Squares Estimation.- Robust Regression.- Model Selection and Biased Estimation.- Cubic Splines and Additive Models.- Nonlinear Regression and Optimization.- Generalized Linear Models.- Survival Analysis.- Time Series Analysis.- Mixed Effects Models.

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Book Synopsis"Can science be funny?" takes a close look at an element of modern science communication that is as innovative as it is promising for the future: comedy!Readers are guided through vividly presented academic theory as well as exciting hands-on and best practice examples from renowned practitioners and cabaret artists:- What do sheep's cheese and car tires have in common?- Can laughter break down walls?- How does "Die Anstalt" work?- How does magic create knowledge?- Is there humor in museums?- When a Dalmatian comes to the cash register- Three steps to humor- Serving suggestion for the Holy Spirit- dictatorship of stupidity- And much more!But it's not all just funny. Comedy can also take away some of the biting sharpness of criticism, making it digestible, even palatable, for the addressees."Can Science Be Funny?" navigates between criticism and cabaret, tackling comedy in various guises from different perspectives.22 contributions show how the results of science, research and technology can be brought to the general public in new ways. In particular, they also demonstrate how humour can be used as a critical and questioning force - valuable for all types of communication and helpful so that they come across more shrewdly in the future.The translation was done with the help of the artificial intelligence (machine translation by the service DeepL.com). The text has subsequently been revised further by the original editors in order to refine the work stylistically. Trade Review“Can Science be Witty? is one of the most enjoyable books I’ve read in many a moon. Its clichéd lack of academic self-awareness, lamentable English and spindly humour mean that the book inadvertently provides a positive answer to the question of its title. Oscar Wilde must be turning in his grave.” (The Bay Magazine, theswanseabay.co.uk, May, 2023)Table of ContentsForeword.- 1 Getting started.- 2 Science slam about sheep's cheese and car tyres.- 3 Laughter tears down walls.- 4 "Die Anstalt" as an example of criticism, satire and humour in science communication.- 5 A love song.- 6 The paradigm disease: An almost incurable scientific epidemic.7 Scientists, magicians and charlatans - How magic creates knowledge.8 Searching for humour in the Deutsches Museum - An exploration.9 From Big Bang to Big Van.10 If a dalmatian comes to the cash desk.11 Derblecken bei acatech - A humorous joke at acatech. 11 Derblecken bei acatech.- 12 Wit and lightness in science - the international perspective.- 13 "You don't understand science anyway!"- 14 Distance, please!- 15 Serving suggestion for the Holy Spirit.- 16 Dictatorship of stupidity.- 17Anecdotes from my physics lessons.- 18 Humour in knowledge transfer - Academic basics with workshop report.- 19 Georg Christoph Lichtenberg: An early pioneer of funny science.- 20 Can the Anthropocene be funny? A science comic.- 21 Science cabaret: a script.- 22 Done. Now what?!

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Book SynopsisThe theory of dynamical systems, or mappings, plays an important role in various disciplines of modern physics, including celestial mechanics and fluid mechanics. This comprehensive introduction to the general study of mappings has particular emphasis on their applications to the dynamics of the solar system. The book forms a bridge between continuous systems, which are suited to analytical developments and to discrete systems, which are suitable for numerical exploration. Featuring chapters based on lectures delivered at the School on Discrete Dynamical Systems (Aussois, France, February 1996) the book contains three parts - Numerical Tools and Modelling, Analytical Methods, and Examples of Application. It provides a single source of information that, until now, has been available only in widely dispersed journal articles.Table of Contents1. Part I: Modelling Mappings: An Aim and a Tool for the Study of Dynamical Systems 2. Spectra of Stretching Numbers and Helicity Angles 3. Diffusion and Transient Spectra in a 4-Dimensional Symplectic Mapping 4. Distribution of Periodic Orbits in 2-D Dynamical Systems 5. Symplectic Integrators 6. The Use of Mappings for Stability Problems in Beam Dynamics 7. Part II: Rigorous and Numerical Determination of Rotational Invariant Curves for the Standard Map 8. Interpolation of Discrete Hamiltonian Systems 9. Standard and Anomalous Diffusion in Dynamical Systems 10. Part III: Symplectic Maps and Their Use in Celestial Mechanics 11. Perturbation Theory for Volume Preserving Maps: Application to the Magnetic Field Lines in Plasma Physics

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Book SynopsisStudy smarter and stay on top of your calculus course with the bestselling Schaumâs Outlineânow with the NEW Schaumâs app and website! Schaumâs Outline of Calculus, Seventh Edition is the go-to study guide for hundreds of thousands of high school and college students enrolled in calculus coursesâincluding Calculus, Calculus II, Calculus III, AP Calculus and Precalculus. With an outline format that facilitates quick and easy review, Schaumâs Outline of Calculus, Seventh Edition helps you understand basic concepts and get the extra practice you need to excel in these courses. Chapters include Linear Coordinate Systems, Functions, Limits, Rules for Differentiating Functions, Law of the Mean, Inverse Trigonometric Functions, The Definite Integral, Space Vectors, Directional Derivatives, and much, much more.Features: NEW to this edition: the new Schaumâs app and website! 1,105 problems solved step by step<Table of ContentsPrefaceChapter 1 Linear Coordinate Systems. Absolute Value. Inequalities. Linear Coordinate System Finite Intervals Infinite Intervals Inequalities Solved Problems Supplementary ProblemsChapter 2 Rectangular Coordinate Systems Coordinate Axes Coordinates Quadrants The Distance Formula The Midpoint Formulas Proofs of Geometric Theorems Solved Problems Supplementary ProblemsChapter 3 Lines The Steepness of a Line The Sign of the Slope Slope and Steepness Equations of Lines A Point–Slope Equation Slope–Intercept Equation Parallel Lines Perpendicular Lines Solved Problems Supplementary ProblemsChapter 4 Circles Equations of Circles The Standard Equation of a Circle Solved Problems Supplementary ProblemsChapter 5 Equations and Their Graphs The Graph of an Equation Parabolas Ellipses Hyperbolas Conic Sections Solved Problems Supplementary ProblemsChapter 6 Functions Solved Problems Supplementary ProblemsChapter 7 Limits Limit of a Function Right and Left Limits Theorems on Limits Infinity Solved Problems Supplementary ProblemsChapter 8 Continuity Continuous Function Solved Problems Supplementary ProblemsChapter 9 The Derivative Delta Notation The Derivative Notation for Derivatives Differentiability Solved Problems Supplementary ProblemsChapter 10 Rules for Differentiating Functions Differentiation Composite Functions. The Chain Rule. Chain Rule Alternative Formulation of the Chain Rule Inverse Functions Higher Derivatives Solved Problems Supplementary ProblemsChapter 11 Implicit Differentiation Implicit Functions Derivatives of Higher Order Solved Problems Supplementary ProblemsChapter 12 Tangent and Normal Lines The Angles of Intersection Solved Problems Supplementary ProblemsChapter 13 Law of the Mean. Increasing and Decreasing Functions. Relative Maximum and Minimum Increasing and Decreasing Functions Solved Problems Supplementary ProblemsChapter 14 Maximum and Minimum Values Critical Numbers Second Derivative Test for Relative Extrema First Derivative Test Absolute Maximum and Minimum Tabular Method for Finding the Absolute Maximum and Minimum Solved Problems Supplementary ProblemsChapter 15 Curve Sketching. Concavity. Symmetry. Concavity Points of Inflection Vertical Asymptotes Horizontal Asymptotes Symmetry Inverse Functions and Symmetry Even and Odd Functions Hints for Sketching the Graph G of y = f (x) Solved Problems Supplementary ProblemsChapter 16 Review of Trigonometry Angle Measure Directed Angles Sine and Cosine Functions Solved Problems Supplementary ProblemsChapter 17 Differentiation of Trigonometric Functions Continuity of cos x and sin x Graph of sin x Graph of cos x Other Trigonometric Functions Derivatives Other Relationships Graph of y = tan x Graph of y = sec x Angles Between Curves Solved Problems Supplementary ProblemsChapter 18 Inverse Trigonometric Functions The Derivative of sin−1 x The Inverse Cosine Function The Inverse Tangent Function Solved Problems Supplementary ProblemsChapter 19 Rectilinear and Circular Motion Rectilinear Motion Motion Under the Influence of Gravity Circular Motion Solved Problems Supplementary ProblemsChapter 20 Related Rates Solved Problems Supplementary ProblemsChapter 21 Differentials. Newton’s Method. The Differential Definition Newton’s Method Solved Problems Supplementary ProblemsChapter 22 Antiderivatives Laws for Antiderivatives Solved Problems Supplementary ProblemsChapter 23 The Definite Integral. Area Under a Curve. Sigma Notation Area Under a Curve Properties of the Definite Integral Solved Problems Supplementary ProblemsChapter 24 The Fundamental Theorem of Calculus Mean Value Theorem for Integrals Average Value of a Function on a Closed Interval Fundamental Theorem of Calculus Change of Variable in a Definite Integral Solved Problems Supplementary ProblemsChapter 25 The Natural Logarithm The Natural Logarithm Definition Properties of the Natural Logarithm Solved Problems Supplementary ProblemsChapter 26 Exponential and Logarithmic Functions Definition Properties of ex The General Exponential Function General Logarithmic Functions Solved Problems Supplementary ProblemsChapter 27 L’Hôpital’s Rule L’Hôpital’s Rule Indeterminate Type 0 · ∞ Indeterminate Type ∞ − ∞ Indeterminate Types 00, ∞0, and 1∞ Solved Problems Supplementary ProblemsChapter 28 Exponential Growth and Decay Half-Life Solved Problems Supplementary ProblemsChapter 29 Applications of Integration I: Area and Arc Length Area Between a Curve and the Y-Axis Areas Between Curves Arc Length Solved Problems Supplementary ProblemsChapter 30 Applications of Integration II: Volume Disk Formula Washer Method Cylindrical Shell Method Difference of Shells Formula Cross-Section Formula (Slicing Formula) Solved Problems Supplementary ProblemsChapter 31 Techniques of Integration I: Integration by Parts Solved Problems Supplementary ProblemsChapter 32 Techniques of Integration II: Trigonometric Integrands and Trigonometric Substitutions Trigonometric Integrands Trigonometric Substitutions Solved Problems Supplementary ProblemsChapter 33 Techniques of Integration III: Integration by Partial Fractions Method of Partial Fractions Solved Problems Supplementary ProblemsChapter 34 Techniques of Integration IV: Miscellaneous Substitutions Solved Problems Supplementary ProblemsChapter 35 Improper Integrals Infinite Limits of Integration Discontinuities of the Integrand Solved Problems Supplementary ProblemsChapter 36 Applications of Integration III: Area of a Surface of Revolution Solved Problems Supplementary ProblemsChapter 37 Parametric Representation of Curves Parametric Equations Arc Length for a Parametric Curve Solved Problems Supplementary ProblemsChapter 38 Curvature Derivative of Arc Length Curvature The Radius of Curvature The Circle of Curvature The Center of Curvature The Evolute Solved Problems Supplementary ProblemsChapter 39 Plane Vectors Scalars and Vectors Sum and Difference of Two Vectors Components of a Vector Scalar Product (or Dot Product) Scalar and Vector Projections Differentiation of Vector Functions Solved Problems Supplementary ProblemsChapter 40 Curvilinear Motion Velocity in Curvilinear Motion Acceleration in Curvilinear Motion Tangential and Normal Components of Acceleration Solved Problems Supplementary ProblemsChapter 41 Polar Coordinates Polar and Rectangular Coordinates Some Typical Polar Curves Angle of Inclination Points of Intersection Angle of Intersection The Derivative of the Arc Length Curvature Solved Problems Supplementary ProblemsChapter 42 Infinite Sequences Infinite Sequences Limit of a Sequence Monotonic Sequences Solved Problems Supplementary ProblemsChapter 43 Infinite Series Geometric Series Solved Problems Supplementary ProblemsChapter 44 Series with Positive Terms. The Integral Test. Comparison Tests. Series of Positive Terms Solved Problems Supplementary ProblemsChapter 45 Alternating Series. Absolute and Conditional Convergence. The Ratio Test. Alternating Series Definition Solved Problems Supplementary ProblemsChapter 46 Power Series Power Series Uniform Convergence Solved Problems Supplementary ProblemsChapter 47 Taylor and Maclaurin Series. Taylor’s Formula with Remainder. Taylor and Maclaurin Series Applications of Taylor’s Formula with Remainder Solved Problems Supplementary ProblemsChapter 48 Partial Derivatives Functions of Several Variables Limits Continuity Partial Derivatives Partial Derivatives of Higher Order Solved Problems Supplementary ProblemsChapter 49 Total Differential. Differentiability. Chain Rules. Total Differential Differentiability Chain Rules Chain Rule (2 → 1) Chain Rule (2 → 2) Implicit Differentiation Solved Problems Supplementary ProblemsChapter 50 Space Vectors Vectors in Space Direction Cosines of a Vector Determinants Vector Perpendicular to Two Vectors Vector Product of Two Vectors Triple Scalar Product The Straight Line The Plane Solved Problems Supplem entary ProblemsChapter 51 Surfaces and Curves in Space Planes Spheres Cylindrical Surfaces Ellipsoid Elliptic Paraboloid Elliptic Cone Hyperbolic Paraboloid Hyperboloid of One Sheet Hyperboloid of Two Sheets Tangent Line and Normal Plane to a Space Curve Tangent Plane and Normal Line to a Surface Surface of Revolution Solved Problems Supplementary ProblemsChapter 52 Directional Derivatives. Maximum and Minimum Values. Directional Derivatives Relative Maximum and Minimum Values Absolute Maximum and Minimum Values Solved Problems Supplementary ProblemsChapter 53 Vector Differentiation and Integration Vector Differentiation Space Curves Surfaces The Operation ∇ Divergence and Curl Integration Line Integrals Solved Problems Supplementary ProblemsChapter 54 Double and Iterated Integrals The Double Integral The Iterated Integral Solved Problems Supplementary ProblemsChapter 55 Centroids and Moments of Inertia of Plane Areas Plane Area by Double Integration Centroids Moments of Inertia Solved Problems Supplementary ProblemsChapter 56 Double Integration Applied to Volume Under a Surface and the Area of a Curved Surface Solved Problems Supplementary ProblemsChapter 57 Triple Integrals Cylindrical and Spherical Coordinates The Triple Integral Evaluation of Triple Integrals Centroids and Moments of Inertia Solved Problems Supplementary ProblemsChapter 58 Masses of Variable Density Solved Problems Supplementary ProblemsChapter 59 Differential Equations of First and Second Order Separable Differential Equations Homogeneous Functions Integrating Factors Second-Order Equations Solved Problems Supplementary ProblemsAppendix A Trigonometric FormulasAppendix B Geometric FormulasIndex

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Book SynopsisTough Test Questions? Missed Lectures? Not Enough Time?Fortunately, thereâs Schaumâs. More than 40 million students have trusted Schaumâs to help them succeed in the classroom and on exams. Schaumâs 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, sovled problems, and practice exercises to test your skills. This Schaumâs Outline gives you:â 300 supplemental problems to reinforce knowledgeâ Additional new end of chapter problems and supplementary problemsâ New chapter on solving Higher Degree Equationsâ New chapter on Algebra for Calculusâ Concise exaplanations of all intermediate algebra conceptsâ Support for all major textbooks for courses in Algebra

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Book SynopsisOne of the most illuminating, useful and exciting books ever published in the mathematical field Taking only a modicum of knowledge for granted, Lancelot Hogben leads readers of this famous book through the whole course from simple arithmetic to calculus. His illuminating explanation is addressed to the person who wants to understand the place of mathematics in modern civilization but who has been intimidated by its supposed difficulty. Mathematics is the language of size, shape, and order – a language Hogben shows one can both master and enjoy.Trade Review'It makes alive the contents of the elements of mathematics' Albert Einstein'Deals with maths in a way that they never taught us at school' Daily Express'If only I had been brought up on this book, the sense and meaning of mathematics would have been made clear to me... The book combines utmost brilliance with extraordinarily good common sense' A. L. Rowse'A great book of first-class importance' H. G. Wells

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Book SynopsisMany student private pilots don’t realize at the start of their course that many hours of study are required on top of the in-class schedule. This book will help those trainee pilots without science backgrounds, or those that need a refresher, to brush up on the necessary theory. It covers subjects that will be encountered many times during the PPL course, such as principles of flight, aircraft general knowledge, flight performance and planning, meteorology, navigation and human factors. The content is organized around two main groups of information, namely core knowledge, concentrating more on the concepts; and a practical toolbox, dedicated to some techniques that will be required during the course.

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Book SynopsisCo-opetition offers a new way of thinking that combines competition and cooperation. It is the first book to adapt game theory to the needs of CEOs, managers and entrepreneurs. Though often compared to games like chess or poker, business is different - people are free to change the rules, the players, the boundaries, even the game itself. The essence of business success lies in making sure you are in the right game. Actively shaping which game you play, and how you play it, is the core of the innovative business strategy laid out in Co-opetition. Barry Nalebuff and Adam Brandenburger, professors at Yale and Harvard, are pioneers in the practice of applying the science of game theory to the art of corporate strategy. They have devised a practice-oriented model to help you break out of the traditional win-lose or lose-lose situations. Dozens of companies - including Intel, Nintendo, American Express and Nutrasweet - have been using the strategies of co-opetition to change their game and enjoy the benefits of win-win opportunities.Trade ReviewSeize on Co-opetition * The Economist *Do read Co-opetition. You will certainly learn a great deal, while having fun at the same time. -- Rudi Bogni * Times Higher Education Supplement *A terrific book! * Tom Peters *

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Book SynopsisMaking good decisions under conditions of uncertainty - which is the norm - requires a sound appreciation of the way random chance works. As analysis and modelling of most aspects of the world, and all measurement, are necessarily imprecise and involve uncertainties of varying degrees, the understanding and management of probabilities is central to much work in the sciences and economics. In this Very Short Introduction, John Haigh introduces the ideas of probability and different philosophical approaches to probability, and gives a brief account of the history of development of probability theory, from Galileo and Pascal to Bayes, Laplace, Poisson, and Markov. He describes the basic probability distributions, and goes on to discuss a wide range of applications in science, economics, and a variety of other contexts such as games and betting. He concludes with an intriguing discussion of coincidences and some curious paradoxes. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Trade ReviewAn excellent and provocative introduction to a fascinating and underappreciated subject. * Mathematical Gazette *Table of Contents1. Fundamentals ; 2. The workings of probability ; 3. Historical sketch ; 4. Chance experiments ; 5. Making sense of probabilities ; 6. Games people play ; 7. Applications in science and operations research ; 8. Other applications ; 9. Curiosities and dilemmas ; Appendix - Answers to questions posed

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Book SynopsisWhat is time? The Janus Point offers a ground-breaking solution to one of the greatest mysteries in physics.For over a century, the greatest minds have sought to understand why time seems to flow in one direction, ever forward. In The Janus Point, Julian Barbour offers a radically new answer: it doesn't.At the heart of this book, Barbour provides a new vision of the Big Bang - the Janus Point - from which time flows in two directions, its currents driven by the expansion of the universe and the growth of order in the galaxies, planets and life itself. What emerges is not just a revolutionary new theory of time, but a hopeful argument about the destiny of our universe.'Both a work of literature and a masterpiece of scientific thought' Lee Smolin, author of The Trouble with Physics'Profound...original...accessible to anyone who has pondered the mysteries of space and time' Martin Rees, Astronomer Royal 'Takes on fundamental questions, offering a new perspective on how the Universe started and where it may be headed' Science MagazineTrade ReviewJulian Barbour is a profound and original thinker with the boldness to tackle some of nature's deepest problems. He is also a fine writer, and this renders his book - despite its conceptual depth - accessible to anyone who has pondered the mysteries of space and time -- Martin Rees, Astronomer Royal and former President of the Royal SocietyWith a rare humanity and a perspective based on a lifetime of study, Barbour writes a book that is both a work of literature and a masterpiece of scientific thought -- Lee Smolin, author of The Trouble with PhysicsThe origin of the arrow of time is arguably the most important conceptual problem in cosmology, and the prospect that it can be solved in a universe where time flows "backward" in the far past is as exciting as it is provocative. In this engaging book, Julian Barbour conveys this excitement admirably -- Sean Carroll, author of From Eternity to HereThe Janus Point shows history-in-the-making: a project to recast the foundations of all of cosmology, gravity, thermodynamics and the arrow of time. The book has given me a lot to ponder. As Gauss said of Riemann's habilitation lecture, '[it] exceeded my expectations' -- Bill Unruh, Professor of Physics at University of British ColumbiaJulian Barbour has no peer when it comes to explaining scientific ideas in a way that is accessible without being simplistic -- Neal Stephenson, author of Snow CrashJulian Barbour has discovered an unexpected and remarkably simple feature of Newtonian dynamics that is the basis of his seductive and eloquently presented explanation of the history of the universe, even time itself -- Michael Victor Berry, Professor of Physics (Emeritus) at Bristol UniversityThis delightful, provocative book is a cosmic physics adventure, enlivened with history and poetry -- Theodore A. Jacobson, Professor of Physics at University of MarylandJulian Barbour has a complete mastery of the history of ideas yet a remarkable lightness and clarity in explaining what are profound concepts. The Janus Point is controversial and gripping, an extraordinary introduction to his view of the universe -- Pedro G. Ferreira, author of The Perfect TheoryBarbour takes on fundamental questions, offering a new perspective - illustrated with lucid examples and poetically constructed prose - on how the Universe started (or more precisely, how it did not start) and where it may be headed. This book is an engaging read, which both taught me something new about meat-and-potatoes physics and reminded me why asking fundamental questions can be so fun -- Matthew Johnson * Science *A closely argued, substantive take on one of the biggest unsolved mysteries of physics, written by someone who has wrestled with not only the physics, but also the history and philosophy relevant to his subject. What's more, Barbour's approach, unlike many in the popular science game, is to publish only when he thinks he has something worth saying. That alone is enough to make him worth listening to -- Michael Brooks * Nautilus *Julian Barbour is one that rare breed, an optimistic scientist, and his engrossing The Janus Point not only turns accepted thinking about the universe on its head...but also suggests our very understanding of the nature of time needs to be reappraised * Choice *Any reader willing to engage with Barbour's ideas will come away enlightened -- Sidney Perkowitz * Physics World *

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Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

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Book SynopsisHow do you divide a line into three? Or five? Or seven? Is there a simple way to marry harmony and geometry? What is the secret diagram alluded to by writers of antiquity? In this groundbreaking book, philosopher Adam Tetlow reveals the long lost Helicon, the master diagram of the ancient arts and crafts.Watch in astonishment as this magical geometric figure produces simple fractions, musical harmonies, Pythagorean triangles, perspective and more. WOODEN BOOKS are small but packed with information. "Fascinating" FINANCIAL TIMES. "Beautiful" LONDON REVIEW OF BOOKS. "Rich and Artful" THE LANCET. "Genuinely mind-expanding" FORTEAN TIMES. "Excellent" NEW SCIENTIST. "Stunning" NEW YORK TIMES. Small books, big ideas.

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Book SynopsisEngaging introduction to that curious feature of mathematics which provides framework for so many structures in biology, chemistry, and the arts. Discussion ranges from theories of biological growth to intervals and tones in music, Pythagorean numerology, conic sections, Pascal''s triangle, the Fibonnacci series, and much more.

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Book SynopsisCalculus is the key to much of modern science and engineering. It is the mathematical method for the analysis of things that change, and since in the natural world we are surrounded by change, the development of calculus was a huge breakthrough in the history of mathematics. But it is also something of a mathematical adventure, largely because of the way infinity enters at virtually every twist and turn...In The Calculus Story David Acheson presents a wide-ranging picture of calculus and its applications, from ancient Greece right up to the present day. Drawing on their original writings, he introduces the people who helped to build our understanding of calculus. With a step by step treatment, he demonstrates how to start doing calculus, from the very beginning.Trade ReviewA masterpiece... Packed with insights, both historical and mathematical. * Steven Strogatz, professor of mathematics, Cornell University, and author of The Joy of X and Infinite Powers *This is the book on calculus I wish I'd written. It's a beautifully simple, friendly guide that's bursting at the seams with glorious, persuasive explanations as to why calculus is one of the most powerful ideas ever conceived by mankind. * Hannah Fry, Broadcaster, lecturer, and author of The Mathematics of Love *A splendid little book ... accessible to a very wide audience ... The book is highly recommended. * Adam McBride, Mathematical Gazette *A remarkably expansive and frictionless tour of mathematical history and theory... The calculus story is no textbook... It is the antithesis of the dreary way calculus is too often taught at schools and universities... a supplement for a high school student, the parents of such a student, or an adult wishing to reacquaint herself painlessly with material long forgotten. * Henrik Latter, Plus *This is a very readable book... It offers an illuminating perspective on calculus... A very enjoyable book for the layperson or the user of calculus. * Alex Chaplin, School Science Review *Wish I'd had it as a maths student! * Tim Harford, Undercover Economist *Another wonderful book. * Mark McCartney, LMS Newsletter *A very clear explanation of calculus ([I] wish I'd had it as a maths student!) along with some history of the subject. * Tim Harford, The Undercover Economist *Superb introduction to calculus that should be in every young mathematician's bookcase. * Peter Ransom, Symmetry Plus *Don't panic if your mathematical muscles appear to have withered away (or you never truly cracked differentiation), David Acheson's The Calculus Story could be just the thing... A roller-coaster read, constantly climbing and diving through the wonderful world of calculus... There's something for everyone, from the inexperienced integrator to the seasoned solver of equations... His enthusiasm for calculus is almost palpable. * Timothy Revell, New Scientist *Dazzling. * Matthew Reisz, Times Higher Education *I would have killed for this book when I was 13 ... he [David Acheson] belongs in the league of great authors of popular works on mathematics. * George Matthews, Mathematics Today *A worthy successor to 1089 and All That. * Adhemar Bult heel, European Mathematical Society *A simple guide to calculus - where it came from, how it works, what it's good for, and where it went. Brief, informative, charming, and a model of clarity. Ideal motivation for beginners, and recommended to anyone who wonders what the subject is about. * Ian Stewart, author of Seventeen Equations that Changed the World *This wide-ranging picture of calculus and its applications, from antiquity to the present, reveals the method as both the key to much of modern science and engineering, and something of a mathematical adventure. * Science *Acheson offers a much-needed short account of the big picture of calculus as a whole, illustrated with examples and reproductions from historic publications [...] Short pages, many illustrations, and a sense of telling a big story contribute to the success of the book. * Paul J. Campbell, Mathematical Magazine *Table of ContentsREFERENCES; FURTHER READING; INDEX

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Book SynopsisTrade ReviewOne of Choice's Outstanding Academic Titles for 2013 Shortlisted for the 2013 BSHM Neumann Book Prize, British Society for the History of Mathematics "Once a mainstay of mathematics, spherical trigonometry no longer appears on school curricula. Here, Glen Van Brummelen reasserts the field's importance, sharing in illuminating detail how it figured in astronomy, cartography and our understanding of Earth's rotation."--Rosalind Metcalfe, Nature "The present book is very well written; it leaves a clear impression that the author intended to endear--not merely present and teach--spherical trigonometry to the reader. Although not a history book, there are separate chapters shedding light on the approaches to the subject in the ancient, medieval, and modern times. There are also chapters on spherical geometry, polyhedra, stereographic projection and the art of navigation. The book is thoroughly illustrated and is a pleasant read. Chapters end with exercises; the appendices contain a long list of available and not so available textbooks and recommendations for further reading organized by individual chapters. The book made a valuable addition to my library. I freely recommend it to math teachers and curious high schoolers."--Alexander Bogomolny, CTK Insights "A no-nonsense introduction to spherical trigonometry."--Book News, Inc. "A beautiful popular book."--ThatsMaths.com "Full of academic, textbook content, the book is a delight to math students. So if you are game for a journey into the world of spherical trigonometry, pick up the book. Van Brummelen gives exercises at the end of the chapters that can be fun."--R. Balashankar, Organiser "Heavenly Mathematicsis a truly enjoyable description of the somewhat forgotten science of spherical trigonometry... As readers discover this discipline, they will also appreciate the beauty inherent in the topic."--Choice "Heavenly Mathematics proves the value of bringing a fascinating piece of mathematical history within the grasp of the general reader."--Florin Diacu, Literary Review of Canada "Van Brummelen has written a wonderful introduction ... that draws on the history of [spherical trigonometry] to illuminate the mathematics itself and at the same time gives readers a real sense of what research in the history of early mathematics is all about."--Metascience "[Heavenly Mathematics] is an excellent survey of spherical trigonometry... Simply an appreciation of a beautiful lost subject, with historical overtones... [D]istinguishable for its appealingly fresh style."--Mathematical Reviews "[Heavenly Mathematics] is a lovely book to read... [A] wonderful introduction for anyone who wishes to learn more about this subject... I am in full agreement with the author that spherical trigonometry ought to be brought to a wider audience, and I believe that this is the book to do it."--Mathematics Today "Engaging, clear and not overly technical; you can safely lend this book to your friends in the history department... [Heavenly Mathematics] is excellent."--Zentralblatt MATH "Heavenly Mathematics will be of interest to mathematically inclined historians of science and also to students of mathematics and engineering. Because spherical trigonometry is relevant in applications of modern science, this elegant book may even contribute to a renaissance of the subject."--Jan P. Hogendijk, Isis "This book could serve as an excellent textbook for any secondary school mathematics classroom at or above the level of geometry and certainly trigonometry; as the basis for a high school honors class; or as a textbook and seminar topic for college students."--Teresa Floyd, Mathematics Teacher "Any reader of this book (and there should be many) will see how present day mathematics may be viewed through the kaleidoscope of its historical origins... Glen Van Brummelen has written a beautifully produced book that includes fascinating biographical detail at every stage of his narrative."--P.N. Ruane, Mathematical Gazette "An engaging read that will appeal to historians of science, mathematicians, trigonometry teachers, and anyone interested in the history of mathematics."--Elizabeth Hamm, Aestimatio Critical Reviews in the History of ScienceTable of ContentsPreface vii 1 Heavenly Mathematics 1 2 Exploring the Sphere 23 3 The Ancient Approach 42 4 The Medieval Approach 59 5 The Modern Approach: Right- Angled Triangles 73 6 The Modern Approach: Oblique Triangles 94 7 Areas, Angles, and Polyhedra 110 8 Stereographic Projection 129 9 Navigating by the Stars 151 Appendix A. Ptolemy's Determination of the Sun's Position 173 Appendix B. Textbooks 179 Appendix C. Further Reading 182 Index 189

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Book SynopsisPresents hints, tips and tricks to learning your times tables. This title features fun illustrations, shortcuts, clever ways to make some or the harder times tables simpler to learn (and even some harmless cheats that their teacher will approve of).Table of Contents 1: Introduction 2: The one times table 3: The zero times table 4: The two times table 5: The five times table 6: The ten times table 7: The four times table 8: The eleven times table 9: The three times table 10: Times tables quiz 11: The nine times table 12: The six times table 13: The seven times table 14: The eight times table 15: The twelve times table 16: Times tables quiz 17: Long multiplication 18: Window-frame multiplication 19: Long division 20: Times tables grid 21: Glossary 22: Answers

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Book SynopsisThis series for Key Stage 3 mathematics has been written to match the Framework for teaching mathematics. Comprising parallel resources for each year and covering all ability levels, it has a consistent but fully differentiated approach.Table of ContentsPart 1 Number support: place value, ordering and rounding; special numbers; mental calculation - whole numbers and decimals; written and calculator calculations; fractions; percentages, fractions and decimals; ratio and proportion. Part 2 Algebra support; expressions, equations, formulae; sequences and functions; graphs. Part 3 Shape, space and measures support: lines and angles; shape - construction; coordinates and transformations; measures. Part 4 Handling data support: planning and collecting data; mode, range, mean, median - displaying data; interpreting graphs - comparing data; probability.

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Book SynopsisEvery day, most of us will read or watch something in the news that is based on statistics in some way. Sometimes it''ll be obvious - ''X people develop cancer every year'' - and sometimes less obvious - ''How smartphones destroyed a generation''. Statistics are an immensely powerful tool for understanding the world, but in the wrong hands they can be dangerous.Introducing you to the common mistakes that journalists make and the tricks they sometimes deploy, HOW TO READ NUMBERS is a vital guide that will help you understand when and how to trust the numbers in the news - and, just as importantly, when not to.Trade ReviewA charming, practical and insightful guide. You might not even notice how much you're learning - you'll be too busy having fun -- TIM HARFORD, author of HOW TO MAKE THE WORLD ADD UPA vital plea to take statistics more seriously - the prose being as clear and elegant as the numbers -- SATHNAM SANGHERA, author of EMPIRELANDReading this book is strongly correlated with not looking stupid. Highly recommended -- HELEN LEWIS, author of Difficult WomenAn excellent guide to everyday statistics . . . the authors do a splendid job of stringing words together so smartly that even difficult concepts are explained and so understood with ease. [A] timely and lively book -- Manjit Kumar * THE TIMES *Wonderfully written - incredibly readable. It should be made compulsory reading for everyone before they leave school -- EVAN DAVISAn erudite, enlightening guide to the numbers we read in the news - and why they are so often wrong. The authors make sense of dense material and offer engrossing insights into sampling bias, statistical significance and the dangers of believing the casual language used in newspapers * INDEPENDENT *[A] fascinating, easy-to-read explanation of how to interpret numbers in the news . . . their enlightening book provides us with the tools to spot when we're being led astray -- Nick Rennison * DAILY MAIL *An absolute lifesaver . . . Breezy, easy to read, funny and loaded with useful information -- IAN DUNT, author of HOW TO BE A LIBERALA great combination of important and accessible -- MISHAL HUSAINBrilliant . . . part of the joy of How to Read Numbers is how light and fun it is. At the end of the process, you'll be better equipped to understand what it means when a glass of red wine can both increase and decrease your chances of getting cancer, how many portions of fruit and veg you need to eat each day, and any number of stories about numbers you might read or hear * THE BIG ISSUE *

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Book SynopsisJump-start your career as a data scientistlearn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on how to think about constructing your dataset. Gain an understanding of relational database structure, query design, and SQL syntaxDevelop queries to construct datasets for use in applications like interactive reports and machine learning algorithmsReview strategies and approaches so you can design analytical datasetsPractice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward! Table of ContentsIntroduction xix Chapter 1 Data Sources 1 Data Sources 1 Tools for Connecting to Data Sources and Editing SQL 2 Relational Databases 3 Dimensional Data Warehouses 7 Asking Questions About the Data Source 9 Introduction to the Farmer’s Market Database 11 A Note on Machine Learning Dataset Terminology 12 Exercises 13 Chapter 2 The SELECT Statement 15 The SELECT Statement 15 The Fundamental Syntax Structure of a SELECT Query 16 Selecting Columns and Limiting the Number of Rows Returned 16 The ORDER BY Clause: Sorting Results 18 Introduction to Simple Inline Calculations 20 More Inline Calculation Examples: Rounding 22 More Inline Calculation Examples: Concatenating Strings 24 Evaluating Query Output 26 SELECT Statement Summary 29 Exercises Using the Included Database 30 Chapter 3 The WHERE Clause 31 The WHERE Clause 31 Filtering SELECT Statement Results 32 Filtering on Multiple Conditions 34 Multi-Column Conditional Filtering 40 More Ways to Filter 41 BETWEEN 41 IN 42 LIKE 43 IS NULL 44 A Warning About Null Comparisons 44 Filtering Using Subqueries 46 Exercises Using the Included Database 47 Chapter 4 CASE Statements 49 CASE Statement Syntax 50 Creating Binary Flags Using CASE 52 Grouping or Binning Continuous Values Using CASE 53 Categorical Encoding Using CASE 56 CASE Statement Summary 59 Exercises Using the Included Database 60 Chapter 5 SQL JOINs 61 Database Relationships and SQL JOINs 61 A Common Pitfall when Filtering Joined Data 71 JOINs with More than Two Tables 74 Exercises Using the Included Database 76 Chapter 6 Aggregating Results for Analysis 79 GROUP BY Syntax 79 Displaying Group Summaries 80 Performing Calculations Inside Aggregate Functions 84 MIN and MAX 88 COUNT and COUNT DISTINCT 90 Average 91 Filtering with HAVING 93 CASE Statements Inside Aggregate Functions 94 Exercises Using the Included Database 96 Chapter 7 Window Functions and Subqueries 97 ROW NUMBER 98 RANK and DENSE RANK 101 NTILE 102 Aggregate Window Functions 103 LAG and LEAD 108 Exercises Using the Included Database 111 Chapter 8 Date and Time Functions 113 Setting datetime Field Values 114 EXTRACT and DATE_PART 115 DATE_ADD and DATE_SUB 116 DATEDIFF 118 TIMESTAMPDIFF 119 Date Functions in Aggregate Summaries and Window Functions 119 Exercises 126 Chapter 9 Exploratory Data Analysis with SQL 127 Demonstrating Exploratory Data Analysis with SQL 128 Exploring the Products Table 128 Exploring Possible Column Values 131 Exploring Changes Over Time 134 Exploring Multiple Tables Simultaneously 135 Exploring Inventory vs. Sales 138 Exercises 142 Chapter 10 Building SQL Datasets for Analytical Reporting 143 Thinking Through Analytical Dataset Requirements 144 Using Custom Analytical Datasets in SQL: CTEs and Views 149 Taking SQL Reporting Further 153 Exercises 157 Chapter 11 More Advanced Query Structures 159 UNIONs 159 Self-Join to Determine To-Date Maximum 163 Counting New vs. Returning Customers by Week 167 Summary 171 Exercises 171 Chapter 12 Creating Machine Learning Datasets Using SQL 173 Datasets for Time Series Models 174 Datasets for Binary Classification 176 Creating the Dataset 178 Expanding the Feature Set 181 Feature Engineering 185 Taking Things to the Next Level 189 Exercises 189 Chapter 13 Analytical Dataset Development Examples 191 What Factors Correlate with Fresh Produce Sales? 191 How Do Sales Vary by Customer Zip Code, Market Distance, and Demographic Data? 211 How Does Product Price Distribution Affect Market Sales? 217 Chapter 14 Storing and Modifying Data 229 Storing SQL Datasets as Tables and Views 229 Adding a Timestamp Column 232 Inserting Rows and Updating Values in Database Tables 233 Using SQL Inside Scripts 236 In Closing 237 Exercises 238 Appendix Answers to Exercises 239 Index 255

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Book SynopsisMathematics is a product of human culture which has developed along with our attempts to comprehend the world around us. In A Brief History of Mathematical Thought, Luke Heaton explores how the language of mathematics has evolved over time, enabling new technologies and shaping the way people think. From stone-age rituals to algebra, calculus, and the concept of computation, Heaton shows the enormous influence of mathematics on science, philosophy and the broader human story.The book traces the fascinating history of mathematical practice, focusing on the impact of key conceptual innovations. Its structure of thirteen chapters split between four sections is dictated by a combination of historical and thematic considerations. In the first section, Heaton illuminates the fundamental concept of number. He begins with a speculative and rhetorical account of prehistoric rituals, before describing the practice of mathematics in Ancient Egypt, Babylon and Greece. He

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Book SynopsisTable of ContentsForeword by Ravi Bapna xix Foreword by Gareth James xxi Preface to the Second R Edition xxiii Acknowledgments xxvi Part I Preliminaries Chapter 1 Introduction 3 1.1 What Is Business Analytics? 3 1.2 What Is Machine Learning? 5 1.3 Machine Learning, AI, and Related Terms 5 1.4 Big Data 7 1.5 Data Science 8 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 13 Chapter 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 Classification 18 Prediction 18 Association Rules and Recommendation Systems 18 Predictive Analytics 19 Data Reduction and Dimension Reduction 19 Data Exploration and Visualization 19 Supervised and Unsupervised Learning 20 2.3 The Steps in a Machine Learning Project 21 2.4 Preliminary Steps 23 Organization of Data 23 Predicting Home Values in the West Roxbury Neighborhood 23 Loading and Looking at the Data in R 24 Sampling from a Database 26 Oversampling Rare Events in Classification Tasks 27 Preprocessing and Cleaning the Data 28 2.5 Predictive Power and Overfitting 35 Overfitting 36 Creating and Using Data Partitions 38 2.6 Building a Predictive Model 41 Modeling Process 41 2.7 Using R for Machine Learning on a Local Machine 46 2.8 Automating Machine Learning Solutions 47 Predicting Power Generator Failure 48 Uber’s Michelangelo 50 2.9 Ethical Practice in Machine Learning 52 Machine Learning Software: The State of the Market (by Herb Edelstein) 53 Problems 57 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 63 3.1 Uses of Data Visualization 63 Base R or ggplot? 65 3.2 Data Examples 65 Example 1: Boston Housing Data 65 Example 2: Ridership on Amtrak Trains 67 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 67 Distribution Plots: Boxplots and Histograms 70 Heatmaps: Visualizing Correlations and Missing Values 73 3.4 Multidimensional Visualization 75 Adding Variables: Color, Size, Shape, Multiple Panels, and Animation 76 Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 79 Reference: Trend Lines and Labels 83 Scaling Up to Large Datasets 85 Multivariate Plot: Parallel Coordinates Plot 85 Interactive Visualization 88 3.5 Specialized Visualizations 91 Visualizing Networked Data 91 Visualizing Hierarchical Data: Treemaps 93 Visualizing Geographical Data: Map Charts 95 3.6 Major Visualizations and Operations, by Machine Learning Goal 97 Prediction 97 Classification 97 Time Series Forecasting 97 Unsupervised Learning 98 Problems 99 Chapter 4 Dimension Reduction 101 4.1 Introduction 101 4.2 Curse of Dimensionality 102 4.3 Practical Considerations 102 Example 1: House Prices in Boston 103 4.4 Data Summaries 103 Summary Statistics 104 Aggregation and Pivot Tables 104 4.5 Correlation Analysis 107 4.6 Reducing the Number of Categories in Categorical Variables 109 4.7 Converting a Categorical Variable to a Numerical Variable 111 4.8 Principal Component Analysis 111 Example 2: Breakfast Cereals 111 Principal Components 116 Normalizing the Data 117 Using Principal Components for Classification and Prediction 120 4.9 Dimension Reduction Using Regression Models 121 4.10 Dimension Reduction Using Classification and Regression Trees 121 Problems 123 Part III Performance Evaluation Chapter 5 Evaluating Predictive Performance 129 5.1 Introduction 130 5.2 Evaluating Predictive Performance 130 Naive Benchmark: The Average 131 Prediction Accuracy Measures 131 Comparing Training and Holdout Performance 133 Cumulative Gains and Lift Charts 133 5.3 Judging Classifier Performance 136 Benchmark: The Naive Rule 136 Class Separation 136 The Confusion (Classification) Matrix 137 Using the Holdout Data 138 Accuracy Measures 139 Propensities and Threshold for Classification 139 Performance in Case of Unequal Importance of Classes 143 Asymmetric Misclassification Costs 146 Generalization to More Than Two Classes 149 5.4 Judging Ranking Performance 150 Cumulative Gains and Lift Charts for Binary Data 150 Decile-wise Lift Charts 153 Beyond Two Classes 154 Gains and Lift Charts Incorporating Costs and Benefits 154 Cumulative Gains as a Function of Threshold 155 5.5 Oversampling 156 Creating an Over-sampled Training Set 158 Evaluating Model Performance Using a Non-oversampled Holdout Set 159 Evaluating Model Performance If Only Oversampled Holdout Set Exists 159 Problems 162 Part IV Prediction and Classification Methods Chapter 6 Multiple Linear Regression 167 6.1 Introduction 167 6.2 Explanatory vs. Predictive Modeling 168 6.3 Estimating the Regression Equation and Prediction 170 Example: Predicting the Price of Used Toyota Corolla Cars 171 Cross-validation and caret 175 6.4 Variable Selection in Linear Regression 176 Reducing the Number of Predictors 176 How to Reduce the Number of Predictors 178 Regularization (Shrinkage Models) 183 Problems 188 Chapter 7 k-Nearest Neighbors (kNN) 193 7.1 The k-NN Classifier (Categorical Outcome) 193 Determining Neighbors 194 Classification Rule 194 Example: Riding Mowers 195 Choosing k 196 Weighted k-NN 199 Setting the Cutoff Value 200 k-NN with More Than Two Classes 201 Converting Categorical Variables to Binary Dummies 201 7.2 k-NN for a Numerical Outcome 201 7.3 Advantages and Shortcomings of k-NN Algorithms 204 Problems 205 Chapter 8 The Naive Bayes Classifier 207 8.1 Introduction 207 Threshold Probability Method 208 Conditional Probability 208 Example 1: Predicting Fraudulent Financial Reporting 208 8.2 Applying the Full (Exact) Bayesian Classifier 209 Using the “Assign to the Most Probable Class” Method 210 Using the Threshold Probability Method 210 Practical Difficulty with the Complete (Exact) Bayes Procedure 210 8.3 Solution: Naive Bayes 211 The Naive Bayes Assumption of Conditional Independence 212 Using the Threshold Probability Method 212 Example 2: Predicting Fraudulent Financial Reports, Two Predictors 213 Example 3: Predicting Delayed Flights 214 Working with Continuous Predictors 218 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 220 Problems 223 Chapter 9 Classification and Regression Trees 225 9.1 Introduction 226 Tree Structure 227 Decision Rules 227 Classifying a New Record 227 9.2 Classification Trees 228 Recursive Partitioning 228 Example 1: Riding Mowers 228 Measures of Impurity 231 9.3 Evaluating the Performance of a Classification Tree 235 Example 2: Acceptance of Personal Loan 236 9.4 Avoiding Overfitting 239 Stopping Tree Growth 242 Pruning the Tree 243 Best-Pruned Tree 245 9.5 Classification Rules from Trees 247 9.6 Classification Trees for More Than Two Classes 248 9.7 Regression Trees 249 Prediction 250 Measuring Impurity 250 Evaluating Performance 250 9.8 Advantages and Weaknesses of a Tree 250 9.9 Improving Prediction: Random Forests and Boosted Trees 252 Random Forests 252 Boosted Trees 254 Problems 257 Chapter 10 Logistic Regression 261 10.1 Introduction 261 10.2 The Logistic Regression Model 263 10.3 Example: Acceptance of Personal Loan 264 Model with a Single Predictor 265 Estimating the Logistic Model from Data: Computing Parameter Estimates 267 Interpreting Results in Terms of Odds (for a Profiling Goal) 270 10.4 Evaluating Classification Performance 271 10.5 Variable Selection 273 10.6 Logistic Regression for Multi-Class Classification 274 Ordinal Classes 275 Nominal Classes 276 10.7 Example of Complete Analysis: Predicting Delayed Flights 277 Data Preprocessing 282 Model-Fitting and Estimation 282 Model Interpretation 282 Model Performance 284 Variable Selection 285 Problems 289 Chapter 11 Neural Nets 293 11.1 Introduction 293 11.2 Concept and Structure of a Neural Network 294 11.3 Fitting a Network to Data 295 Example 1: Tiny Dataset 295 Computing Output of Nodes 296 Preprocessing the Data 299 Training the Model 300 Example 2: Classifying Accident Severity 304 Avoiding Overfitting 305 Using the Output for Prediction and Classification 305 11.4 Required User Input 307 11.5 Exploring the Relationship Between Predictors and Outcome 308 11.6 Deep Learning 309 Convolutional Neural Networks (CNNs) 310 Local Feature Map 311 A Hierarchy of Features 311 The Learning Process 312 Unsupervised Learning 312 Example: Classification of Fashion Images 313 Conclusion 320 11.7 Advantages and Weaknesses of Neural Networks 320 Problems 322 Chapter 12 Discriminant Analysis 325 12.1 Introduction 325 Example 1: Riding Mowers 326 Example 2: Personal Loan Acceptance 327 12.2 Distance of a Record from a Class 327 12.3 Fisher’s Linear Classification Functions 329 12.4 Classification Performance of Discriminant Analysis 333 12.5 Prior Probabilities 334 12.6 Unequal Misclassification Costs 334 12.7 Classifying More Than Two Classes 336 Example 3: Medical Dispatch to Accident Scenes 336 12.8 Advantages and Weaknesses 339 Problems 341 Chapter 13 Generating, Comparing, and Combining Multiple Models 345 13.1 Ensembles 346 Why Ensembles Can Improve Predictive Power 346 Simple Averaging or Voting 348 Bagging 349 Boosting 349 Bagging and Boosting in R 349 Stacking 350 Advantages and Weaknesses of Ensembles 351 13.2 Automated Machine Learning (AutoML) 352 AutoML: Explore and Clean Data 352 AutoML: Determine Machine Learning Task 353 AutoML: Choose Features and Machine Learning Methods 354 AutoML: Evaluate Model Performance 354 AutoML: Model Deployment 356 Advantages and Weaknesses of Automated Machine Learning 357 13.3 Explaining Model Predictions 358 13.4 Summary 360 Problems 362 345 Part V Intervention and User Feedback Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 367 14.1 A/B Testing 368 Example: Testing a New Feature in a Photo Sharing App 369 The Statistical Test for Comparing Two Groups (T-Test) 370 Multiple Treatment Groups: A/B/n Tests 372 Multiple A/B Tests and the Danger of Multiple Testing 372 14.2 Uplift (Persuasion) Modeling 373 Gathering the Data 374 A Simple Model 376 Modeling Individual Uplift 376 Computing Uplift with R 378 Using the Results of an Uplift Model 378 14.3 Reinforcement Learning 380 Explore-Exploit: Multi-armed Bandits 380 Example of Using a Contextual Multi-Arm Bandit for Movie Recommendations 382 Markov Decision Process (MDP) 383 14.4 Summary 388 Problems 390 Part VI Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 393 15.1 Association Rules 394 Discovering Association Rules in Transaction Databases 394 Example 1: Synthetic Data on Purchases of Phone Faceplates 394 Generating Candidate Rules 395 The Apriori Algorithm 397 Selecting Strong Rules 397 Data Format 399 The Process of Rule Selection 400 Interpreting the Results 401 Rules and Chance 403 Example 2: Rules for Similar Book Purchases 405 15.2 Collaborative Filtering 407 Data Type and Format 407 Example 3: Netflix Prize Contest 408 User-Based Collaborative Filtering: “People Like You” 409 Item-Based Collaborative Filtering 411 Evaluating Performance 412 Example 4: Predicting Movie Ratings with MovieLens Data 413 Advantages and Weaknesses of Collaborative Filtering 416 Collaborative Filtering vs. Association Rules 417 15.3 Summary 419 Problems 421 Chapter 16 Cluster Analysis 425 16.1 Introduction 426 Example: Public Utilities 427 16.2 Measuring Distance Between Two Records 429 Euclidean Distance 429 Normalizing Numerical Variables 430 Other Distance Measures for Numerical Data 432 Distance Measures for Categorical Data 433 Distance Measures for Mixed Data 434 16.3 Measuring Distance Between Two Clusters 434 Minimum Distance 434 Maximum Distance 435 Average Distance 435 Centroid Distance 435 16.4 Hierarchical (Agglomerative) Clustering 437 Single Linkage 437 Complete Linkage 438 Average Linkage 438 Centroid Linkage 438 Ward’s Method 438 Dendrograms: Displaying Clustering Process and Results 439 Validating Clusters 441 Limitations of Hierarchical Clustering 443 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 444 Choosing the Number of Clusters (k) 445 Problems 450 Part VII Forecasting Time Series Chapter 17 Handling Time Series 455 17.1 Introduction 455 17.2 Descriptive vs. Predictive Modeling 457 17.3 Popular Forecasting Methods in Business 457 Problems 466 Chapter 18 Regression-Based Forecasting 469 18.1 A Model with Trend 469 Linear Trend 469 Exponential Trend 473 Polynomial Trend 474 Problems 489 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 499 19.1 Smoothing Methods: Introduction 500 19.2 Moving Average 500 Centered Moving Average for Visualization 500 Trailing Moving Average for Forecasting 501 Choosing Window Width (w) 504 Problems 516 Part VIII Data Analytics Chapter 20 Social Network Analytics 527 20.1 Introduction 527 20.2 Directed vs. Undirected Networks 529 20.3 Visualizing and Analyzing Networks 530 Plot Layout 530 Edge List 533 Adjacency Matrix 533 Using Network Data in Classification and Prediction 534 Problems 548 Chapter 21 Text Mining 549 21.1 Introduction 549 21.2 The Tabular Representation of Text 550 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 551 Problems 570 Chapter 22 Responsible Data Science 573 22.1 Introduction 573 22.2 Unintentional Harm 574 22.3 Legal Considerations 576 22.4 Principles of Responsible Data Science 577 Non-maleficence 578 Fairness 578 Transparency 579 Accountability 580 Data Privacy and Security 580 Problems 599 Part IX Cases Chapter 23 Cases 603 23.1 Charles Book Club 603 The Book Industry 603 Database Marketing at Charles 604 Machine Learning Techniques 606 Assignment 608 23.2 German Credit 610 Background 610 Data 610 Assignment 614 Index 647

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