The specialities of each individual number, from zero to nine, and the little mathematical tricks as shown by Shakuntala Devi, all combine to make the reader learn to befriend numbers and excel at maths.

    With a new introduction, three new chapters, modernized language and methods throughout, and an appendix of challenging and enjoyable practice problems, Calculus Made Easy has been thoroughly updated for the modern reader.

    Worked examples and exercises support the text. An ELBS/LPBB edition is available.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    GRAPH THEORY WITH APPLICATIONS TO ENGINEERING AND COMPUTER SCIENCE-PHI-DEO, NARSINGH-1979-EDN-1

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    

    It is how China and India began to embrace neoliberal ideas of economics and attributed a sense of dignity and liberty to the bourgeoisie they had denied for so long. The result was an explosion in economic growth and proof that economic change depends less on foreign trade, investment, or material causes, and a whole lot more on ideas and what people believe.Or so says Deirdre N. McCloskey in Bourgeois Dignity, a fiercely contrarian history that wages a similar argument about economics in the West. Here she turns her attention to seventeenth- and eighteenth-century Europe to reconsider the birth of the industrial revolution and the rise of capitalism. According to McCloskey, our modern world was not the product of new markets and innovations, but rather the result of shifting opinions about them. During this time, talk of private property, commerce, and even the bourgeoisie itself radically altered, becoming far more approving and flying in the face of prejudices several millennia old. The wealth of nations, then, didn’t grow so dramatically because of economic factors: it grew because rhetoric about markets and free enterprise finally became enthusiastic and encouraging of their inherent dignity.An utterly fascinating sequel to her critically acclaimed book The Bourgeois Virtues, Bourgeois Dignity is a feast of intellectual riches from one of our most spirited and ambitious historians—a work that will forever change our understanding of how the power of persuasion shapes our economic lives.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    In The Soul of Capitalism, Greider examines how the greatest wealth-creation engine in the history of the world is failing most of us, why it must be changed, and how specifically it can be transformed. Brilliantly perceptive and sweeping in its ambition, The Soul of Capitalism is also hard-headed and practical, as Greider, one of our most eloquent populist spokesmen, assures us we are not powerless. He illustrates how American capitalism can be aligned more faithfully and obediently with what people want and need in their lives, with what American society needs for a healthy, balanced, and humane future. He proves that it is within our power to reinvent capitalism to make it work for us. The Soul of Capitalism -- solid, pragmatic, visionary, optimistic -- addresses the nation's most urgent needs.

    Widely praised and much-discussed, this classic book explores how cooperation can emerge in a world of self-seeking egoists—whether superpowers, businesses, or individuals—when there is no central authority to police their actions. The problem of cooperation is central to many different fields. Robert Axelrod recounts the famous computer tournaments in which the “cooperative” program Tit for Tat recorded its stunning victories, explains its application to a broad spectrum of subjects, and suggests how readers can both apply cooperative principles to their own lives and teach cooperative principles to others.

    In this unconventional introduction, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Unlike most popular physics books—which give readers a taste of what physicists know but shy away from equations or math—Susskind and Hrabovsky actually teach the skills you need to do physics, beginning with classical mechanics, yourself. Based on Susskind's enormously popular Stanford University-based (and YouTube-featured) continuing-education course, the authors cover the minimum—the theoretical minimum of the title—that readers need to master to study more advanced topics.An alternative to the conventional go-to-college method, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.