No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). This text may be used in a one or two course sequence. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

    What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

    We can no longer see ourselves as separate from the 'great chain of being,' and we can no longer see this as a non-religious issue. Francis intuited all of this 800 years ago. —Father Richard Rohr, O.F.M., Center for Action and Contemplation, Albuquerque, New Mexico Three of the greatest minds in Franciscan theology, Ilia Delio, O.S.F., Franciscan Keith Douglass Warner, O.F.M., and Pamela Wood, come together to discuss one of the greatest crises of our time—the destruction of the Earth. This book takes both a theological and practical approach to developing a Franciscan spirituality of the earth. Four sections highlight the distinct relationships creation has with the world: incarnation, community, contemplation and conversion. In this meticulously researched book, the authors propose ways in which we can all understand our own roles in relationship to the Earth and ways in which we can make it better. Each section offers reflective action opportunities designed to bring the book's ecological and theological insights into the reader's daily life and nurture a Franciscan spirituality of the earth. Prayers, meditations, spiritual practices and group activities are offered which provide a practical hands-on approach to reconnecting with the earth and acting in right relationship. "Earth, with all its creatures, is in crisis. And responding to this crisis will require every possible resource of our human community. One of the most precious of these resources is the Franciscan tradition. It is a joy to welcome this book as a wise, thoughtful, inspiring and practical contribution to ecological theology, grounded in ancient Christian tradition that sees Earth as our sister and mother. Care for Creation is part of a wider retrieval of Franciscan theology for our new time, but is unique in this blend of three interrelated disciplines, scientifically informed ecology, theology and the practice of reflective action." —From the Foreword by Denis Edwards

    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.

    This book has two underlying themes that are more or less independent of the statistical hypothesis tests that are the main content of the book. The first theme is the importance of looking at the data before formulating a hypothesis. With this in mind, the author discusses, in detail, plotting data, looking for outliers, and checking assumptions (Graphical displays are used extensively). The second theme is the importance of the relationship between the statistical test to be employed and the theoretical questions being posed by the experiment. To emphasize this relationship, the author uses real examples to help the student understand the purpose behind the experiment and the predictions made by the theory. Although this book is designed for students at the intermediate level or above, it does not assume that students have had either a previous course in statistics or a course in math beyond high-school algebra.

    It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.

    Even the simplest numbers can become powerful forces when manipulated by politicians or the media, but in the case of the law, your liberty -- and your life -- can depend on the right calculation. In Math on Trial, mathematicians Leila Schneps and Coralie Colmez describe ten trials spanning from the nineteenth century to today, in which mathematical arguments were used -- and disastrously misused -- as evidence. They tell the stories of Sally Clark, who was accused of murdering her children by a doctor with a faulty sense of calculation; of nineteenth-century tycoon Hetty Green, whose dispute over her aunt's will became a signal case in the forensic use of mathematics; and of the case of Amanda Knox, in which a judge's misunderstanding of probability led him to discount critical evidence -- which might have kept her in jail. Offering a fresh angle on cases from the nineteenth-century Dreyfus affair to the murder trial of Dutch nurse Lucia de Berk, Schneps and Colmez show how the improper application of mathematical concepts can mean the difference between walking free and life in prison. A colorful narrative of mathematical abuse, Math on Trial blends courtroom drama, history, and math to show that legal expertise isn't't always enough to prove a person innocent.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    But new technology using concentrated sunlight to provide intense heat energy that can be effectively stored overcomes this problem, providing clean renewable power around the clock. Further, the large amounts of power produced can be used to tackle the issue of feeding the world’s growing population—by enabling energy-intense methods of purifying polluted land for agricultural production.Drawing carbon out of the atmosphere is an essential component in limiting climate change. Flannery explores the potential of kelp, a fast-growing sea algae, to be used on a large scale to convert carbon from the air to a non-gaseous form, reducing levels of atmospheric carbon.With accessible and engaging explanations of the fascinating science behind these technologies, as well as accounts of the systems already in operation around the world, Sunlight and Seaweed is an enlightening and uplifting view of the future.

    It helps in the process of choosing the right statistical technique and includes a detailed guide to interpreting SPSS ouput.

     He ordered a superhuman effort to make Britain a nuclear power. Although Britain had been a junior partner in the Manhattan Project which had produced the American bombs, no British scientist had more than partial knowledge of the complex physics involved. The war over, the Americans cut off all help. At a time of daunting economic difficulty and amid the growing tension of the Cold War, the project hurriedly took shape behind a cloak of almost paranoid secrecy and in an atmosphere of constant stringency and shortage. Brian Cathcart’s book ranges over politics, diplomacy, espionage and science, but above all it tells the story of the brilliant young scientist William Penney, his team and their struggle. The men who worked behind the security fences at Aldermaston have been allowed to speak. The tales include fearsome risks, vast resourcefulness, bureaucratic obstruction, naval intransigence and a measure of black humour. The veil is also lifted on the extraordinary contribution of Klaus Fuchs, the Soviet spy. Finally the high drama of the test itself, conducted off the coast of Australia after a naval operation which came close to total fiasco, is recounted in gripping detail. Test of Greatness draws on what at the time the book was published were newly declassified documents. Cathcart also speaks uses primary sources, such as the words of the participants, illustrating and illuminating in vivid, human terms a secret but crucial chapter of post-war British history. Praise for Brian Cathcart: ‘The story of the British bomb mixes science, politics, espionage, Essex and morality. A nation is changed for ever when it decides to become a nuclear power. Brian Cathcart takes this complicated array of factors and makes them rise out of the page and walk to a very wide audience.’ – Sir Peter Hennessy, military historian Brian Cathcart was Assistant Editor of the Independent on Sunday when he wrote Test of Greatness. Since then he has taken up a position at Kingston University London and founded Hacked Off in the aftermath of the tabloid phone-hacking scandal. He has just published his eighth book, The News from Waterloo. His previous works include accounts of the murders of Jill Dando and Stephen Lawrence.

    This book contains articles available free on Wikipedia and other free sources.

    . ." and "The data bear this out. . . ." But the field of statistics is not just about data. Statistics is the entire process involved in gathering evidence to answer questions about the world, in cases where that evidence happens to be numerical data. Statistics For Dummies is for everyone who wants to sort through and evaluate the incredible amount of statistical information that comes to them on a daily basis. (You know the stuff: charts, graphs, tables, as well as headlines that talk about the results of the latest poll, survey, experiment, or other scientific study.) This book arms you with the ability to decipher and make important decisions about statistical results, being ever aware of the ways in which people can mislead you with statistics. Get the inside scoop on number-crunching nuances, plus insight into how you canDetermine the odds Calculate a standard score Find the margin of error Recognize the impact of polls Establish criteria for a good survey Make informed decisions about experiments This down-to-earth reference is chock-full of real examples from real sources that are relevant to your everyday life: from the latest medical breakthroughs, crime studies, and population trends to surveys on Internet dating, cell phone use, and the worst cars of the millennium. Statistics For Dummies departs from traditional statistics texts, references, supplement books, and study guides in the following ways:Practical and intuitive explanations of statistical concepts, ideas, techniques, formulas, and calculations. Clear and concise step-by-step procedures that intuitively explain how to work through statistics problems. Upfront and honest answers to your questions like, "What does this really mean?" and "When and how I will ever use this?" Chances are, Statistics For Dummies will be your No. 1 resource for discovering how numerical data figures into your corner of the universe.

    Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress." Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the "laws of the medium numbers"-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on "na�ve empiricism," but can be sometimes be estimated via parametric methods. - The "empirical distribution" is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many "biases" found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.