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.

    This text has invited more than 4 million students into the study of this dynamic and essential discipline.The authors have restructured each chapter around a conceptual framework of five or six big ideas. An Overview draws students in and sets the stage for the rest of the chapter, each numbered Concept Head announces the beginning of a new concept, and Concept Check questions at the end of each chapter encourage students to assess their mastery of a given concept. New Inquiry Figures focus students on the experimental process, and new Research Method Figures illustrate important techniques in biology. Each chapter ends with a Scientific Inquiry Question that asks students to apply scientific investigation skills to the content of the chapter.

    "Using Econometrics: A Practical Guide "provides readers with a practical introduction that combines single-equation linear regression analysis with real-world examples and exercises. This text also avoids complex matrix algebra and calculus, making it an ideal text for beginners. New problem sets and added support make "Using Econometrics" modern and easier to use.

    Aimed at accountants or readers of other career paths, this book helps them build their decision making skills and understand how to use accounting information to make quality business decisions.

    For introductory courses in computer concepts or computer literacy often including instruction in Microsoft Office.A Computer Concepts Text Focused on Today’s Student!Technology in Action engages students by combining a unique teaching approach with rich companion media.

    This title presents the use of focus companies and the financial statements. The decision-making focus shows the relevance of financial accounting regardless of whether or not the student has chosen to major in accounting.

    This book provides an applications-oriented introduction to multivariate data analysis for the non-statistician, by focusing on the fundamental concepts that affect the use of specific techniques.

    The author's writing has been praised for anticipating readers' questions, and appeals to their need to learn visually and by solving problems. Emphasizing that learners should reason their way to solutions rather than memorize facts, Bruice encourages them to think about what they have learned previously and apply that knowledge in a new setting.

    Students will receive a grounding in areas that vary from treatment to organizational and community settings. This edition also includes of new case studies, practice examples and guiding principles.

    But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

    Steven M. Cahn brings together a rich, balanced, and wide-ranging collection of forty classic and contemporary readings. Most importantly, he has carefully edited the articles so that they will be exceptionally clear and understandable to undergraduate students. The selections are organized into three parts, providing instructors with flexibility in designing and teaching a variety of ethics courses. The first part, Challenges to Morality, considers the overly simple assumptions that beginning students may bring to moral issues. The second part, Moral Theories, provides selections from the most influential ethical theories of the past along with commentary by contemporary thinkers. The third part, Moral Problems, offers opposing perspectives on nine controversial issues: abortion, euthanasia, world hunger, terrorism, pornography, affirmative action, animal rights, the environment, and capital punishment. Exploring Ethics features selections from Plato, Aristotle, Hobbes, Kant, Mill, Nietzsche, Thomas Nagel, James Rachels, John Rawls, Tom Regan, Peter Singer, Susan Wolf, and many others. Each reading is preceded by a detailed introduction and followed by study questions that encourage students to think philosophically. Ideal for courses in introduction to ethics or contemporary moral problems, this unique anthology can be used independently or as a companion to a single-authored text. An Instructor's Manual and Companion Website accompany the text. The instructor's materials include a testbank, key terms with definitions, reading summaries, and PowerPoint lectur

    Revised from the 1989 edition not so much with new information, which has not changed much at the introductory level, bu

    Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong."Statistics Done Wrong" comes to the rescue with cautionary tales of all-too-common statistical fallacies. It'll help you see where and why researchers often go wrong and teach you the best practices for avoiding their mistakes.In this book, you'll learn: - Why "statistically significant" doesn't necessarily imply practical significance- Ideas behind hypothesis testing and regression analysis, and common misinterpretations of those ideas- How and how not to ask questions, design experiments, and work with data- Why many studies have too little data to detect what they're looking for-and, surprisingly, why this means published results are often overestimates- Why false positives are much more common than "significant at the 5% level" would suggestBy walking through colorful examples of statistics gone awry, the book offers approachable lessons on proper methodology, and each chapter ends with pro tips for practicing scientists and statisticians. No matter what your level of experience, "Statistics Done Wrong" will teach you how to be a better analyst, data scientist, or researcher.

    The applications and computations in operations research are emphasized. Significantly revised, this text streamlines the coverage of the theory, applications, and computations of operations research. Numerical examples are effectively used to explain complex mathematical concepts. A separate chapter of fully analyzed applications aptly demonstrates the diverse use of OR. The popular commercial and tutorial software AMPL, Excel, Excel Solver, and Tora are used throughout the book to solve practical problems and to test theoretical concepts. New materials include Markov chains, TSP heuristics, new LP models, and a totally new simplex-based approach to LP sensitivity analysis.

    While such visualizations can better inform us, they can also deceive by displaying incomplete or inaccurate data, suggesting misleading patterns—or simply misinform us by being poorly designed, such as the confusing “eye of the storm” maps shown on TV every hurricane season.Many of us are ill equipped to interpret the visuals that politicians, journalists, advertisers, and even employers present each day, enabling bad actors to easily manipulate visuals to promote their own agendas. Public conversations are increasingly driven by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie teaches us how to do just that.