Combining psychological support with a project management approach that breaks tasks into small, manageable chunks, experienced dissertation coach Alison Miller shows you how to overcome negativity and succeed in completing your dissertation beyond your own expectations.

    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.

    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.

    Now, in his most comprehensive book yet on the topic, Buzan reveals exciting new ways to improve one's memory, concentration, creativity, ability to learn, and more.

    This accessible book introduces you to landmark studies as well as the cutting-edge research that defines this fascinating field. To help you further experiment with and understand the concepts in the text, you can use COGLAB 2.0: THE ONLINE COGNITIVE PSYCHOLOGY LABORATORY. Available at www.academic.cengage.com, COGLAB contains dozens of classic experiments designed to help you learn about cognitive concepts and how the mind works.

    Some years before that, in 1977, Eco published a little book for his students, "How to Write a Thesis," in which he offered useful advice on all the steps involved in researching and writing a thesis -- from choosing a topic to organizing a work schedule to writing the final draft. Now in its twenty-third edition in Italy and translated into seventeen languages, "How to Write a Thesis "has become a classic. Remarkably, this is its first, long overdue publication in English.Eco's approach is anything but dry and academic. He not only offers practical advice but also considers larger questions about the value of the thesis-writing exercise. "How to Write a Thesis" is unlike any other writing manual. It reads like a novel. It is opinionated. It is frequently irreverent, sometimes polemical, and often hilarious. Eco advises students how to avoid "thesis neurosis" and he answers the important question "Must You Read Books?" He reminds students "You are not Proust" and "Write everything that comes into your head, but only in the first draft." Of course, there was no Internet in 1977, but Eco's index card research system offers important lessons about critical thinking and information curating for students of today who may be burdened by Big Data."How to Write a Thesis" belongs on the bookshelves of students, teachers, writers, and Eco fans everywhere. Already a classic, it would fit nicely between two other classics: "Strunk and White" and "The Name of the Rose."This MIT Press edition will be available in three different cover colors.ContentsThe Definition and Purpose of a ThesisChoosing the TopicConducting ResearchThe Work Plan and the Index CardsWriting the ThesisThe Final Draft

    The 11th edition of this text continues to build on four major marketing themes: building and managing profitable customer relationships, building and managing strong brands to create brand equity, harnessing new marketing technologies in the digital age, and marketing in a socially responsible way around the globe.

    Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.

    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.

    To meet it, you need a fully integrated text and supplements package that sets the stage for a perfectly choreographed learning experience.

    Child Development

    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.

    Not in an editor, IDE, or design tool. You're well educated on how to work with software and hardware, but what about wetware--our own brains? Learning new skills and new technology is critical to your career, and it's all in your head. In this book by Andy Hunt, you'll learn how our brains are wired, and how to take advantage of your brain's architecture. You'll learn new tricks and tips to learn more, faster, and retain more of what you learn. You need a pragmatic approach to thinking and learning. You need to Refactor Your Wetware. Programmers have to learn constantly; not just the stereotypical new technologies, but also the problem domain of the application, the whims of the user community, the quirks of your teammates, the shifting sands of the industry, and the evolving characteristics of the project itself as it is built. We'll journey together through bits of cognitive and neuroscience, learning and behavioral theory. You'll see some surprising aspects of how our brains work, and how you can take advantage of the system to improve your own learning and thinking skills.In this book you'll learn how to:Use the Dreyfus Model of Skill Acquisition to become more expertLeverage the architecture of the brain to strengthen different thinking modesAvoid common "known bugs" in your mindLearn more deliberately and more effectivelyManage knowledge more efficientlyPrinted in full color.

    Each chapter summarizes the clinical presentation, etiology, and diagnosis of the each of the most commonly enountered disorders; reviews the models or theories that guide intervention; and delineates step-by-step protocols for assessment and treatment, illustrated with transcripts and vignettes.

    Research Methodology Book