The book assumes no prior knowledge, taking the student through every stage of their research project in manageable steps. Advice on planning and conducting studies, analyzing data, and writing up practical reports is given, and examples are provided, as well as advice on how to report results in conventional (APA) style. Unlike other introductory texts, there is practical guidance on qualitative research, as well as discussion of issues of bias, interpretation, and variance. Content on qualitative methods has been expanded for the fifth edition and now includes additional material on widely used methods, such as grounded theory, thematic analysis, interpretive phenomenological analysis (IPA), and discourse analysis. The book provides clear coverage of statistical procedures, and includes everything needed at an undergraduate level from nominal level tests, to multi-factorial ANOVA designs, multiple regression, and log linear analysis. In addition, the book provides detailed and illustrated SPSS textbook. Each chapter contains a self-test glossary, key terms, and exercises, ensuring that key concepts have been understood. Students are further supported. Students are further supported by an accompanying website that provides additional exercises, revision flash cards, links to further reading, and data for use with SPSS. The website will also include updated coverage of SPSS should a new version be launched. The bestselling research methods text for over a decade, Research Methods and Statistics in Psychology remains an invaluable resource for students of psychology throughout their studies.

    But before numerical information can be useful it must be analyzed, interpreted, and assimilated. Unfortunately, teaching the techniques for making sense of data has been neglected at all levels of our educational system. As a result, through our culture there is little appreciation of how to effectively use the volumes of data generated by both business and government. This book can remedy that situation. Readers report that this book as changed both the way they look a data and the very form their monthly reports. It has turned arguments about the numbers into a common understanding of what needs to be done about them. These techniques and benefits have been thoroughly proven in a wide variety of settings. Read this book and use the techniques to gain the benefits for your company.

    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.

    What she discovered was the densest concentration of these hawks anywhere in the lower forty-eight states. Why? Houle's findings, eloquently reported, show that ranchers and grazing and wildlife not only can coexist, but in some instances must coexist if we are to save the last of the native prairies for us all.

    Part I asks: Is evolving Nature all there is – self-generated, self-sustaining, self-contained? Are human beings, as the topmost outgrowth of Nature, responsible to none other but themselves? That is the stance of naturalist and atheist Richard Dawkins. Or is evolving reality derived from, and dependent on, a transcendent Source and Destiny, to whom humans are accountable and whose benevolence reaches out to humans as persons because humans are persons? That is the conviction of the Christian faith. Part II shows that Dawkins’ interpretation of religion is deficient even in evolutionary terms and lacks the objectivity and impartiality of genuine science.Backed with in-depth study and thorough research, Richard Dawkins’ God Delusion: A repentant refutation is a masterfully written work that attempts to provide answers to believers and non-believers by presenting scientific and religious reasoning.

    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.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    These areas of knowledge have converged into a modern theory of epidemiology that has been slow to penetrate into textbooks, particularly at the introductory level. Epidemiology: An Introduction closes the gap. It begins with a brief, lucid discussion of causal thinking and causal inference and then takes the reader through the elements of epidemiology, focusing on the measures of disease occurrence and causal effects. With these building blocks in place, the reader learns how to design, analyze and interpret problems that epidemiologists face, including confounding, the role of chance, and the exploration of interactions. All these topics are layered on the foundation of basic principles presented in simple language, with numerous examples and questions for further thought.

    The Second Edition has been carefully updated, including coverage of recent events such as Hurricane Katrina, and offers unparalleled multimedia tools for instructors and students.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Children are given a well-rounded understanding of this beautiful fish: its anatomy, feeding habits and behavior. The following Sharks are featured:* The swift Black Tip Reef Shark* The dangerous Bull Shark* The resourceful Hammerhead Shark* The feared Great White Shark* The stealthy Lemon Shark* The fanged Nurse Shark* The gentle Whale Shark* The deceptive Wobbegong

    

    People need cheering up. I have the answer. ‘Pills, Thrills and Methadone Spills: Adventures of a Community Pharmacist’ is a collection of the best blogs, tweets and anecdotes about the wonderful world of pharmacy.“If the shutter is three quarters down, then we are shut and not just vertically challenged”...“Gave me huge insight into the ‘real’ world of community pharmacy – I didn’t realise just how much pharmacists deal with on a day to day basis, so for me this was very informative, but in a reallyclever, and massively funny way!” Lucy Pitt, Marketing Manager, The Pharmacy Show“As well as being brilliantly funny, this book is a refreshingly honest view of the world of pharmacy. From student pharmacists to the fully-qualified, every chapter provides a story that the reader can relate to and enjoy.” Georgia Salter, Pharmacy Student“A well observed reflection of life in pharmacy with very funny reflections” Catherine Duggan, Royal Pharmaceutical Society"It is always fun to be reminded that pharmacists' perils and fun at the workplace are similar irrespective of which country we practise in!" Selina Hui-Hoong Wee , Pharmacist, Malaysia“A great entertaining and amusing read" Mike Holden, Chief Executive, National Pharmacy AsociationThanks to Laura Martins for her initial book cover design!

    In this book you will find some shocking details of the great figures of science such as Newton, Galileo, Kepler, Copernicus and Kant, presented with the characteristic humor that only a popularizer of science as Sergio de Regules could bring to this wonderful collection of essays.

    Data--our data--is mined and processed for profit, power, and political gain. In Living in Data, Thorp asks a crucial question of our time: How do we stop passively inhabiting data, and instead become active citizens of it?Threading a data story through hippo attacks, glaciers, and school gymnasiums, around colossal rice piles, and over active minefields, Living in Data reminds us that the future of data is still wide open, that there are ways to transcend facts and figures and to find more visceral ways to engage with data, that there are always new stories to be told about how data can be used.Punctuated with Thorp's original and informative illustrations, Living in Data not only redefines what data is, but reimagines who gets to speak its language and how to use its power to create a more just and democratic future. Timely and inspiring, Living in Data gives us a much-needed path forward.