It explores the critical concepts while also examining why they are relevant. The core content is presented within the framework of predicting products, proposing mechanisms, and solving synthesis problems. Readers will fine-tune the key skills involved in solving those types of problems with the help of interactive, step-by-step instructions and problems.

    Providing a concise introduction to abstract algebra, this work unfolds some of the fundamental systems with the aim of reaching applicable, significant results.

    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.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    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.

    Arnold, CHOICE"The book provides readers with a clear, sympathetic and unbiased understanding of the four conflicting visions of curriculum that will enable them to more productively interact with educators who might hold different beliefs. The book stimulates readers to better understand their own beliefs and also to provide them with an understanding of alternate ways of thinking about the fundamental goals of education" --SIRREADALOT.ORG"A much needed, insightful view of alternative curriculum orientations. This is an exceptionally written book that will be useful to teachers, curriculum workers, and school administrators."--Marc Mahlios, University of Kansas"Curriculum Theory: Conflicting Visions and Enduring Concerns is a thought provoking text that invites self-analysis."--Lars J. Helgeson, University of North DakotaCurriculum Theory: Conflicting Visions and Enduring Concerns presents a clear, unbiased, and rigorous description of the major curriculum philosophies that have influenced educators and schooling over the last century. Author Michael Stephen Schiro analyzes four educational visions--Scholar Academic, Social Efficiency, Learner Centered, and Social Reconstruction--to enable readers to reflect on their own educational beliefs and allow them to more productively interact with educators who might hold different beliefs.Key FeaturesProvides a historical perspective on the origins of curriculum ideologies: The book places our current educational debates and issues in a historical context of enduring concerns.Offers a model of how educational movements can be critically analyzed: Using a post-structuralist perspective, this model enables readers to more effectively contribute to the public debate about educational issues.Pays careful attention to the way language is used by educators to give meaning to frequently unspoken assumptions: The text's examination helps readers better understand curricular disagreements that occur in schools.Highlights the complexities of curriculum work in a social context: With an understanding of the ideological pressures exerted on them by society and colleagues, readers can put these pressures in perspective and maintain their own values, beliefs, and practices.Intended AudienceThis book is designed as a supplemental text for advanced undergraduate and graduate courses such as Curriculum Theory, Introduction to Curriculum and Instruction, Curriculum Philosophy, and Curriculum Theory and Practice in the department of education.Talk to the author! schiro@bc.eduTo visit the author's web site, please visit: http: //www2.bc.edu/ schiro/sage.html.

    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.

    Designed for two-semester, beginning graduate courses in Mathematical Statistics, and for senior undergraduate Mathematics, Statistics, and Actuarial Science majors, this text retains its ongoing features and continues to provide students with background material.

    

    Problems are designed to progressively enhance MATLAB-use proficiency, so students need not be familiar with MATLAB at the start of your course. Program scripts that are answers to exercises in the text are available at no charge in electronic form (see Teaching Resources below). *Supplement and Review Mini-Chapters after each of the text's three parts contain an extensive review list of terms, test-like problem sets with answers, and detailed suggestions on supplemental reading to reinforce students' learning and help them prepare for exams. *Read-Only Chapters, strategically placed to provide a change of pace during the course, provide informative, yet enjoyable reading for students. *Measurement Details and Results samples offer students a realistic perspective on the seldom-perfect nature of device characteristics, contrary to the way they are often represented in introductory texts. Content Highlig

    You will have numerous opportunities to practice statistical techniques through learning checks, examples, demonstrations, and problems. Exam preparation is made easy with a student companion website that provides tutorials, crossword puzzles, flashcards, learning objectives, and more!

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    This book provides the first complete identification guides to the birds of this teeming tropical paradise. It gives descriptions of 820 regional species, illustrated in 88 specially commissioned color plates accompanied by notes detailing distinctive features and habitats. Entries cover nomenclature, plumage, markings, voice, global range, distribution and regional status, habits, and diet. The main text gives practical information on where to find many exotic species, citing major birdwatching locations. Introductory chapters discuss habitats, climate, land-use, and conservation concerns. Professional ornithologists and amateur bird watchers alike will find this the indispensable bird guide for eastern Malaysia and western Indonesia for many years to come. It is also an unrivalled source of information for casual travellers and ecotourists.