KEY TOPICS: Specific, practical strategies include ways to get students active from the start through activities that build teamwork and immediately get them thinking about the subject matter. 101 activities include ice-breakers for the beginning of class, strategies for the middle of a lesson, and concluding exercises to foster student reflection and future application. In addition, these activities are designed to enliven learning, deepen understanding, and promote retention. Designed for the preservice and inservice teacher, this book is effective for anyone teaching in middle schools, high schools, colleges, and centers for adult education. For professionals working in middle school/secondary school education.

    

    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.

    Written for the prospective or new middle and high school-level teacher, the text's content is ready to be applied in a classroom setting. The book addresses the planning decisions teachers must make, including arranging the physical space; creating a positive climate; establishing expectations, rules, and procedures; planning and conducting instruction; encouraging appropriate behavior; addressing problem behavior; and using good communication skills, with particular attention paid to the growth of diverse and inclusive classrooms.

    The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Written in uncommonly engaging, lucid, and elegant prose, this text is an understand-it-yourself, do-it-yourself manual designed to help research students in any discipline understand the fundamental structure of quality research and the methodical process that leads to genuinely significant results. It guides the reader, step-by-step, from the selection of a problem to study, through the process of conducting authentic research, to the preparation of a completed report, with practical suggestions based on a solid theoretical framework and sound pedagogy. Suited for the core text in any introductory research course or even for self-instruction, this text will show students two things: 1) that quality research demands planning and design; and, 2) how their own research projects can be executed effectively and professionally.

    KEY TOPICS: This book familiarizes the reader with the basics of test construction and prepares the reader to effectively evaluate different tests, choose tests for particular purposes and individual examines, and interpret scores properly.

    While other companies can improve productivity through the use of new technologies or better systems, these approaches are not available in the arts. Hamlet requires the same number of performers today as it did in Shakespeare’s time. The New York Philharmonic requires the same number of musicians now as it did when Tchaikovsky conducted it over one hundred years ago. Costs go up, but the size of theaters and the price resistance of patrons limit what can be earned from ticket sales. Therefore, the performing arts industry faces a severe gap between earnings and expenses. Typical approaches to closing the gap—raising ticket prices or cutting artistic or marketing expenses—don’t work. What, then, does it take to create and maintain a healthy arts organization? Michael M. Kaiser has revived four major arts organizations: the Kansas City Ballet, the Alvin Ailey American Dance Theater, American Ballet Theatre, and London’s Royal Opera House. In The Art of the Turnaround he shares with readers his ten basic rules for bringing financially distressed arts organizations back to life and keeping them strong. These rules cover the requirements for successful leadership, the pitfalls of cost cutting, the necessity of extending the programming calendar, the centrality of effective marketing and fund raising, and the importance of focusing on the present with a positive public message. In chapters organized chronologically, Kaiser brings his ten rules vividly to life in discussions of the four arts organizations he is credited with saving. The book concludes with a chapter on his experiences at the John F. Kennedy Center for the Performing Arts, an arts organization that needed an artistic turnaround when he became the president in 2001 and that today exemplifies in practice many of the ten rules he discusses throughout his book.

    MATLAB has been incorporated throughtout, both in examples and problems.

    The author investigates three converging elements in his examination of human differences: individual attitudes and behaviors, cultural expectations, and institutional policies and practices. This examination provides the basis for the conceptual organization of the text.

    In this book, a wealth of exercises are provided throughout each section, designed to reinforce learning and the logical comprehension of topics. The use of real data is incorporated much more extensively than in any other book on the market. Consist of strong coverage of computer-based methods, especially in the coverage of analysis of variance and regression. This text stresses mastery of methods most often used in medical research, with specific reference to actual medical literature and actual medical research. The approach minimizes mathematical formulation, yet gives complete explanations of all important concepts. Every new concept is systematically developed through completely worked-out examples from current medical research problems. Computer output is used to illustrate concepts when appropriate.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    It provides guidance to help you apply best practices in the design, delivery, and maintenance of AWS environments. This documentation is offered for free here as a Kindle book, or you can read it in PDF format at https://aws.amazon.com/whitepapers/.