Reciprocal Teaching at Work: Powerful Strategies and Lessons for Improving Reading Comprehension, 2nd Edition Review

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(More customer reviews)I am in New Zealand so it costs to get a book posted to here, so I think twice, before I buy. I have been reading through it and am well pleased with the purchase. I have dabbled in reciprocal reading and have used Jill Eggleton's Connectors which are very good. However this book provides me with the support that I need to run reciprocal reading in a variety of contexts. It is explicit and provides me with the information that I need to go ahead in 2011. I teach Year 6 - 8, thats about Grade 5 - 7 in the U.S. I think. However any teacher from early grades up would benefit from this book.

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In this much anticipated, revised, and expanded edition of her bestseller Reciprocal Teaching at Work, Lori Oczkus continues to provide solutions for teaching comprehension. By focusing on four evidence-based and classroom-tested strategies that good readers use to comprehend text-predicting, questioning, clarifying, and summarizing-Lori shows you new ways to use reciprocal teaching to improve students comprehension while actively engaging them in learning and encouraging independence.
This second edition is jam-packed with fresh material including
* A new chapter on getting started with reciprocal teaching * Dozens of creative, exciting lessons and tips for using reciprocal teaching in whole-class settings, guided reading groups, and literature circles * Ideas for differentiating instruction for struggling readers and English language learners * Expanded suggestions for grades K5 and all new ideas for grades 612 * Practical ways to use reciprocal teaching as a Response to Intervention (RTI) * Support materials such as reproducibles, posters, and a lesson planning menu * A free online professional development guide and free online classroom video clips
With 35 lessons and a wealth of materials to get you started-and keep you going-this is the all-inclusive resource you need to lead your students to become active, engaged, and independent readers who truly comprehend what they read.
The International Reading Association is the world's premier organization of literacy professionals. Our titles promote reading by providing professional development to continuously advance the quality of literacy instruction and research.
Research-based, classroom-tested, and peer-reviewed, IRA titles are among the highest quality tools that help literacy professionals do their jobs better.
Some of the many areas we publish in include:
-Comprehension-Response To Intervention/Struggling Readers-Early Literacy -Adolescent Literacy-Assessment-Literacy Coaching-Research And Policy

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Experimental Design and Data Analysis for Biologists Review

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(More customer reviews)Comprehensible, comprehensive, and interesting statistical texts are as rare as the Tasmanian Tiger. This text is very comprehensive and is loaded with interesting examples and does an excellent job of presenting the scientific method. It's not always as easy to follow as I would like, but I deal with this by recommending to my students that they read the assignment after, instead of before the lecture. I cover the first 12 chapters (through covariance) in my upper division Biometry course. I'm an ecologist and have enjoyed learning much that was never covered in my undergraduate biometry and two graduate statistics courses. This would be an excellent text for graduate courses.

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This essential textbook is designed for students or researchers in biology who need to design experiments, sampling programs, or analyze resulting data. The text begins with a revision of estimation and hypothesis testing methods, before advancing to the analysis of linear and generalized linear models. The chapters include such topics as linear and logistic regression, simple and complex ANOVA models, log-linear models, and multivariate techniques. The main analyses are illustrated with many examples from published papers and an extensive reference list to both the statistical and biological literature is also included. The book is supported by a web-site that provides all data sets, questions for each chapter and links to software.

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Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics) Review

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(More customer reviews)Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.
The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.
Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.
A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).

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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

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A Modern Approach to Regression with R Review

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(More customer reviews)The back cover of the book is, in my view, quite accurate. It covers a traditional topic, regression, from a modern perspective. By that it means "assumes interactive use of software" and is very focused on formulating and answering questions about data rather than prescribing a certain course of action. It is not a textbook on "modern" regression such as smoothing or the like, though there is a chapter on that, along with logistic regression, basic time series and mixed models. The fact that all the datasets and examples are freely available and that the author doesn't skip steps in his treatment is a HUGE benefit to instructors using this book for class or those engaged in self-study. I am giving this book a spin in my class in the fall....

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This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.--This text refers to the Hardcover edition.

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Business Statistics: Contemporary Decision Making Review

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(More customer reviews)Very good book for business majors taking a statistics class. Statistics is not easy to understand, but this book tries to make it as painless as possible. The flow is easy to follow and the examples make it all the more understandable.

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Help your students see the light.
With its myriad of techniques, concepts and formulas, business statistics can be overwhelming for many students. They can have trouble recognizing the importance of studying statistics, and making connections between concepts.
Ken Black's fifth edition of Business Statistics: For Contemporary Decision Making helps students see the big picture of the business statistics course by giving clearer paths to learn and choose the right techniques.
Here's how Ken Black helps students see the big picture:
Video Tutorials-In these video clips, Ken Black provides students with extra learning assistance on key difficult topics. Available in WileyPLUS.
Tree Taxonomy Diagram-Tree Taxonomy Diagram for Unit 3 further illustrates the connection between topics and helps students pick the correct technique to use to solve problems.
New Organization-The Fifth Edition is reorganized into four units, which will help professor teach and students see the connection between topics.

WileyPLUS-WilePLUS provides everything needed to create an environment where students can reach their full potential and experience the exhilaration of academic success. In addition to a complete online text, online homework, and instant feedback, WileyPLUS offers additional Practice Problems that give students the opportunity to apply their knowledge, and Decision Dilemma Interactive Cases that provide real-world decision-making scenarios.Learn more at www.wiley.co,/college/wileyplus.

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Business Statistics: Contemporary Decision Making Review

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(More customer reviews)I love this text. It is easy to understand the demonstration problems are great and well explained. The problem sets are enjoyable and the answers in the back to the odd problems is a great feature. I think more needs to be included in the index - more pages and references to locate specific items in the text. Overall, great text and well written

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Help your students see the light.
With its myriad of techniques, concepts and formulas, business statistics can be overwhelming for many students. They can have trouble recognizing the importance of studying statistics, and making connections between concepts.
Ken Black's fifth edition of Business Statistics: For Contemporary Decision Making helps students see the big picture of the business statistics course by giving clearer paths to learn and choose the right techniques.
Here's how Ken Black helps students see the big picture:
Video Tutorials-In these video clips, Ken Black provides students with extra learning assistance on key difficult topics. Available in WileyPLUS.

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Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) Review

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(More customer reviews)Note, this is a review of the first edition.
Overview
This book was the textbook used at the University of Wisconsin-Madison for the graduate course in Bayesian Decision and Control I during the fall of 2001 and 2002. It strikes a good balance between theory and practical example, making it ideal for a first course in Bayesian theory at an intermediate-advanced graduate level. Its emphasis is on Bayesian modeling and to some degree computation.
Prerequisites
While no Bayesian theory is assumed, it is assumed that the reader has a background in mathematical statistics, probability and continuous multi-variate distributions at a beginning or intermediate graduate level. The mathematics used in the book is basic probability and statistics, elementary calculus and linear algebra.
Intended audience
This book is primarily for graduate students, statisticians and applied researchers who wish to learn Bayesian methods as opposed to the more classical frequentist methods.
Material covered
It covers the fundamentals starting from first principles, single-parameter models, multi-parameter models, large sample inference, hierarchical models, model checking and sensitivity analysis (model checking and sensitivity analysis are especially well covered), study design, regression models, generalized linear models, mixture models and models for missing data. In addition it covers posterior simulation and integration using rejection sampling and importance sampling. There is one chapter on Markov chain Monte Carlo simulation (MCMC) covering the generalized Metropolis algorithm and the Gibbs sampler.
Over 38 models are covered, 33 detailed examples from a wide range of fields (especially biostatistics). Each of the 18 chapter has a bibliographic note at the end. There are two appendixes: A) a very helpful list of standard probability distributions and B) outline of proofs of asymptotic theorems.
Sixteen of the 18 chapters end with a set of exercises that range from easy to quite difficult. Most of the students in my fall 2001 class used the statistical language R to do the exercises.
The book's emphasis is on applied Bayesian analysis. There are no heavy advanced proofs in the book. While the proofs of the basic algorithms are covered there are no algorithms written in pseudo code...Additional books of related interest
1) Statistical Decision Theory and Bayesian Analysis, James Berger, second edition. Emphasis on decision theory and more difficult to follow than Gelman's book. Covers empirical and hierarchical Bayes analysis. More philosophical challenging than Gelman's book.
2) Monte Carlo Statistical Methods, Robert and Casella. Very mathematically oriented book. Does a good job of covering MCMC.
3) Monte Carlo Methods in Bayesian Computation, Ming-Hui Chen, Qi-Man Shao, Joseph George Ibrahim. An enormous number of algorithms related to MCMC not covered elsewhere. If you need MCMC and need an algorithm to implement MCMC this is the book to read.
4) Monte Carlo Strategies in Scientific Computing, Jun S. Liu. Covers a wide range of scientific disciplines and how Monte Carlo methods can be used to solve real world problems. Includes hot topics such as bioinformatics. Very concise. Well written, but requires effort to understand as so many different topics are covered. This book is my most often borrowed book on Monte Carlo methods. Jun S. Liu is a big gun at Harvard.
5) Probabilistic Networks and Expert Systems. Cowell, Dawid, Lauritzen, Spiegelhalter. Covers the theory and methodology of building Bayesian networks (probabilistic networks).

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Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. 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.

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