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Join the RBI—a group of teen agents with special gifts—on a series of action-packed adventures as they travel the world! The RBI team begin to link together the hidden clues that they have found on their last few missions and the results point them towards Antarctica and an ancient ice station. However, terrible blizzards, floating ice, and a man who swims with the seals make their hunt for a lost artifact anything but easy!

Join the RBI—a group of teen agents with special gifts—on a series of action-packed adventures as they travel the world! The RBI team begin to link together the hidden clues that they have found on their last few missions and the results point them towards Antarctica and an ancient ice station. However, terrible blizzards, floating ice, and a man who swims with the seals make their hunt for a lost artifact anything but easy!

Join the RBI—a group of teen agents with special gifts—on a series of action-packed adventures as they travel the world! The coast of Japan is being terrorized by a mysterious creature. The RBI are sent to investigate sightings of red glowing eyes that peer out of sea mists and an enormous beast that breathes fire and tramples cars. A trip to Japan reveals a bizarre inventor and stories of a slumbering sea dragon. Is their mission linked to the old legend of the “Dragon’s Triangle” and its tales of disappearing ships?

The Florida swamplands are home to hungry gators, wild electrical storms, and a most unusual creature. Sightings of a strange lizard-like animal reach Ripley High and the RBI are sent to investigate. During their search, the RBI agents find themselves in

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Join the RBI—a group of teen agents with special gifts—on a series of action-packed adventures as they travel the world! A plane crash ten years ago and reports of a strange wolf boy lead the RBI to the wilds of western China and to a girl who thinks that the creature might be her long-lost brother. But the mountains and surrounding jungle are full of dangers. Can the team brave hungry tigers, snarling wolves and treacherous ravines to uncover the truth? A dangerous mission for the RBI.

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.