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Learn to use one of the most popular CSS frameworks and build mobile-friendly web pages. Used for numerous websites and applications, Bootstrap is a key tool for modern web development. You will explore the grid system and then be introduced to the power of Bootstrap in practical projects. You’ll make navigation bars, use themes and styling, create and manipulate cover pages, admin dashboards, forms, and modal dialogs. You’ll learn to use Scrollspy and create tooltips and popovers. Today's web is responsive and Bootstrap continues to be at the forefront with web professionals. Learn by doing with Practical Bootstrap today. What You Will Learn Review how the grid system applies to Bootstrap Create stunning cover pages that encompass a large background image Build an admin dashboard page that changes its layout according to a device’s display width Work with the modal HTML markup and its main parts Customize modal behavior by setting various options using JavaScript Integrate Bootstrap JavaScript libraries with your own HTML page Add scroll spying functionality to your long-content pages Adjust the offset and activation point of the scroll sections Who This Book Is For Anyone who wants to learn how to use Bootstrap. You should have knowledge of HTML, CSS and basic JavaScript.
Discover how easy it is to design killer interfaces and responsive websites with the Bootstrap framework. This practical book gets you started building pages with Bootstrap’s HTML/CSS-based tools and design templates right away. You’ll also learn how to extend your app with interactive features, using its suite of jQuery plugins—often without writing a single line of code. Developed by Twitter and available free from GitHub, Bootstrap supports responsive design by dynamically adjusting your web page layout. With just a basic knowledge of HTML, CSS, and JavaScript, you can build apps that work equally well on desktop screens, smartphones, and tablets. This book shows you how. Become familiar with Bootstrap’s file structure, grid systems, and container layouts Learn HTML elements for typography, code, tables, forms, buttons, images, and icons Design interfaces and other web elements, such as navigation, breadcrumbs, and custom modal windows Use jQuery plugins for features such as revolving slideshows, tabbable interfaces, and dropdown menus Modify everything from column count to typography colors with the LESS stylesheet language
Web design is the process of creating websites. It encompasses several different aspects, including webpage layout, content production, and graphic design. This book offers you everything you need to know for building your websites.
Disk contains the library functions and documentation for use with Splus for Windows.
A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
A hands-on guide to using R to carry out key statistical practices in educational and behavioral sciences research Computing has become an essential part of the day-to-day practice of statistical work, broadening the types of questions that can now be addressed by research scientists applying newly derived data analytic techniques. Comparing Groups: Randomization and Bootstrap Methods Using R emphasizes the direct link between scientific research questions and data analysis. Rather than relying on mathematical calculations, this book focus on conceptual explanations and the use of statistical computing in an effort to guide readers through the integration of design, statistical methodology, and computation to answer specific research questions regarding group differences. Utilizing the widely-used, freely accessible R software, the authors introduce a modern approach to promote methods that provide a more complete understanding of statistical concepts. Following an introduction to R, each chapter is driven by a research question, and empirical data analysis is used to provide answers to that question. These examples are data-driven inquiries that promote interaction between statistical methods and ideas and computer application. Computer code and output are interwoven in the book to illustrate exactly how each analysis is carried out and how output is interpreted. Additional topical coverage includes: Data exploration of one variable and multivariate data Comparing two groups and many groups Permutation tests, randomization tests, and the independent samples t-Test Bootstrap tests and bootstrap intervals Interval estimates and effect sizes Throughout the book, the authors incorporate data from real-world research studies as well aschapter problems that provide a platform to perform data analyses. A related Web site features a complete collection of the book's datasets along with the accompanying codebooks and the R script files and commands, allowing readers to reproduce the presented output and plots. Comparing Groups: Randomization and Bootstrap Methods Using R is an excellent book for upper-undergraduate and graduate level courses on statistical methods, particularlyin the educational and behavioral sciences. The book also serves as a valuable resource for researchers who need a practical guide to modern data analytic and computational methods.
Bootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.