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This title aims to provides the researcher and the student with guidance through the changing debates in cultural studies and related disciplines. In a field where meanings are frequently complex and ambiguous, this resource is for anyone wishing to keep up-to-date with the changing agenda in cultural studies.
1. The book is the complete Preparatory Guide for Banking Awareness 2. Entire syllabus has been split into 11 chapter 3. Each chapter contains theories and solved problems for revision 4. Unsolved problems have been accompanied after every chapter 5. Banking Glossary have provided after every chapter 6. 5 Practice Sets have been given for self-analysis The updated edition of “Banking Awareness” serves as an extremely valuable asset for aspirants who are preparing for banking recruitment exams. Especially designed according to the latest pattern, this book is divided into 18 chapters, giving proper coverage in Economy, Banking and Finance Sector, etc. All the relevant & important chapters & topics on Banking Awareness are covered in the book concisely in a reader-friendly manner. Most of the practice questions given in the book are incorporated from various previous years’ question papers & solved Papers in a Chapterwise manner. 3 Practice sets, abbreviation and glossary are given at the end. This book not only serves as study guide but also as a practice tool to test your banking knowledge. It is highly recommended for the aspirants to sharpen their Problem solving skills with intensive practice of numerous questions provided in the book, and prepare them to face their exam with confidence. TOC Indian Banking and its History, Indian Banking Structure, Reserve Bank of India, Banking Regulation System, Banking Innovation in India, Banking Services in India, Financial Reforms in Banks, Banking Committees and Financial Standards, Financial Institutions in India, Money and Financial Market, Capital Market, Insurance and NBFCs, Tax System in India, Public Finance and Budget, Indian Economy, Industry and Trade, International Organisation and World Bank, Important Government Schemes, Practice Sets (1-3), Abbreviation, Glossary, Financial and Banking Current Affairs.
The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.
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.
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.