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To learn more about Amazon Sponsored Products, click here. S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities.
This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods.
The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets.
Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs,.
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Add both to Cart Add both to List. One of these items ships sooner than the other. Buy the selected items together This item: Ships from and sold by Amazon. Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. The Elements of Statistical Learning: R for Data Science: An Introduction to Statistical Learning: Sponsored products related to this item What's this? Artificial Intelligence By Example: Develop machine intelligence from scratch using Build data-driven test frameworks Machine Learning and Deep Learning with Python, scikit-lea Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.
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Statistics and Computing Hardcover: Springer; 4th edition September 2, Language: Related Video Shorts 0 Upload your video.
Peter rated it really liked it Dec 11, Linear Algebra and Linear Models that they are capable of producing a professional Kindle version. There's a problem loading this menu right now. The companion volume on S Programming will provide an in-depth guide for those writing software in the S language. Lastly, the number of packages is probably twenty times what it was in Buy the selected items together This item:
R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O an Probability and Statistics for Economists. Data Mining with Decision Trees: Theory and Applications Series in Machine Percepti Share your thoughts with other customers. Write a customer review. Read reviews that mention applied statistics linear statistical models modern applied mixed effects random and mixed time series also a very good linear statistical statistical models using s-plus great book language analysis code introduction authors data examples topics methods. There was a problem filtering reviews right now.
Please try again later. Kindle Edition Verified Purchase. The one star concerns explicitly the Kindle edition. The rendering of formula and even plain text! They appear in pale grey and cannot be zoomed! Linear Algebra and Linear Models that they are capable of producing a professional Kindle version. I purchased the Kindle edition in addition to the printed one, just to have it around when I need to look things up.
The same issue is valid for the Springer Kindle versions of: Mixed Effects Models and Extensions in Ecology with R I will repeat this statement in reviews for the above mentioned books. When I have found out how to contact costumer service at Springer I will formally demand a refund or an update.
Statistics and Computing Statistics,Computing Venables,W.N.:Statistics w.S-PLUS. Authors: Venables, W.N., Ripley, B.D. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical. graphical capabilities of modern workstations and personal computers. Various The chapters on applying S to statistical problems are largely self-contained.
Do not buy the Kindle edition. It provides excellent value for its price indeed, any price: All the same, I think it would be useful to identify intended audience for this book in my view.
First, the book is not for novices in Statistics. You'll learn how to fit generalized linear models in the language, not how and why to apply such models properly. Many of the methods discussed are state-of-the-art approaches to topics such as linear, nonlinear, and smooth regression models, tree-based methods, multivariate analysis and pattern recognition, survival analysis, time series and spatial statistics.
Throughout, modern techniques such as robust methods, non-parametric smoothing, and bootstrapping are used where appropriate. The material has been extensively rewritten using new examples and the latest computationally intensive methods.
The companion volume on S Programming will provide an in-depth guide for those writing software in the S language. There are extensive on-line complements covering advanced material, user-contributed extensions, further exercises, and new features of S-PLUS as they are introduced.
Professor Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition, and neural networks. NonLinear and Smooth Regression.
Random and Mixed Effects. A3 Using R under Unix Linux. A4 Using R under Windows. A5 For Emacs Users.