5 edition of **Statistical models in S** found in the catalog.

- 353 Want to read
- 30 Currently reading

Published
**1993**
by Champan & Hall in New York
.

Written in English

- Mathematical statistics -- Data processing.,
- Linear models (Statistics),
- S (Computer program language)

**Edition Notes**

Includes bibliographical references (p. 589-593) and index.

Statement | edited by John M. Chambers, Trevor J. Hastie. |

Series | Chapman & Hall computer science series |

Contributions | Chambers, John M., 1941-, Hastie, Trevor. |

The Physical Object | |
---|---|

Pagination | xv, 608 p. : |

Number of Pages | 608 |

ID Numbers | |

Open Library | OL18226289M |

ISBN 10 | 0412052911, 0412053012 |

Statistics books with examples using S-Plus Smoothing Spline ANOVA Models by Chong Gu; Regression Modeling Strategies with Applications to Linear Models, Logistic Regression and Survival Analysis by Frank E. Harrell, Jr. Mixed Effects Models in S and S . Foundations of Linear and Generalized Linear Models, published by Wiley in February , presents an overview of the most commonly used statistical models by discussing the theory underlying the models and showing examples using R software. The book begins with the fundamentals of linear models.

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population).A statistical model represents, often in considerably idealized form, the data-generating process. A statistical model . Choosing a statistical test can be a daunting task for those starting out in the analysis of experiments. This chapter provides a table of tests and models covered in this book, as well as some general .

A Handbook of Statistical Analyses This book is intended as a guide to data analysis with the R system for sta-tistical computing. R is an environment incorporating an implementation of the S programming language, which is powerful, ﬂexible and has excellent ample linear models File Size: 2MB. Models are central to (almost) all statistical work. This short course aims to give an overview of some of the most prominent statistical models, and associated methods.

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Statistical Models in S by J. Chambers (Editor), T.J. Hastie (Editor) out of 5 stars 4 ratings/5(4). Statistical Models in S - Kindle edition by Hastie, T.J.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Models in S/5(4).

Statistical Models in S - CRC Press Book Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models.

Statistical Models in S. This book contains a collection of ten articles by noted statistical researchers on implementing recent ideas in statistical computing using S.

The software, S, can be purchased from AT&T Bell Laboratories in North Carolina or Statistical Science. Statistical Models in Epidemiology Reprint Edition by David Clayton (Author), Michael Hills (Contributor) out of 5 stars 4 ratings. ISBN ISBN Why is ISBN important. ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book Cited by: The NLME library for analyzing mixed-effects models in S and S-PLUS, developed by the authors, provides the underlying software for implementing the methods presented.

This balanced mix of real data examples, modeling software, and theory makes the book a useful reference for practitioners who use, or intend to use, mixed-effects models /5(9). Statistical Models in S.

Abstract. The interactive data analysis and graphics language S (Becker, Chambers and Wilks, ) has become a popular environment for both data analysts and research Cited by: "Statistical models: theory and practice is lucid, helpful, insightful and a joy to read.

It focuses on the most common tools of applied statistics with a clear and simple presentation. This revised edition Cited by: This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of Author: Kelvyn Jones.

SAR models CAR models Spatial filtering models 17 Time series analysis and temporal autoregression Moving averages Trend Analysis ARMA and ARIMA (Box-Jenkins) models Spectral analysis 18 Resources Distribution tables Bibliography Statistical File Size: 1MB.

A statistical model is a probability distribution constructed to enable infer-ences to be drawn or decisions made from data. This idea is the basis of most tools in the statistical workshop, in which it plays a File Size: KB. A statistical model, ﬁnally, is a stochastic model that contains parameters, which are unknown constants that need to be estimated based on assumptions about the model and the observed data.

There are many reasons why statistical models are preferred over deterministic Size: 1MB. Linear models in statistics/Alvin C. Rencher, G. Bruce Schaalje. – 2nd ed. Includes bibliographical references. ISBN (cloth) 1.

Linear models (Statistics) I. Schaalje. Ji Zhu's R code for fitting IVM models. Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Michael Eisen, Gavin Sherlock, Pat Brown, and David Botstein Exploratory.

STATISTICAL METHODS 1 STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA, La Jolla, USA. Email: [email protected] Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics.

book is published, there will almost certainly be later versions of SPSS available, but we are conﬁdent that the SPSS instructions given in each of the chapters will remain appropriate for the analyses described.

While writing this book we have used the SPSS Base, Advanced Models, Regression Models. The add on here is that it comes with MATLAB code for all the figures in the book, which is definitely helpful and facilitates better understanding and learning how to apply models to your own data.

The Elements of Statistical Learning (T. Hastie, R. Tibshirani and J. Friedman) and Pattern Recognition. A Handbook of Statistics. Introduction to Vectors. Decision-Making using Financial Ratios. Statistics for Business and Economics.

Understanding Statistics. An Introduction to Matlab. A Refresher Course in Mathematics. Introduction to statistical. In many introductory level courses today, teachers are challenged with the task of fitting in all of the core concepts of the course in a limited period of time.

The Introductory Statistics teacher is no stranger to 4/5(8). Mathematical models can project how infectious diseases progress to show the likely outcome of an epidemic and help inform public health interventions.

Models use basic assumptions or collected statistics. "This book is an up to date description of linear mixed models, LMM, and generalized linear mixed models, GLMM. The material is complete enough to cover a course in a Ph.D. program in statistics.

The contribution of this book Brand: Springer-Verlag New York.'The book Predictive Statistics by Bertrand S. and Jennifer L. Clarke provides for an interesting and thought-provoking read.

The underlying idea is that much of current statistical thinking is focused on model Cited by: 2.Introduction. The two instances of modern in the title of this book reflect the two major recent revolutions in biological data analyses. Biology, formerly a science with sparse, often only qualitative data has .