Circumpolar Health Atlas (Heritage)
When many of us picture the areas surrounding the North Pole, we imagine barren landscapes, wintry conditions, and sparse human and animal populations. Opening up the Circumpolar Health Atlas will undoubtedly change this perception. Abounding with hundreds of vibrant, full-colour photographs and maps, this book presents a stunning and immersive portrait of life in the Arctic region, with an emphasis on the factors that contribute to human health in this area. Written with the general reader in m...
Grundlagen Der Datenanalyse Mit R (Statistik Und Ihre Anwendungen)
by Daniel Wollschl Ger
Das Buch liefert eine Einfuhrung in die Datenauswertung mit der frei erhaltlichen Statistiksoftware R. Die wichtigsten statistischen Verfahren in den Human- und Sozialwissenschaften werden ausfuhrlich an Beispielen erlautert und mit Diagrammen illustriert. Neben den klassischen univariaten Verfahren werden auch zahlreiche nonparametrische und multivariate Methoden beschrieben. Fur die 2.Auflage wurden die Neuerungen der Version R 2.14 eingearbeitet und die Abschnitte zu Regression, Resampling-Ve...
* Includes a new chapter on logistic regression. * Discusses the design and analysis of random trials. * Explores the latest applications of sample size tables. * Contains a new section on binomial distribution.
Bayesian Analysis of Spatially Structured Population Dynamics (Ecological Studies, #253)
by Qing Zhao
The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detection/non-detection data, count data, combined count and capture-recapture data, and spatial capture-recapture data, respectively. The book provides R code of Metropolis-Hastings algorithms that allow efficient computing of these complex models. The book is aimed...
Advances in Artificial Life (Lecture Notes in Artificial Intelligence, #929)
This volume contains 71 revised refereed papers, including seven invited surveys, presented during the Third European Conference on Artificial Life, ECAL '95, held in Granada, Spain in June 1995. Originally AL was concerned with applying biologically inspired solutions to technology and with examining computational expertise in order to reproduce and understand life processes. Despite its short history, AL now is becoming a mature scientific field. The volume reports the state of the art in this...
Methods of Microarray Data Analysis V (Lecture Notes in Physics, #339)
As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA (Critical Assessment of Microarray Data Analysis) conference was the first to establish a forum for a cross section of researchers to look at a common data set and apply innovative analytical techniques to microarray data. Methods of Microarray Analysis V includes selected papers from CAMDA'04, and focuses on data sets relating to a significant global health issue,...
Nonparametric Bayesian Inference in Biostatistics (Frontiers in Probability and the Statistical Sciences)
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the...
D.D. Kosambi
This book fills an important gap in studies on D. D. Kosambi. For the first time, the mathematical work of Kosambi is described, collected and presented in a manner that is accessible to non-mathematicians as well. A number of his papers that are difficult to obtain in these areas are made available here. In addition, there are essays by Kosambi that have not been published earlier as well as some of his lesser known works. Each of the twenty four papers is prefaced by a commentary on the signif...
Analysis of Multivariate Survival Data (Statistics for Biology and Health)
by Philip Hougaard
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of vie...
Permutation, Parametric, and Bootstrap Tests of Hypotheses
by G Matheron and Phillip I. Good
This introduction to biostatistics offers health science studentsQwith limited math and statistics backgroundsQa conceptually-based introduction to statistical procedures that will prepare them to conduct or evaluate research in biological and health sciences. Enthusiasm for the material will quickly spread to the reader from the author. The author's appealing writing style makes users of the text Rforget it is math.S Students are encouraged to use common sense rather than rigorous theory to gai...
This volume comprises a selection of original contributions presented at a workshop held in Montpellier, France, in June 1997. The two main objectives of the workshop were, firstly, to bring together what is understood about the processes underlying agroforestry practice, and, secondly, to provide a forum to explore relevant models and modelling approaches. The workshop was also able to play a role in examining the agroforestry systems encountered in temperate and Mediterranean areas, inc...
Bayesian Approaches in Oncology Using R and OpenBUGS
by Atanu Bhattacharjee
Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the sta...
Likelihood, Bayesian and Mcmc Methods in Quantitative Genetics (Statistics for Biology and Health)
by Daniel Sorensen and Daniel Gianola
This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters thro...
Datenanalyse Mit SAS
by Walter Kramer, Olaf Schoffer, and Lars Tschiersch
Das Programmpaket SAS hat sich im Lauf der Jahre als Standardprogramm zur statistischen Datenanalyse etabliert. Der souverAne Umgang mit statistischen Methoden und deren praktischer Umsetzung in SAS bietet somit einen unschAtzbaren Vorteil fA1/4r die tAgliche Arbeit des Datenanalytikers. Im vorliegenden Buch erlernt der Leser zunAchst die Grundlagen fA1/4r die Programmierung. AnschlieAend wird eine groAe Auswahl statistischer Verfahren und deren Umsetzung als SAS-Programm vorgestellt. Dabei wird...
Composite Sampling (Environmental and Ecological Statistics, #4)
by Ganapati P Patil, Sharad D. Gore, and Charles Taillie
Sampling consists of selection, acquisition, and quantification of a part of the population. While selection and acquisition apply to physical sampling units of the population, quantification pertains only to the variable of interest, which is a particular characteristic of the sampling units. A sampling procedure is expected to provide a sample that is representative with respect to some specified criteria. Composite sampling, under idealized conditions, incurs no loss of information for estima...
The Practice of Statistics in the Life Sciences
by Brigitte Baldi and David S. Moore
Moore and Baldi provide an accessible introduction to the uses and applications of statistics in the life sciences with a data analysis approach. The Practice of Statistics in the Life Sciences emphasises balanced content, working with real data, and mastering statistical ideas, and provides engaging life sciences examples and exercises. Data sets, examples and exercises are drawn from diverse areas of biology such as physiology, brain and behaviour, health and medicine, nutrition, ecology, and...
Statistik Ohne Albtraume (Verdammt clever!)
by Michael Knorrenschild and Helmut Van Emden
STATISTICA ist ein integriertes statistisches und grafisches Datenanalysesystem mit Datenbankfunktion, das eine groe Palette grundlegender und hoherer analytischer Verfahren fur die Anwendung in Wissenschaft, Industrie und Wirtschaft enthalt. Die Integration von Statistik und Grafik ist eine der groen Starken von STATISTICA: Aus allen statistischen Prozeduren konnen zahllose Grafiken erstellt werden, die in jeder Komponente veranderbar sind. Die Studentenversin von STATISTICA beinhaltet einen Su...
Estimation and Inferential Statistics
by Pradip Kumar Sahu, Santi Ranjan Pal, and Ajit Kumar Das
This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily aimed at undergraduate and postgraduate students of statistics, the book is also useful to professionals and researchers in statistical, medical, social and other disciplines. It discusses current methodological techniques used in statistics and related interdisciplinary areas. Every concept is supp...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn't difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures. How exactly should data be used in modelling? The literature offers a bewildering variety o...