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Weight-length relations of fishes are useful for estimation of biomass from length observations, e.g., in fisheries or conservation research. Here we provide some guidance to authors of such papers, in order to facilitate the publication and review process

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of \"data breaks\") Exercises based on data analyses reinforce concepts

Julio Ramos, Victor del Aguila y Ana Bazalar son profesores de la Universidad de Lima con vasta experiencia en la ensenanza universitaria. Trabajan en el area de ciencias del Programa de Estudios Generales, con estudiantes de las carreras de negocios. El trabajo propuesto puede ser abordado tanto por docentes, desde la enseanza, como por estudiantes, desde sus aprendizajes, pues privilegia el desarrollo de la organizacin de datos utilizando tablas y grficos estadsticos, la resolucin de problemas por medio de las principales medidas estadsticas y el uso de herramientas grficas para el anlisis exploratorio de datos. En este libro, que fue diseado para el curso Estadstica Bsica para los Negocios, el primero de los cuatro cursos de estadstica que recibe el estudiante en su formacin universitaria, se propone como texto de consulta y, a la vez, como un cuaderno de trabajo para que le permita desarrollar sus habilidades mediante el ejercicio prctico.

The taxonomy of Bambusoideae is in a state of flux and phylogenetic studies are required to help resolve systematic issues. Over 60 taxa, representing all subtribes of Bambuseae and related non-bambusoid grasses were sampled. A combined analysis of five plastid DNA regions, trnL intron, trnL-F intergenic spacer, atpB-rbcL intergenic spacer, rps16 intron, and matK, was used to study the phylogenetic relationships among the bamboos in general and the woody bamboos in particular. Within the BEP clade (Bambusoideae s.s., Ehrhartoideae, Pooideae), Pooideae were resolved as sister to Bambusoideae s.s. Tribe Bambuseae, the woody bamboos, as currently recognized were not monophyletic because Olyreae, the herbaceous bamboos, were sister to tropical Bambuseae. Temperate Bambuseae were sister to the group consisting of tropical Bambuseae and Olyreae. Thus, the temperate Bambuseae would be better treated as their own tribe Arundinarieae than as a subgroup of Bambuseae. Within the tropical Bambuseae, neotropical Bambuseae were sister to the palaeotropical and Austral Bambuseae. In addition, Melocanninae were found to be sister to the remaining palaeotropical and Austral Bambuseae. We discuss phylogenetic and morphological patterns of diversification and interpret them in a biogeographic context.

This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines. Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book's exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice. The book's pedagogical program, built on cognitive science principles, reinforces learning: Boxes provide \"evidence-based\" advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical app

Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration of consumer data, including segmentation and identifying drivers of liking. Applied in approach and written for non-statisticians, the text is aimed at sensory scientists who deal mostly with descriptive analysis and consumer studies. Multivariate and Probabilistic Analyses of Sensory Science Problems offers simple, easy-to-understand explanations of difficult statistical concepts and provides an extensive list of case studies with step-by-step instructions for performing analyses and interpreting the results. Coverage includes a refresher on basic multivariate statistical concepts; use of common data sets throughout the text; summary tables presenting the pros and cons of specific methods and the conclusions that may be drawn from using various methods; and sample program codes to perform the analyses and sample outputs. As the latest member of the IFT Press series, Multivariate and Probabilistic Analyses of Sensory Science Problems will be welcomed by sensory scientists in the food industry and other industries using similar testing methodologies, as well as by faculty teaching advanced sensory courses, and professionals conducting and participating in workshops addressing multivariate analysis of sensory and consumer data.

Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time

Explore the practices and cutting-edge research on the new and exciting topic of paradata Paradata are measurements related to the process of collecting survey data. Improving Surveys with Paradata: Analytic Uses of Process Information is the most accessible and comprehensive contribution to this up-and-coming area in survey methodology. Featuring contributions from leading experts in the field, Improving Surveys with Paradata: Analytic Uses of Process Information introduces and reviews issues involved in the collection and analysis of paradata. The book presents readers with an overview of the indispensable techniques and new, innovative research on improving survey quality and total survey error. Along with several case studies, topics include: * Using paradata to monitor fieldwork activity in face-to-face, telephone, and web surveys * Guiding intervention decisions during data collection * Analysis of measurement, nonresponse, and coverage error via paradata Providing a practical, encompassing guide to the subject of paradata, the book is aimed at both producers and users of survey data. Improving Surveys with Paradata: Analytic Uses of Process The book also serves as an excellent resource for courses on data collection, survey methodology, and nonresponse and measurement error.

This 1998 book describes the sampling and statistical methods used most often by behavioral ecologists and field biologists. Written by a biologist and two statisticians, it provides a rigorous discussion together with worked examples of statistical concepts and methods that are generally not covered in introductory courses, and which are consequently poorly understood and applied by field biologists. The first section reviews important issues such as defining the statistical population and the sampling plan when using non-random methods for sample selection, bias, interpretation of statistical tests, confidence intervals and multiple comparisons. After a detailed discussion of sampling methods and multiple regression, subsequent chapters discuss specialized problems such as pseudoreplication, and their solutions. It will quickly become the statistical handbook for all field biologists.