Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Автор: Kohler Название: Data Analysis Using Stata, Third Edition ISBN: 1597181102 ISBN-13(EAN): 9781597181105 Издательство: Taylor&Francis Рейтинг: Цена: 11176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.
The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.
Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
Автор: Myers, Jerome L Название: Research design and statistical analysis ISBN: 0805864318 ISBN-13(EAN): 9780805864311 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Поставка под заказ.
Описание: This interdisciplinary group of scholars-anthropologists, archaeologists, architects, educators, lawyers, heritage administrators, policy analysts, and consultants-make the first attempt to define and assess heritage values on a local, national and global level. Chapters range from the theoretical to policy frameworks to case studies of heritage practice, written by scholars from eight countries.
Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors' experience.
Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat(R) statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R.
By the time you reach the end of the book (and online material) you will have gained:
A clear appreciation of the importance of a statistical approach to the design of your experiments,
A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables,
Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly,
An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working.
The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.
Автор: Carmona, Rene Название: Statistical Analysis of Financial Data in R ISBN: 1461487870 ISBN-13(EAN): 9781461487876 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Addressing the most challenging issues faced by financial engineers, this book shows how sophisticated mathematics and modern statistical techniques can be used in concrete financial problems. Includes practical examples solved in the R computing environment.
Автор: Box, George E. P. Tiao, George C. Название: Bayesian inference in statistical analysis ISBN: 0471574287 ISBN-13(EAN): 9780471574286 Издательство: Wiley Рейтинг: Цена: 25494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed to form the basis of a graduate course on Bayesian inference, this textbook discusses important general issues of the Bayesian approach. It investigates problems, illustrating the appropriate analysis of mathematical results with numerical examples.
Описание: Vol.4: Effective Environmental Management for Sustainable Development
Автор: Kahraman Название: Fuzzy Statistical Decision-Making ISBN: 3319390120 ISBN-13(EAN): 9783319390123 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.
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