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Excel 2016 for Biological and Life Sciences Statistics, Quirk


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Цена: 7965.00р.
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Автор: Quirk
Название:  Excel 2016 for Biological and Life Sciences Statistics
ISBN: 9783319394886
Издательство: Springer
Классификация:



ISBN-10: 3319394886
Обложка/Формат: Paperback
Страницы: 248
Вес: 0.46 кг.
Дата издания: 2016
Серия: Excel for Statistics
Язык: English
Иллюстрации: 2 black & white illustrations, 162 colour illustrations, biography
Размер: 161 x 239 x 15
Читательская аудитория: Graduate/advanced undergraduate textbook
Основная тема: Statistics / Life Sciences, Medicine, Health Sciences
Подзаголовок: A Guide to Solving Practical Problems
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.Excel is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel 2016 to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand biological and life science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.
Дополнительное описание: Introduction.- Sample size, mean, standard deviation, standard error of the mean.- Random number generator.- Confidence interval about the mean using the TINV function and hypothesis testing.- One-group t-test for the mean.- Two-group t-test of the differ



Statistics: Principles and Methods, 7th Edition

Автор: Richard A. Johnson, Gouri K. Bhattacharyya
Название: Statistics: Principles and Methods, 7th Edition
ISBN: 0470904119 ISBN-13(EAN): 9780470904114
Издательство: Wiley
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Цена: 35369.00 р.
Наличие на складе: Поставка под заказ.

Описание: Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include real-world data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods.

Dynamic Prediction in Clinical Survival Analysis

Автор: Van Houwelingen
Название: Dynamic Prediction in Clinical Survival Analysis
ISBN: 1439835330 ISBN-13(EAN): 9781439835333
Издательство: Taylor&Francis
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Цена: 24499.00 р.
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Описание:

There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models.

Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:

  • Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model
  • Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated
  • Part III is dedicated to the use of time-dependent information in dynamic prediction
  • Part IV explores dynamic prediction models for survival data using genomic data

Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition

Автор: Nicholas J. Horton , Ken Kleinman
Название: Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition
ISBN: 1482237369 ISBN-13(EAN): 9781482237368
Издательство: Taylor&Francis
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Цена: 11789.00 р.
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Описание:

Improve Your Analytical Skills

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

  • The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
  • New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
  • New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
  • New chapter on simulation that includes examples of data generated from complex models and distributions
  • A detailed discussion of the philosophy and use of the knitr and markdown packages for R
  • New packages that extend the functionality of R and facilitate sophisticated analyses
  • Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Hidden Markov Models for Time Series

Автор: Zucchini
Название: Hidden Markov Models for Time Series
ISBN: 1482253836 ISBN-13(EAN): 9781482253832
Издательство: Taylor&Francis
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Цена: 14086.00 р.
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Описание: Hidden Markov Models (HMMs) remains a vibrant area of research in statistics, with many new applications appearing since publication of the first edition.


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