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Author: S. Michelson
Publisher: Springer Science & Business Media
ISBN: 0306468530
Category : Science
Languages : en
Pages : 173
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Book Description
We live in a very uncertain world. Variation surrounds our work. There is noise in our experiments, in our measurements, and in our test subjects. From all these sources of uncertainty and variation, we try to extract a coherent picture of very complex and sometimes dynamic, biological and chemical processes. In fact, one of our major challenges is to separate this signal, the 'real' biology or chemistry, from the noise. The tools developed to do this are called, collectively, biostatistics. Any tool, even a hammer, can be misused. This could result, at best, in inefficiency, and, at worst, in disaster. With the advent of newer, us- friendly statistical software packages, desk top computing, and point-a- click technologies, it is easier than ever to make mistakes in your analyses. The beauty of having access to so much computing power is that you can now enjoy ultimate flexibility in data processing: that can also be a problem. Ask your computer to produce a particular analysis, report or graphic, and that is exactly what you will get: if you happen to have asked for the wrong thing it will be produced just as quickly, and you will probably never know it was wrong. One aim of this handbook is to help you choose the correct tool for the job at hand, understand its strengths and weaknesses, and to help you recognize when you should seek expert advice. We describe biostatistics as a collection of tools for very good reasons.
Author: S. Michelson
Publisher: Springer Science & Business Media
ISBN: 0306468530
Category : Science
Languages : en
Pages : 173
Get Book Here
Book Description
We live in a very uncertain world. Variation surrounds our work. There is noise in our experiments, in our measurements, and in our test subjects. From all these sources of uncertainty and variation, we try to extract a coherent picture of very complex and sometimes dynamic, biological and chemical processes. In fact, one of our major challenges is to separate this signal, the 'real' biology or chemistry, from the noise. The tools developed to do this are called, collectively, biostatistics. Any tool, even a hammer, can be misused. This could result, at best, in inefficiency, and, at worst, in disaster. With the advent of newer, us- friendly statistical software packages, desk top computing, and point-a- click technologies, it is easier than ever to make mistakes in your analyses. The beauty of having access to so much computing power is that you can now enjoy ultimate flexibility in data processing: that can also be a problem. Ask your computer to produce a particular analysis, report or graphic, and that is exactly what you will get: if you happen to have asked for the wrong thing it will be produced just as quickly, and you will probably never know it was wrong. One aim of this handbook is to help you choose the correct tool for the job at hand, understand its strengths and weaknesses, and to help you recognize when you should seek expert advice. We describe biostatistics as a collection of tools for very good reasons.
Author: Bernard Rosner
Publisher: Cengage Learning
ISBN: 9781305268920
Category : Mathematics
Languages : en
Pages : 0
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Book Description
Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to implementation using software either from SAS, Stata, R, Excel or Minitab. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: Steve Selvin
Publisher: Cambridge University Press
ISBN: 1139471244
Category : Medical
Languages : en
Pages : 219
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Book Description
This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in survival analysis concepts and techniques for non-statisticians.
Author: Harvey Motulsky
Publisher: Oxford University Press
ISBN: 0199365067
Category : Literary Criticism
Languages : en
Pages : 209
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Book Description
With its engaging and conversational tone, Essential Biostatistics: A Nonmathematical Approach provides a clear introduction to statistics for students in a wide range of fields, and a concise statistics refresher for scientists and professionals who need to interpret statistical results. It explains the ideas behind statistics in nonmathematical terms, offers perspectives on how to interpret published statistical results, and points out common conceptual traps to avoid. It can be used as a stand-alone text or as a supplement to a traditional statistics textbook.
Author: Ying Kuen Cheung
Publisher: CRC Press
ISBN: 1420091514
Category : Mathematics
Languages : en
Pages : 207
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Book Description
As clinicians begin to realize the important role of dose-finding in the drug development process, there is an increasing openness to "novel" methods proposed in the past two decades. In particular, the Continual Reassessment Method (CRM) and its variations have drawn much attention in the medical community, though it has yet to become a commonplace tool. To overcome the status quo in phase I clinical trials, statisticians must be able to design trials using the CRM in a timely and reproducible manner. A self-contained theoretical framework of the CRM for researchers and graduate students who set out to learn and do research in the CRM and dose-finding methods in general, Dose Finding by the Continual Reassessment Method features: Real clinical trial examples that illustrate the methods and techniques throughout the book Detailed calibration techniques that enable biostatisticians to design a CRM in timely manner Limitations of the CRM are outlined to aid in correct use of method This book supplies practical, efficient dose-finding methods based on cutting edge statistical research. More than just a cookbook, it provides full, unified coverage of the CRM in addition to step-by-step guidelines to automation and parameterization of the methods used on a regular basis. A detailed exposition of the calibration of the CRM for applied statisticians working with dose-finding in phase I trials, the book focuses on the R package ‘dfcrm’ for the CRM and its major variants. The author recognizes clinicians’ skepticism of model-based designs, and addresses their concerns that the time, professional, and computational resources necessary for accurate model-based designs can be major bottlenecks to the widespread use of appropriate dose-finding methods in phase I practice. The theoretically- and empirically-based methods in Dose Finding by the Continual Reassessment Method will lessen the statistician’s burden and encourage the continuing development and implementation of model-based dose-finding methods.
Author: Stanton Glantz
Publisher: McGraw-Hill Medical
ISBN: 9780071435093
Category : Medical
Languages : en
Pages : 550
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Book Description
Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author’s unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.
Author: Barbara Illowsky
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 2106
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Book Description
Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Author: Dr Michael John de Smith
Publisher: The Winchelsea Press
ISBN: 1912556081
Category : Education
Languages : en
Pages : 827
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Book Description
A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools.
Author: George Casella
Publisher: Springer Science & Business Media
ISBN: 0387759646
Category : Mathematics
Languages : en
Pages : 325
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Book Description
Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. In this book the basic theoretical underpinnings are covered. It describes the principles that drive good designs and good statistics. Design played a key role in agricultural statistics and set down principles of good practice, principles that still apply today. Statistical design is all about understanding where the variance comes from, and making sure that is where the replication is. Indeed, it is probably correct to say that these principles are even more important today.
Author: Prabhanjan Tattar
Publisher: Packt Publishing Ltd
ISBN: 178712326X
Category : Computers
Languages : en
Pages : 428
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Book Description
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
