Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Statistical science --- biostatistiek --- statistiek

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Newly revised to specifically address Microsoft Excel 2019, 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. 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. Excel 2019 for Biological and Life Sciences Statistics capitalizes on these improvements by teaching students and professionals how to apply Excel 2019 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. This new edition offers a wealth of new practice problems and solutions, as well as updated chapter content throughout.

Statistical science --- biostatistiek --- statistiek

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The specialized field of scan statistics, fathered by Joseph Naus around 1999, burgeoned rapidly to prominence in the field of applied probability and statistics. In additional to challenging theoretical probelms, scan statistics has exciting applications in many areas of science and technologyu including archaelogy, astronomy, physics, bioinformatics, and food sciences, just to name a few. In many fields, decision makers give a great deal of weight to clusters of events. Public Health investigators look for common cause factors to explain clusters of, for example, cancer. Molecular biologists look for palindrome clusters in DNA for clues as to the origin of replication viruses. Telecommunication engineers design capacity to accommodate clusters of calls being dialed simultaneously to a switchboard. Quality control experts investigate clusters of defects. The probabilities of different types of clusters  under various conditions are tools of the physical, natural, and social sciences. Scan statistics arise naturally in the scanning of time and space, seeking clusters of events. It is therefore no surprise that scan statistics is a major area of research in probability and statistics in the 21st century. In the last 5 years about 1600 hits appear on Google Scholar referencing the extensive activity in scan statistics and the breadth of the application. (Since 2010, about 482 hits are recorded in Google scholar.) The Handbook of Scan Statistics in two volumes is intended for researchers in probability and statistics and scientists in several areas including biology, engineering, health, medical, and social sciences. It will be of great value to graduate students in statistics and in all areas where scan statistics are used.

Statistical science --- biostatistiek --- statistiek

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biomathematics. Biometry. Biostatistics --- biostatistiek --- Medical statistics --- Biometry

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The approach taken in this book is, to studies monitored over time, what the Central Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem shows that test statistics involving very different types of clinical trial outcomes are asymptotically normal, this book shows that the joint distribution of the test statistics at different analysis times is asymptotically multivariate normal with the correlation structure of Brownian motion (``the B-value") irrespective of the test statistic. The so-called B-value approach to monitoring allows us to use, for different types of trials, the same boundaries and the same simple formula for computing conditional power. Although Brownian motion may sound complicated, the authors make the approach easy by starting with a simple example and building on it, one piece at a time, ultimately showing that Brownian motion works for many different types of clinical trials. The book will be very valuable to statisticians involved in clinical trials. The main body of the chapters is accessible to anyone with knowledge of a standard mathematical statistics text. More mathematically advanced readers will find rigorous developments in appendices at the end of chapters. Reading the book will develop insight into not only monitoring, but power, survival analysis, safety, and other statistical issues germane to clinical trials. Michael Proschan, Gordon Lan, and Janet Wittes are elected Fellows of the American Statistical Association. All have spent formative years in the Biostatistics Research Branch of the National Heart, Lung, and Blood Institute (NHLBI/NIH). While there, they were intimately involved in the design and statistical monitoring of large-scale randomized clinical trials, developing methodology to aid in their monitoring. For example, Lan developed, with DeMets, the now widely-used spending function approach to group sequential designs, whose properties were further investigated by Proschan. The B-value approach used in the book was introduced in a very influential paper by Lan and Wittes. The statistical theory behind conditional power was developed by Lan, along with Simon and Halperin, and was the cornerstone for the conditional error approach to adaptive clinical trials introduced by Proschan and Hunsberger. All three authors have expertise in adaptive methodology for clinical trials. Michael Proschan is a Mathematical Statistician at the National Institutes of Health; Gordon Lan is Senior Director of Biometrics at Johnson & Johnson Pharmaceutical Research & Development, L.L.C.; Janet Wittes is President of Statistics Collaborative, a statistical consulting company she founded in 1990.

Biomathematics. Biometry. Biostatistics --- medische statistiek --- biostatistiek --- biometrie