This textbook is an introduction to lifetime data analysis aimed primarily at undergraduate and beginning graduate statistics students needing exposure to major topics of the subject, but not yet specializing in a particular application area such as engineering or the biomedical sciences. Unlike other texts at the same level, the work is oriented equally towards reliability, which involves data analysis from engineering and the physical sciences, and survival analysis, usually related to data analysis in the biomedical sciences. Differing only in emphasis and terminology, the two areas—reliability and survival analysis—have essentially the same statistical subject matter: analysis of nonnegative random variables subject to censoring with possible covariates. Reliability places more emphasis on parametric models, which are covered in Chapter 6, and uses accelerated life much more than proportional hazards regression. Survival analysis employs semi-parametric models extensively, particularly Cox’s proportional hazards model, which is covered in Chapter 7. Key features and topics: * balanced presentation geared toward undergraduate and beginning statistics students not yet specializing in a specific area of application * extensive exercises at all levels of difficulty employing well-known computing packages such as SAS and SYSTAT * real-world applications to the health sciences, manufacturing and industrial processes * thorough explanations of methods and tests moving away from the "cookbook” approach commonly used in other textbooks * coverage of topics not found in textbooks at the same level: current status data, step stress tests, and threshold regression * solutions manual available to instructors adopting the text. An Introduction to Lifetime Data Analysis may be used as a textbook for undergraduate and beginning-level graduate courses in statistics. Prerequisites include distribution theory and maximum likelihood-based statistical inference, as well as familiarity with standard elementary methods of analysis up to regression. The text is also an excellent reference for advanced students, researchers, and practitioners in applied statistics. weiterlesen