<p><b>Enables readers to understand the methods and applications of experimental design to successfully improve reliability for life testing</b> <p>This book illustrates how experimental design and tests can improve the reliability of the outcome. Focusing on reliability applications and methods, it describes and illustrates methods for designing experiments and analyzes the results when the response is a lifetime. It addresses both optimal and robust design with censored data. <p>To aid in reader comprehension, examples and case studies are included throughout the text to illustrate the key factors in designing experiments and emphasize how experiments involving life testing are inherently different. The book also provides numerous state-of-the-art exercises and solutions to help readers better understand the real-world applications of experimental design and reliability. The authors utilize R, SAS®, and JMP® software throughout as appropriate and a supplemental website contains the related data sets. <p>Written by internationally known experts in the fields of experimental design methodology and reliability data analysis, sample topics covered in the book include: <ul> <li>An introduction to reliability, lifetime distributions, and inference for parameter of lifetime distributions</li> <li>Design of experiments, optimal design, and robust design</li> <li>Lifetime regression, parametric regression models, the Cox Proportional Hazard Model, and design strategies for reliability achievement</li> <li>Accelerated testing, models for acceleration, and design of experiments for accelerated testing</li></ul><p>Featuring an accessible approach to reliability for readers with various levels of technical expertise while avoiding the mathematical rigor found in similar literature in this area, this book is a key reference for statistical researchers, reliability engineers, quality engineers, and professionals in applied statistics and engineering and a comprehensive textbook for upper-undergraduate and graduate-level courses in statistics and engineering.