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9781032019963

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The person reading this book will need to have an elementary understanding of statistics.These chapters focuses on the future of reliability and how this will impact us all in the various industries. Machine learning will also play a big part in the future of industries; this also is mentioned. For anyone studying a reliability field, this book is a must. A+ (Jacque van der Westhuizen, RAM Specialist-Sasol) Engineers and managers working in industries that utilize reliability methods on a regular basis should find this book useful. These chapters explain the what and why for applying reliability methods to real problems. The text is extremely clear, especially for such a technical topic. It is written for non-specialists, and is well-written for this audience...I would recommend publication, because I found this to be unique material, which would be of significant practical usefulness. Further, each of the authors is well-known in the field, adding further credibility to the work. (Roger Hoerl, Union College) The book will be of interest to academics and practitioners alike. For academics the book provides a broader range of applications of statistics in practice and industry. The way the authors discussed the examples are pitched at the right level for post-graduate engineering students to understand the importance of statistical thinking in reliability studies, and also for statistics students to provide an understanding of the practical side of data analysis for reliability related problems. (Roelof Coetzer, Data Science Group, Sasol South Africa) This is a well-written book which provides good insight into the statistical challenges and opportunities for successfully achieving product reliability for individuals who have completed at least the introductory course(s) in statistics. The target audience will include STEM students and academicians, business managers and professionals, engineers and others. Given the authors' goal of attracting a broad audience, they present a wide array of real-world applications related to locomotives, aircraft engines, automobiles, medicine, and household appliance, etc. (Carolyn Morgan, MECK, Limited LLC)

"""The person reading this book will need to have an elementary understanding of statistics.These chapters focuses on the future of reliability and how this will impact us all in the various industries. Machine learning will also play a big part in the future of industries; this also is mentioned. For anyone studying a reliability field, this book is a must. A+""(Jacque van der Westhuizen, RAM Specialist-Sasol) ""Engineers and managers working in industries that utilize reliability methods on a regular basis should find this book useful. These chapters explain the ""what and why"" for applying reliability methods to real problems. The text is extremely clear, especially for such a technical topic. It is written for non-specialists, and is well-written for this audience…I would recommend publication, because I found this to be unique material, which would be of significant practical usefulness. Further, each of the authors is well-known in the field, adding further credibility to the work."" (Roger Hoerl, Union College) ""The book will be of interest to academics and practitioners alike. For academics the book provides a broader range of applications of statistics in practice and industry. The way the authors discussed the examples are pitched at the right level for post-graduate engineering students to understand the importance of statistical thinking in reliability studies, and also for statistics students to provide an understanding of the practical side of data analysis for reliability related problems."" (Roelof Coetzer, Data Science Group, Sasol South Africa) ""This is a well-written book which provides good insight into the statistical challenges and opportunities for successfully achieving product reliability for individuals who have completed at least the introductory course(s) in statistics. The target audience will include STEM students and academicians, business managers and professionals, engineers and others. Given the authors’ goal of attracting a broad audience, they present a wide array of real-world applications related to locomotives, aircraft engines, automobiles, medicine, and household appliance, etc."" (Carolyn Morgan, MECK, Limited LLC) ""I thoroughly enjoyed reading Achieving Product Reliability, and recommend it to engineers, technical and management level, and statisticians, both lecturers and research fellows, who are interested in reliability theory and data analysis."" (Roelof L.J. Coetzer, Technometrics, 63:4, 5629564) ""The person reading this book will need to have an elementary understanding of statistics.These chapters focuses on the future of reliability and how this will impact us all in the various industries. Machine learning will also play a big part in the future of industries; this also is mentioned. For anyone studying a reliability field, this book is a must. A+""(Jacque van der Westhuizen, RAM Specialist-Sasol) ""Engineers and managers working in industries that utilize reliability methods on a regular basis should find this book useful. These chapters explain the ""what and why"" for applying reliability methods to real problems. The text is extremely clear, especially for such a technical topic. It is written for non-specialists, and is well-written for this audience…I would recommend publication, because I found this to be unique material, which would be of significant practical usefulness. Further, each of the authors is well-known in the field, adding further credibility to the work."" (Roger Hoerl, Union College) ""The book will be of interest to academics and practitioners alike. For academics the book provides a broader range of applications of statistics in practice and industry. The way the authors discussed the examples are pitched at the right level for post-graduate engineering students to understand the importance of statistical thinking in reliability studies, and also for statistics students to provide an understanding of the practical side of data analysis for reliability related problems."" (Roelof Coetzer, Data Science Group, Sasol South Africa) ""This is a well-written book which provides good insight into the statistical challenges and opportunities for successfully achieving product reliability for individuals who have completed at least the introductory course(s) in statistics. The target audience will include STEM students and academicians, business managers and professionals, engineers and others. Given the authors’ goal of attracting a broad audience, they present a wide array of real-world applications related to locomotives, aircraft engines, automobiles, medicine, and household appliance, etc."" (Carolyn Morgan, MECK, Limited LLC)"

""The person reading this book will need to have an elementary understanding of statistics.These chapters focuses on the future of reliability and how this will impact us all in the various industries. Machine learning will also play a big part in the future of industries; this also is mentioned. For anyone studying a reliability field, this book is a must. A+""(Jacque van der Westhuizen, RAM Specialist-Sasol) ""Engineers and managers working in industries that utilize reliability methods on a regular basis should find this book useful. These chapters explain the ""what and why"" for applying reliability methods to real problems. The text is extremely clear, especially for such a technical topic. It is written for non-specialists, and is well-written for this audience…I would recommend publication, because I found this to be unique material, which would be of significant practical usefulness. Further, each of the authors is well-known in the field, adding further credibility to the work."" (Roger Hoerl, Union College) ""The book will be of interest to academics and practitioners alike. For academics the book provides a broader range of applications of statistics in practice and industry. The way the authors discussed the examples are pitched at the right level for post-graduate engineering students to understand the importance of statistical thinking in reliability studies, and also for statistics students to provide an understanding of the practical side of data analysis for reliability related problems."" (Roelof Coetzer, Data Science Group, Sasol South Africa) ""This is a well-written book which provides good insight into the statistical challenges and opportunities for successfully achieving product reliability for individuals who have completed at least the introductory course(s) in statistics. The target audience will include STEM students and academicians, business managers and professionals, engineers and others. Given the authors’ goal of attracting a broad audience, they present a wide array of real-world applications related to locomotives, aircraft engines, automobiles, medicine, and household appliance, etc."" (Carolyn Morgan, MECK, Limited LLC) ""I thoroughly enjoyed reading Achieving Product Reliability, and recommend it to engineers, technical and management level, and statisticians, both lecturers and research fellows, who are interested in reliability theory and data analysis."" (Roelof L.J. Coetzer, Technometrics, 63:4, 5629564) ""The person reading this book will need to have an elementary understanding of statistics.These chapters focuses on the future of reliability and how this will impact us all in the various industries. Machine learning will also play a big part in the future of industries; this also is mentioned. For anyone studying a reliability field, this book is a must. A+""(Jacque van der Westhuizen, RAM Specialist-Sasol) ""Engineers and managers working in industries that utilize reliability methods on a regular basis should find this book useful. These chapters explain the ""what and why"" for applying reliability methods to real problems. The text is extremely clear, especially for such a technical topic. It is written for non-specialists, and is well-written for this audience…I would recommend publication, because I found this to be unique material, which would be of significant practical usefulness. Further, each of the authors is well-known in the field, adding further credibility to the work."" (Roger Hoerl, Union College) ""The book will be of interest to academics and practitioners alike. For academics the book provides a broader range of applications of statistics in practice and industry. The way the authors discussed the examples are pitched at the right level for post-graduate engineering students to understand the importance of statistical thinking in reliability studies, and also for statistics students to provide an understanding of the practical side of data analysis for reliability related problems."" (Roelof Coetzer, Data Science Group, Sasol South Africa) ""This is a well-written book which provides good insight into the statistical challenges and opportunities for successfully achieving product reliability for individuals who have completed at least the introductory course(s) in statistics. The target audience will include STEM students and academicians, business managers and professionals, engineers and others. Given the authors’ goal of attracting a broad audience, they present a wide array of real-world applications related to locomotives, aircraft engines, automobiles, medicine, and household appliance, etc."" (Carolyn Morgan, MECK, Limited LLC)

Author Information

Dr. Necip Doganaksoy is an associate professor at the School of Business of Siena College, following a 26-year career in industry, mostly at General Electric (GE). Dr. William Q. Meeker is a professor of statistics and distinguished professor of liberal arts and sciences at Iowa State University and a frequent consultant to industry. Dr. Gerald J. Hahn is a retired manager of statistics at GE Global Research after a 46-year career at GE. All three authors are Fellows of the American Society for Quality and the American Statistical Association, elected members of the International Statistical Institute, authors of three or more books, and recipients of numerous prestigious professional awards.

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