I met José Ramírez, PhD, in Chicago at the JMP Discovery conference and Innovators’ Summit. An industrial statistician and longtime JMP and SAS user, he was quite the celebrity at the conference, where he gave a well-attended talk about designing experiments using JMP and SAS.
José told me about his new book, co-authored with his wife Brenda Ramírez, who is also an industrial statistician and expert user of JMP and SAS. The pair wrote the book, Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide, over two years, on weekends and evenings. They also write a blog called Stat Insights that includes excerpts from their book and discusses “statistics as a catalyst for engineering and scientific discoveries.”
Here, José and Brenda share details about the book for readers of the JMP Blog.
Arati: Why did you decide to write this book?
José & Brenda: A few years ago, the JMP team approached us with the idea to write a book for engineers and scientists. This seemed like a natural progression in our careers, since we have been collaborating with engineers and scientists for many years and we have developed and delivered countless hours of training in statistics and continuous improvement. In addition, we are big fans of JMP software and have been using it for a long time.
So writing this book seemed like the perfect opportunity for us to consolidate the significant knowledge we have gained as practicing industrial statisticians, and share it in a way that is far-reaching and useful to this community. An additional inspiration for our book comes from the National Bureau of Standards Handbook 91 Experimental Statistics by Mary Natrella. We wanted to bring the same spirit and utility of the NBS Handbook 91 to the countless engineers, scientists and data analysts whose work requires them to transform data into actionable information.
Arati: Who, specifically, will benefit from reading and using your book? And how do you hope they will use the book?
Brenda: The book is primarily written for engineers and scientists who need to use statistics and JMP to make sense of data and make sound decisions based on their analyses. This includes, for example, people working in semiconductor, automotive, chemical and aerospace industries. Other professionals in these industries who will find it valuable include quality engineers, reliability engineers, Six Sigma Black Belts and statisticians.
In addition to the working professional, those who are studying to become engineers, scientists or even statisticians, as well as those teaching them, should get a copy of our book. It is a great teaching aid.
For those who want a reference for how to solve common problems using statistics and JMP, we walk through different case studies using a seven-step problem-solving framework, with heavy emphasis on the problem setup, interpretation, and translation of the results in the context of the problem.
For those who want to learn more about the statistical techniques and concepts, we provide a practical overview of the underpinnings and provide appropriate references. Finally, for those who want to learn how to benefit from the power of JMP, we have loaded the book with many step-by-step instructions and tips and tricks.
Arati: What kinds of case studies or problems do you discuss in the book?
José: In Chapters 3 through 7, we start with a problem description, setting the stage for the uncertainties that need to be solved using the statistical techniques described in the chapter.
All of the case studies in the book are based upon common problems that engineers or scientist will come across at some point in their careers, and the chapter headings reflect the specific application.
For example, in Chapter 4, “Comparing the Measured Performance of a Material, Process, or Product to a Standard,” we use a semiconductor example involving a new three-zone vertical furnace for thin film deposition of waters to illustrate the usefulness of one-sample significance tests to qualify a new piece of equipment.
In Chapter 5, “Comparing the Measured Performance of Two Materials, Processes, or Products,” we compare the performance of two mass spectrometers in an analytical laboratory using the atomic weight of silver to determine if a bias exists and to understand their measurement error.
Although it is not officially a case study, we are thrilled to include in Chapter 7 the data from Albert Einstein’s first published paper. In his 1901 paper, a young Einstein used least squares to fit a model to investigate the nature of intermolecular forces.
Arati: It’s pretty cool that you had Professor Douglas Montgomery write the foreword to your book. How did you make that happen?
José: Ever since we were students, we have been using and following the work of Professor Montgomery, and we believe his books are excellent references for engineers and scientists. We also share a passion for industrial statistics and Doug and I have crossed paths many times over the years at various statistical conferences and events, including, more recently, at JMP conferences.
When we put all of these pieces together – statistics, engineering and JMP – Professor Montgomery seemed like the perfect person to entrust with this important part of our book. So we just had to find a way to ask him if he would be willing to write the foreword to our book. Luckily for us, that opportunity arose at the Quality & Productivity Research conference in June 2008 in Madison, WI. At that event, I was able to discuss this possibility with him, and without hesitation he said, “Yes.”
Arati: How will you use this book going forward in your professional career?
Brenda: This book is a reflection of how we collaborate with engineers and scientists to use statistics as a catalyst for new discoveries and insights. Having the book will make it easier to share our statistical engineering philosophy with others.
Arati: Where is your book sold?
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