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Welcome to EricSayre.com. Eric C. Sayre, PhD is a statistician, researcher, author and programmer currently living in Vancouver, BC. He began working professionally in the field of statistics in 1997, and has worked on a contractual basis for Arthritis Research Canada since 2000. Beginning in 2002, he went back to complete two graduate degrees while remaining active in the research community. He completed his PhD in the Department of Statistics and Actuarial Science at Simon Fraser University in 2009. Eric's current research focus is Machine Learning. His progress so far: i) Introduction to Data Science in Python by University of Michigan on Coursera, Certificate earned on April 24, 2017; ii) Machine Learning by Stanford University on Coursera, Certificate earned on March 24, 2017. Eric is currently enrolled in the 5-course Applied Data Science with Python Specialization taught by University of Michigan professors and delivered through Coursera.

Since 1997, Eric has also been a statistical consultant/collaborator for a variety of clinical and epidemiological research groups, in both health research as well as various private industry projects. I am experienced in a variety of programming languages, databases, statistical packages, and other software, including SAS (expert), R, Parscale, C++, Python, Octave, Microsoft SQL, VB Script, Active Server Pages (ASP), HTML, Microsoft Office (Word, Excel, PowerPoint, Access and Outlook), EndNote, to name but a few. Need help with your project? Happy to help! Click the tab on the left for more info.

Eric is a well-published researcher, with more than 200 publications since 1997. These are a mixture of first-authorships and coauthorships on articles published in peer-reviewed medical journals, abstracts presented at scientific meetings, research reports, invited talks and his own two graduate theses. Click the tab on the left for a complete list of publications and links to the full text of his theses.

In Eric's PhD research, he developed a new method of unsupervised learning (hypothesis generation) designed specifically for mixed-type data (continuous, ordinal, nominal, binary symmetric and binary asymmetric), along with data mining software to perform the analyses. Variable-Weighted Ultrametric Optimization for Mixed-Type Data (VWUO-MD) is useful in identifying new, complex relationships between variables of many different kinds, for example between a multitude of health conditions, socio-economic and geographic factors, and health services utilization patterns. VWUO-MD is a valuable tool for exploiting the increasing multitude of highly multivariate, mixed-type databases available to researchers and industry, in developing new, previously unthought-of hypotheses. Click the tab on the left for more information, an abstract, links to the complete PhD thesis and user's guide, and secure links to a free trial or to purchase the software.

Eric's side interests include (among others) writing, diet and exercise. He also has a great love of "junk food". Over several years, Eric applied his skills and experience in research and writing to develop a diet and exercise plan designed for those who love junk food, but want to lose weight and maintain a healthy body. This requires a diet that respects our love of junk food and occasional reluctance to exercise, but also respects the science of healthy nutrition, in a minimally demanding schedule designed for weight loss without giving up our love of pigging out. To this end, he has written a compact, scientifically motivated but fun and comedic how-to manual on doing just that, The Real Junk Food Diet Book. This diet should not be confused with similarly named diet books, there are very important differences. Click the tab on the left for more information and secure links to a free preview or to purchase the book as an eBook, or to borrow it for free like a library book!


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