Introduction to Weibull Regression by Dr. Rong Pan
$300 per person, Group discounts available! 3 hours 4/24/2019
Weibull distribution is widely used in failure time data analysis. In this three hour webinar, which costs $300, we will thoroughly discuss the Weibull regression model and its many applications in reliability testing modeling and data analysis problems. First, the properties of Weibull distribution, particularly the properties that are related to reliability, will be illustrated. Second, we will demonstrate how to fit the failure data, including censored data, to a Weibull model and how to assess the goodness of fit. Next, the Weibull regression method, which is one of the most popular data analysis methods for reliability data with covariates such as the data from accelerated life tests, will be introduced. We will discuss how to evaluate the effect of a covariate and how to conduct residual analysis. An iterative model building process will be presented in an exercise. Finally, we will compare the Weibull regression model with the Cox’s proportional hazard model, another popular semi-parametric regression model.
In this webinar, we will focus on the statistical techniques for building failure time regression models and for predicting product reliability. In addition, based on the properties of reliability prediction, we can properly compare reliabilities of products from two and more manufacturers/suppliers and construct statistically efficient tests to demonstrate reliability. As nowadays most data analysis can be performed by computers, we will show the Weibull analysis on multiple examples using Minitab® and JMP®, as well as some Excel® templates that are customized for specific reliability data analysis tasks.
Instructor: Dr. Rong Pan is an Associate Professor of Industrial Engineering in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. His research interests include failure time data analysis, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. He has over 15 years’ experience in teaching and research in the areas of statistical methods for reliability modeling and data analysis. His research has been supported by NSF, Arizona Science Foundation, Air Force Research Lab, etc. He has published over 60 journal papers and 40+ refereed conference papers. He was the recipient of the Stan Ofsthum Award, presented by the Society of Reliability Engineers, in 2008 and 2011, and the William A. Golomski Award, presented by the IIE Quality Control & Reliability Engineering Division, in 2015. His papers won the Best Reliability Paper Award of Quality Engineering in 2012 and 2013. Dr. Pan is a senior member of ASQ, IIE and IEEE, a lifetime member of SRE, and a member of INFORMS. He serves on the editorial boards of Journal of Quality Technology and Quality Engineering.