creator Chen, Zhefu advisor Kang, Lulu In a multistage manufacturing process, variation propagates in the process when we produce products from stage to stage. Since there are limits of reducing variation through process in traditional industrial management and it is hard to monitor the process when the engineering domain knowledge is insufficient. We investigated statistical methods based on piecewise tree regression model including: CART, Bayesian CART, Bayesian treed linear model and Bayesian treed Gaussian process to multistage manufacturing process. The difference performances between models were discussed in a wafer manufacturing process case as a result comparison. Key Words: Multistage process, variation propagation, process monitoring, piecewise tree regression Submitted by Liana Khananashvili (khananashvili@iit.edu) on 2014-10-22T21:21:28Z No. of bitstreams: 2 thesis Zhefu Chen.pdf: 1186870 bytes, checksum: 474539bf77c4fc69b36812d789958225 (MD5) first page with signature.pdf: 127076 bytes, checksum: 7c97625abf242f3d8f45c36a16a9bc65 (MD5) Made available in DSpace on 2014-10-22T21:21:28Z (GMT). No. of bitstreams: 2 thesis Zhefu Chen.pdf: 1186870 bytes, checksum: 474539bf77c4fc69b36812d789958225 (MD5) first page with signature.pdf: 127076 bytes, checksum: 7c97625abf242f3d8f45c36a16a9bc65 (MD5) Previous issue date: 2014-05 M.S. in Applied Mathematics, May 2014 2014 2014-05 http://hdl.handle.net/10560/3296 en Thesis born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access MATH / Applied Mathematics Illinois Institute of Technology Affiliated department