(The fee for each course includes coffee breaks and lunch. Registration is limited.)
Modern manufacturing processes (e.g. for semiconductors and vaccines) are highly complex and intensively monitored. Relationships between product quality and the many process variables can be difficult to quantify.
The course will consist of two sections: first, we will draw on our experience using Six Sigma methodology in multiple investigations to share recommendations for structuring team explorations of process data. The second part of the course will shift to the challenges of extracting information from large amounts of data. Particular attention has been devoted recently to finding meaningful relationships in data sets with many more variables than cases. While many of these methods were developed for gene expression and other "omics" data, they are equally applicable to manufacturing data.
In this course, we will describe powerful data mining methods including partial least squares regression (PLS) and random forests. We will explain the ideas behind the methods, and demonstrate how to perform them in readily available software. These methods will be illustrated using examples in which applying these techniques has yielded important insights into manufacturing processes. Class format is 1 day (8 hours) lecture with examples and software demonstrations. Software used will be R and JMP.
This course provides an introduction to the basic principles of acceptance sampling including: concepts and terminology, how to specify a plan and derive it mathematically or graphically, and use of published plans for both attributes and variables sampling. Familiarity with the normal, binomial, Poisson and hypergeometric distributions is assumed. Participants are encouraged to bring their own laptop.
This training covers Binary, Nominal and Ordinal Logistic Regression. Students will learn to identify the context in which Logistic Regression applies. Students will fit Logistic Regression models using Minitab. Students will learn to evaluate goodness of fit, and interpret the models. Data formatting requirements will be explained. The role of link functions will be shown. There will be coverage of input variables that are continuous and categorical. Students will practice effective methods of reporting the findings from their modeling effort. In order to practice model fitting, each student should bring a laptop computer. A 30 day trial version of Minitab Version 15 will be made available to those students that do not already have Minitab installed. In order to load the trial version, students will need to have administrative privilege for their computers.
This course will present and discuss the analyses of many different life data analysis applications in the area of product reliability and materials evaluation. The analyses illustrate the use of a mix of proven traditional techniques, enhanced and brought up to date with modern computer-based methodology. Methods used in the analyses include nonparametric estimation, probability plotting, maximum likelihood estimation of parametric models, analysis of data with multiple failure modes, acceleration models, Bayesian methods, degradation analysis, and the analysis of recurrence data from repairable systems. Using a series of real examples from reliability applications, this course will focus on graphical presentation of reliability data, statistical modeling, and interpretation of results. The prerequisite is a course in applied statistics covering material through simple linear regression.