Free ENBIS Webinar by Alessandro Di Bucchianico26 May 2015; 12:30 – 13:30; Webinar
Alessandro Di Bucchianico will talk about "Statlab: An Interactive Teaching Tool For DOE". The Webinar will be moderated by Antje Christensen.
A good working knowledge of DOE (Design of Experiments) is essential for both industrial statisticians and engineers. It is therefore paramount that courses in statistics pay sufficient attention to this topic. However, a distinctive feature of DOE is that it is pro-active, unlike many other statistical techniques. Hence, this requires a teaching approach that forces students to actively think about several aspects of setting up an experimental design, without steering them too much. In order to create such a teaching environment to be used in statistics courses at various departments of Eindhoven University of Technology, a web based tool called Statlab has been developed. This tool adapts itself to the student who is being led through one of several possible case studies. In the framework of the webinar we will describe the teaching philosophy behind this tool and demonstrate its implementation in practice.
Students generally consider using Statlab a stimulating teaching environment. Educators see it as a useful addition to existing teaching tools like the well-known helicopter experiment.
The main features of Statlab are the following:
- it hides options unless asked for;
- it is web-based, written in Java, and freely available;
- it is multilingual (presently Dutch and English versions are available);
- it contains several screening and optimisation case studies;
- it includes an automatic grading system by sending an e-mail to the teacher.
Statlab can be freely used through the web site http://www.win.tue.nl/statlab/. The current version of Statlab consists of three types of assignments. In the first type of assignment, the goal is to set up a screening experiment. During this screening experiment the student needs to create a two-level factorial design in order to determine the significant factors in a manufacturing process. The student has to take decisions concerning design size, blocks, high and low factor values, aliasing structure, centre points, replication and randomisation. The second type of assignment usually is a sequel to the previous type: it is the optimisation phase using response surface methods (RSM). In this assignment the student should find the optimal values of the significant effects using the method of steepest ascent. There is also a third assignment type in which students have to perform robust parameter design in the style of Taguchi.