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The Department of Statistics of Bogor Agricultural University (Indonesia) organizes two courses presenting state-of-the-art methodology on Design of Experiments. The courses will be held on 22nd-25th August 2016. They will be delivered by two distinguished experts in this field, i.e. Prof. Peter Goos (University of Leuven, University of Antwerp) and Prof. Eric Schoen (TNO-Netherlands, University of Antwerp).  The first is well known in the area of optimal design of experiments, while the second is an expert on fractional factorial designs and the use of orthogonal arrays as experimental designs.

Description of the courses

There are two advanced courses: optimal designs and orthogonal arrays. These courses cover two important branches of design of experiments.

Optimal design of experiments

Design of experiments or DOE is a key tool for product and process improvement and innovation. However, experimenters often have to deal with a mismatch between standard experimental designs, such as factorial and fractional factorial designs, central composite designs, and the features of their problems. This course motivates the standard and routine use of a fully flexible approach to design of experiments, named optimal design of experiments, by showing its industrial application in ten case studies covering a wide range of practical situations. The increasing computing power and the availability of the user-friendly software JMP for the tailor-made design of experiments has made optimal experimental design a key tool for applied statisticians and researchers in the 21st century. This course will demonstrate the usefulness of optimal design of experiments in a wide variety of contexts, based on the case studies in the book “Optimal Design of Experiments: A Case Study Approach”.

Orthogonal arrays

Orthogonal arrays are rectangular arrangements of symbols, where the rows correspond to the different experimental tests and the columns correspond to different experimental factors. The symbols in each column are the settings of the factor. For a given number of experimental tests, a given number of factors and a given number of factor settings, there may be many different orthogonal arrays that can be used as an experimental designs. Some are downright disastrous to use, while others can be very good. The course has two main themes. The first theme is the definition, discussion and calculation of various criteria to measure the potential of an orthogonal array when used as an experimental design. The second theme is the generation of  all orthogonal arrays for experiments with given numbers of experimental tests, numbers of factors and numbers of factor levels  using a powerful algorithm. Computer exercises worked on during the course help to obtain experience of selecting a suitable orthogonal array to define experimental tests.