Søren Bisgaard

Teaching Statistics to Engineers

Abstract Engineers perform experiments and analyze data as an integral part of their job regardless of whether they have learned statistics. But those that have are likely to be more effective engineers. The fact that many engineers have only recently “discovered” statistics suggests that we need to reconsider our approach to teaching this important science. I report on our experience teaching engineers using an approach that integrates statistics into engineering practice. Examples of course structure and curricula for both university and professional industrial courses are discussed. Special teaching methods are presented that help engineers understand that statistics can help them solve their problems.

Standard errors for the eigenvalues in second-order response surface models

In the interpretation of the geometry of second-order response surface models, standard errors and confidence intervals for the eigenvalues of the second-order coefficient matrix play an important role. In this article, we propose a new method for estimating the standard errors, and hence approximate confidence intervals, of these eigenvalues. The method is simple in both concept and execution. It involves the refitting of a full quadratic model after rotating the coordinate system to coincide with the canonical axes. The estimated standard errors of the pure quadratic terms from this refitting are then used as approximate standard errors of the eigenvalues. Because this approach is based on the canonical form, it is geometrically intuitive and easily taught. Our method is intended as a way for practitioners to get quick estimates of the standard errors of the eigenvalues. In our justification of the approach, we show that it is equivalent to using the delta method proposed by Carter, Chinchilli, and Camp...

Blocking Generators for Small 2k-p Designs

Small 2k-p designs are increasingly being used in industry for process and product experimentation. Blocking of these designs can often significantly increase their efficiency. To facilitate the practical use of blocking, a comprehensive table providing..

INDUSTRIAL USE OF STATISTICALLY DESIGNED EXPERIMENTS: CASE STUDY REFERENCES AND SOME HISTORICAL ANECDOTES

Case studies are important because they provide illustrations of the industrial use of design of experiments. However, they usually are hard to come by. Unknown to many, there are, however, quite a few case studies already published in the literature. M..

Designing experiments for tolerancing assembled products

This article provides an outline of theory and methods for the experimental determination of tolerance limits for mating components of assembled products. The emphasis is on novel combinatorial problems of pre- and post-fracfionafion of certain products of two-level factorial designs. The cost of experimentation is discussed and used as a guide to allocating experimental runs. Several design examples are provided. The article also includes a comprehensive example of the experimental determination of tolerances for the components of a throttle handle for small outboard motors.

The use of experimental design in the development of new products

Experimental design methods are tools for conducting informative, time‐ and cost‐effective experiments. Used during product development, these methods can contribute to building quality into products as well as shortening the development cycle time. These techniques make it possible to study the effect of many factors (parameters) simultaneously, to select the factor combination that results in both improved quality and reduced cost, and hence allow for the development of reliable and robust products of high quality. In addition, these methods provide a systematic approach for problem solving during the product development process. This article provides a non‐technical discussion of the role of experimentation and the advantage of using experimental design during product development. Different experimental design methods and examples of their application during product development will also be presented.

The role of scientific method in quality management

Scientific method plays a vital role in quality improvement. In this article, we provide numerous examples of the use of scientific inquiry to improve quality, and explain scientific methods catalytic role for the success of modern quality management. Perspectives for the future are also provided.

Quality Improvement in the Construction Industry: Three Systematic Approaches

A major difference between construction and manufacturing is that most constructed facilities are unique, while manufactured goods usually are massproduced. Therefore, to attain learning effects comparable to those achieved in mass-production, learning from failures in construction needs to take place at an industry-wide level. Further, methods of Quality Management adapted to the special circumstances of construction need to be developed. In this report, we discuss three systematic approaches to prevent construction failures. First, we discuss the statistical analysis of large samples of construction failures (macroscopic analysis) to identify primary failure modes and derive preventive measures. Next, we propose to select typical cases from the most frequently occurring construction failure modes. A detailed analysis (microscopic analysis) of the selected cases can then also lead to preventive measures. Finally, we review the transfer of safety management methods from risk-conscious industrial sectors, such as aviation, to construction (methodological analysis).

Sample Size Estimates for 2k-p Designs with Binary Responses

When the number of defectives (nonconforming products) is to be used as the response in two-level factorial and fractional factorial experiments, it is helpful to have an estimate of the sample size necessary for detecting a specific change. In this art..

A Conceptual Framework for the use of Quality Concepts and Statistical Methods in Product Design

SUMMARY Quality control based on inspection and segregation of defective parts is uneconomical and inefficient. To be effective, quality needs to be planned at the product design stage. In this article we develop a conceptual model for the product design process that incorporates quality improvement concepts at the design stage and bring into a broader context the role of statistical problem-solving, trouble-shooting and experimental design. The conceptual model for the design process is based on the idea of cyclic incremental improvement.