English abstract
Engineers require scientific methods whereby models are developed to explain real phenomena. Model building, data collection, data analysis, and data interpretation form the very core of any sound engineering practice. Therefore statistical methodologies are vital components in engineering curricula and engineers should have the ability to think statistically when dealing with data. They should learn how to design and conduct well-planned experiments to improve the efficiency of the process and the quality of products, and must learn to deal with data, and interpret results produced as a part of their data analysis skills. Statistical methods are vital in engineering practices such as process monitoring by control charts, process optimization by response surface methodology, determining important factors by hypothesis testing, process modelling by regression analysis, initial pilot plant operation by design of experiments and laboratory recommendation. This paper shares some of the experiences of teaching statistics to undergraduate engineering students in an Australian University, focusing on the appropriate content, teaching technique, educational technology, software package, online support and evaluation in an engineering problem solving course. Results from an online survey of students are also presented.
