620-370 Statistics for Mechanical Engineers

This subject is only available to engineering students. Combined science/engineering students should speak to an engineering course adviser before enrolling in this subject as it may be recommended that they complete mathematics and statistics subjects which earn science credit instead.

This subject is not available for science credit points.

Students in the combined degrees BE/BSc or BE/BCom, and students wishing to have access to all 300-level statistics subjects, are advised to enrol in both 620-201 and 620-202 instead of 620-370.

Students may not gain credit for any of 620-152 or 620-160 after having completed 620-370.

It is not possible to gain credit for both 620-370 and any of the following subjects: 620-201, 620-202, 620-205, 620-270, 620-272.

One of 620-113, 620-123, 620-143 or [05]620-193.

This subject introduces the fundamental concepts of probability and statistical inference. Students should develop the ability to use simple probability models in applications to standard situations and to carry out standard statistical analyses. This subject shows the breadth of application of statistics and the important role statistics has in quality improvement, and covers the following topics: basic probability theory; simple probability models (including Bernoulli trials, Poisson processes, sampling models); random variables and descriptions of their probability distributions; simple distribution theory, including binomial, Poisson and normal distributions; mean and variance: the importance of variance in quality management, engineering practice and decision-making under uncertainty; quality checking: acceptance sampling; exploratory data analysis; random sampling and properties of random samples; introduction to statistical inference: estimation, confidence intervals and hypothesis testing in standard situations based in the binomial, Poisson and normal distributions; quality management: control charts; analysis of variance; linear regression and prediction; multiple regression and polynomial regression; and quality improvement: the principles of experimental design and the analysis of some simple designed experiments, including factorial designs and Taguchi methods.

Up to 50 pages of written weekly homework during the semester (20%); a 3-hour written examination in the examination period (80%).