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Unit information: Applied Statistics in 2020/21

Unit name Applied Statistics
Unit code EMAT30007
Credit points 10
Level of study H/6
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Bode
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

The unit is designed to provide students with practical experience in using statistical methodologies to solving real-world problems. The key word in the unit is “practical.” A focus on both real data and simulated realistic data will be a major component of the applied statistics unit. Each concept will be taught in a bottom-up way that will allow students to understand the vital role of the field of probability and statistics in academic and industrial engineering research.

Aims: To give students a thorough grounding in statistical methodologies and to enable students to use these techniques to answer questions about real world data from engineering / applied science.

Intended learning outcomes

  1. Use discrete and continuous probability distributions, sampling distributions, simple and multivariate linear regression. Understand classical statistical inference and have an appreciation of model comparison.
  2. In addition to covering more advanced topics related to traditional multiple regression/correlation, the students will understand logistic regression, experimental design, reliability data analysis and bootstrap techniques.
  3. Experience of applying statistical techniques to data.
  4. Select statistical methodologies for new and unfamiliar problems and justify their use.
  5. Practise interpreting the results obtained from using of statistical methodologies.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, supported by live online sessions, problem sheets and self-directed exercises.

Assessment Details

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.

Reading and References

W.Mendenhall & T. Sincich, "Statistics for Engineering and the Sciences", CRC Press, 2016.

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