Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. |
No incompatible modules listed |
Co-requisite Modules
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No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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No requirements listed |
Recommended Book Resources |
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- Richard K. Burdick, David J. LeBlond, Lori B. Pfahler et al. 2017, Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry, 1st Ed., Springer [ISBN: 3319501844]
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Supplementary Book Resources |
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- John A Rice 2007, Mathematical Statistics and Data Analysis, 3rd Ed., Duxbury Press [ISBN: 9780495118688]
- Samson Weisberg 2013, Applied Linear Regression, 4th Ed., Wiley [ISBN: 9781118386088]
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Supplementary Article/Paper Resources |
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- Ahmed Ismail, Hong-Linh Truong & Wolfgang Kastner 2019, Manufacturing process data analysis pipelines: a requirements analysis and survey, Journal of Big Data, 6(1)
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This module does not have any other resources |
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