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Curricular information is subject to change
On successful completion of this module the student will:
- know how to engage in statistical thinking and rigorous scientific inquiry
- understand the basic mathematics underlying modern statistical analysis
- have proficiency in conducting data analyses using Python, the dominant and fastest-growing programming language used in the engineering and data science industries
- be able to carry out a range of statistical analyses including ANOVA, single- and multi-variable regression, logistic regression, repeated-measures and nonparametric tests (including permutation tests), linear mixed-effect models, and basic Bayesian statistics.
- understand basic principles of model selection and the relationship between statistical and mechanistic modeling
- understand how to design valid, effective, and statistically powered experiments for problems ranging from basic scientific investigations of biological processes to process validation in manufacturing
Motivating, designing and executing scientific studies; Principles of statistical inference; Tests for differences between 2 groups; Confidence intervals; Tests for differences among >2 groups; Multiple comparisons; Repeated-measures designs; Power analysis; Correlation; Simple regression; Logistic regression; Nonlinear regression; multiple regression; Tests for rates and proportions; Additional topics: Bayesian statistics; mechanistic models
Student Effort Type | Hours |
---|---|
Tutorial | 12 |
Computer Aided Lab | 24 |
Specified Learning Activities | 20 |
Autonomous Student Learning | 60 |
Total | 116 |
Students should have previously taken basic probability theory (e.g. from STAT20060)
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Quizzes/Short Exercises: each 15 min and worth 2% each, taken within weekly lab | Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11 | Alternative linear conversion grade scale 40% | No | 20 |
No |
Exam (Open Book): 1.5-hr exam invigilated in classroom, similar format to weekly quizzes but with more questions and more calculations. | End of trimester Duration: 2 hr(s) |
Alternative linear conversion grade scale 40% | No | 25 |
No |
Assignment(Including Essay): 2 data analysis/simulation assignments, each worth 20%. Due approximately in week 7 and 12 |
Week 7, Week 12 | Alternative linear conversion grade scale 40% | No | 40 |
No |
Report(s): Lab Report: 10 Python programming/analysis tasks will be completed and the Jupyter notebooks uploaded to Brightspace. This grade component will be awarded for any complete, non-duplicated attempt | Week 2, Week 3, Week 4, Week 5, Week 6, Week 8, Week 9, Week 10, Week 11 | Alternative linear conversion grade scale 40% | No | 15 |
No |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
Brief feedback will be provided to each student on Brightspace for their data analysis assignment submissions. Summary feedback will be provided via video content or broadcast messages on common pitfalls and tips related to the weekly task submissions. Submission of reports and feedback will all be conducted online through brightspace.