EEEN40660 Experimental Design and Statistics for Engineers

Academic Year 2022/2023

This module covers fundamental principles of experimental design and statistics, and the practical implementation of a comprehensive range of statistical analyses in Python. The module is designed primarily to cater for biomedical engineering students who require a breadth of knowledge in this domain, but the principles and skills generalise to any area of science and engineering. The module is thus suitable for students with any background that comes with fundamental mathematical / analytic skills.

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Curricular information is subject to change

Learning Outcomes:

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

Indicative Module Content:

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 Hours: 
Student Effort Type Hours


Computer Aided Lab


Specified Learning Activities


Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures: 2-4 videos per week with content aligned with lab work from week to week, to be viewed asynchronously, available in Brightspace.
One in-person tutorial session per week for discussions, group work, and journal club work pertaining to the lecture content.
Active/task-based learning: Every week there will be Python programming/analysis demos and tasks to complete, which are to be worked on partially autonomously and partially within a weekly 2-hour lab. It will be possible to join by Zoom if isolating due to Covid.
A 15-minute quiz (one hour in week 12) will be given in brightspace in each lab session, with the password called out on the zoom call. Therefore, attendance to each lab is necessary.
2 Data analysis assignments will further engage and formally assess task-based learning.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have previously taken basic probability theory (e.g. from STAT20060)

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: 10 weekly quizzes starting week 2, each 15 min, 2% of grade
1 final 1-hour quiz in week 12, 25% of grade
Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


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 Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


Assignment: 2 data analysis/simulation assignments, each worth 20%.
Due approximately in week 7 and 12
Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Tutorial Offering 1 Week(s) - Autumn: All Weeks Fri 12:00 - 12:50
Laboratory Offering 1 Week(s) - Autumn: All Weeks Wed 11:00 - 12:50