Medical Devices Engineering MSc
Statistics For Biomedical Engineering 3 STATS3002
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 3 (SCQF level 9)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This is a compulsory course in applied statistics for Engineering students enrolled on a degree programme in Biomedical Engineering. It introduces standard probability distributions, parametric confidence intervals, hypothesis tests and simple linear regression, and shows how these methods are applied in biomedical engineering contexts.
Timetable
2 one-hour lectures per week (Weeks 1 - 10)
4 two-hour tutorial/practical sessions, at fortnightly intervals (Weeks 3, 5, 7 and 9)
Excluded Courses
Statistics 1Y, Statistics 1Z, Statistics 1A.
Co-requisites
None.
Assessment
90-minute, end-of-course examination (80%)
Online coursework tasks (20%)
Reassessment
There will be no opportunity for re-assessment of the online tasks.
Main Assessment In: December
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
Course Aims
The aims of this course are:
■ to make students aware of how statistical issues should affect the design of observational and experimental studies in biomedical engineering
■ to extend students' knowledge of random variables, especially standard probability distributions
■ to introduce point estimation, confidence intervals and hypothesis testing
■ to introduce simple linear regression as a method for modelling data and obtaining predictions
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ describe how to design simple observational and experimental studies, with a view to avoiding bias and improving precision
■ use properties associated with the Binomial, Multinomial and Normal distributions, using tables of values where appropriate
■ calculate and interpret a point estimate and a confidence interval for a population mean or proportion, or a difference between population means or proportions
■ calculate and interpret a prediction interval
■ test hypotheses about a population mean or proportion, or a difference between population means or proportions, and interpret the results
■ carry out a chi-squared test of association and interpret the results
■ fit and interpret a simple linear regression model, and comment on its fit to the data
■ use a fitted regression model to calculate confidence and prediction intervals