Statistics For Biomedical Engineering 3 STATS3002
- Academic Session: 2020-21
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 3 (SCQF level 9)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Available to Erasmus Students: No
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.
2 one-hour lectures per week (Weeks 1 - 10)
4 two-hour tutorial/practical sessions, at fortnightly intervals (Weeks 3, 5, 7 and 9)
Requirements of Entry
Grade D or better in Applicable Mathematics 1A and 1B.
Statistics 1Y, Statistics 1Z, Statistics 1A.
90-minute, end-of-course examination (80%)
Online coursework tasks (20%)
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
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
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
Minimum Requirement for Award of Credits
Students must sit the end-of-course examination.