| Unit name | Regression Models |
|---|---|
| Unit code | BRMSM0056 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Jon Heron |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health Sciences |
Why is this unit important?
Regression models are a statistical method that we commonly use to estimate associations between explanatory and outcome variables, including for causal inference. These can be used to answer important healthcare questions that can contribute to clinical decision-making and improve the health of both individuals and populations.
This unit will enable you to understand, interpret and carry out statistical analysis of data using a range of regression models. Real world examples will be used to demonstrate how these models are applied in epidemiology.
How does this unit fit into your programme of study?
This unit is designed to give you the tools you need to model associations between variables of different types (e.g. continuous, binary, categorical) which can be used for causal inference or prediction purposes. It provides the basis for a lot of techniques that will be taught within the Advanced Statistics unit (BRMSM0058).
An overview of content:
The topics covered are as follows:
How will students, personally, be different as a result of the unit:
Understanding how to estimate and interpret regression models is fundamental to most statistical analyses of health data. You will be able to specify appropriate regression models for a given task and implement these using the statistical software package R.
Learning Outcomes:
On successful completion of the unit, you should be able to:
The learning of regression modelling approaches is most effective when it is practice-based. Teaching on this course will consist of lectures to introduce theoretical concepts and modelling approaches, and accompanying practicals to gain experience applying regression models in R and appropriately interpreting output.
Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis, and completion of assessments.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative assessments will come in many forms such as informal questioning, quizzes and group exercises in lectures, tutorials and homework (ILOs 1-5). These assessments are for learning and will not contribute to the final unit mark. There will also be a single piece of coursework taking the form of a data analysis and interpretation exercise. You will be given a set of analytical tasks to complete. This coursework will be similar in structure to the summative assessment coursework. Model answers will be provided allowing you to gain additional feedback on understanding prior to the final assessment (ILOs 1, 2 & 5). Practice exam questions will be made available along with model answers and there will be opportunity to discuss these in class (ILOs 2-5).
Tasks which count towards your unit mark (summative):
There will be one piece of coursework (50%; ILOs 1-3 & 5) and one exam (50%; ILOs 2-5) for this unit. The coursework will take the form of a data analysis and interpretation exercise, structured as a set of short answer questions. You will be given a set of analytical tasks to complete, including stating the strengths and limitations of your analysis and discussing possible alternatives, with suitable justification.
When assessment does not go to plan:
If you do not pass the unit, you will normally be given the opportunity to take a reassessment as per the Regulations and Code of Practice for Taught Programmes. Decisions on the award of reassessment will normally be taken after all taught units of the year have been completed. Reassessment will normally be in a similar format to the original assessment that has been failed.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0056).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
