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DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Health Information

Postgraduate Course: Intermediate Statistics for Health and Social Care (HEIN11065)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis online course provides an in-depth coverage of commonly used statistical modelling techniques: linear regression, logistic regression and linear mixed effect modelling.
Course description 1) Academic description
Understanding linear models is crucial for practitioners of data science, as linear models are the foundation of machine learning approaches. This course builds on the basic principles of statistics and introduces students to commonly used modelling approaches. It employs an applied perspective by analysing real-life data from health, social and care services using the statistical software R.

This course assumes a basic level of statistical knowledge and familiarity with R, RStudio and R Markdown.

2) Outline Content
The course will build on students┬┐ existing knowledge of the principles of statistics and will cover the following topics: simple linear regression, multiple linear regression, logistic regression, linear mixed effect models. The material is presented to understand rather than memorise statistical concepts.

3) Student Learning Experience
Students will learn from experts in statistics. The course is delivered online and is divided into five sessions, each lasting a week. Teaching sessions will be composed of written materials and video presentations, accompanied by conceptual and programming exercises.

Discussion of the content and reading materials will be posted to an online forum. Course tutors will moderate discussion boards. Students will further evidence their learning by completing a problem-based learning assignment by the end of the course.
Formative peer and teacher-led feedback will be given throughout the course through the discussion boards, and summative assessment feedback will be provided at the end of the course.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs Students will be responsible for their computer equipment and internet access.
Information for Visiting Students
Pre-requisitesIntroduction to statistics in health and social care (HEIN11039) or equivalent, plus knowledge of R statistical programming environment
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Use critical understanding of statistical modelling to match model type (linear regression, logistic regression, linear mixed effects model) to outcome data and understand underlying assumptions
  2. Fit models and interpret the relevant coefficients
  3. Think critically about and use statistical models to solve problems in health and social care and communicate these solutions to a range of audiences
  4. Use statistical software confidently and critically, and interpret and communicate model output
Reading List
Book: Mine Çetinkaya-Rundel and Johanna Hardin (2021). 'Introduction to Modern Statistics.' An online version is available for free at

Book: Max Kuhn and Julia Silge (2022). 'Tidy Modelling with R.' An online version is available for free at
Additional Information
Graduate Attributes and Skills 1) Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in probability and statistics. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve statistical issues as they arise in health and social care systems.

Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within, and contribute positively, ethically and respectfully to, the health and social care community, while acknowledging that different students and community members will have other priorities and goals.

Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context, to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.

2) Skills:
Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.

Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.

Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.

Effective data science practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills.
Special Arrangements This course will be taught online using the Learn virtual learning environment. All course materials are protected by secure username and password access.
Keywordsstatistics,statistical methods,statistical modelling,R,regression,linear,mixed effect models
Course organiserDr Kasia Banas
Course secretaryMrs Laura Miller
Tel: (0131 6)51 5575
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