Course co-ordinator(s): Dr Marcelo Pereyra (Edinburgh).
Aims:
In this course students will
• develop an understanding of the different methodologies of statistical inference
• develop skills in practical, computer-based estimation and inference
• develop report writing and presentation skill
• develop independent research skills
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 1.
Syllabus:
• Inference and decision making
• Parameter estimation
• Likelihood
• Bayesian estimation and credibility theory
• Hypothesis testing
• Project preparation
• Applied statistical project
Learning Outcomes: Subject Mastery
On completion of this course, students will be able to demonstrate:
• understanding of the theoretical bases of three main approaches to statistical inference and the relations between them
• ability to apply all three main approaches to inference in a number of examples
• ability to understand and assess applicability and limitations of these approaches working with data sets in a practical setting
• critical analysis of quantities of interest and conclusions made using statistical inference
• develop their problem-solving skills
• gain ability to critically understand and apply relevant approaches to statistical inference in a practical setting
Learning Outcomes: Personal Abilities
At the end of the course, students should be able to:
• Demonstrate the ability to learn independently
• Manage time, work to deadlines, and prioritise workloads
• Use an appropriate computer package to process data
• Present results in a way which demonstrates that they have understood the technical and broader issues of statistical inference
• Demonstrate high levels of numeracy required for working with data
• Develop and demonstrate skills to communicate with peers as well as with academic staff
• Develop and demonstrate skills in using statistical software
Students will organise their learning and working on a project through time management.
Assessment Methods: Due to covid, assessment methods for Academic Year 2020-21 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 11.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the lecturer
VISION: further information and course materials are available on VISION