Postgraduate Course: Statistical Inference (MATH11129)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	7.5	ECTS Credits	3.75
Summary	This course aims to provide postgraduate students with a broad knowledge of the principal areas of mathematical statistics and statistical methods widely used in actuarial science and finance. Students should have a good grounding in probability before commencement of this course. This course is only available to MSc Financial Mathematics students.
Course description	- Sampling distributions, central limit theorem, t and F distributions - Estimation - properties of estimators, methods of constructing estimators - Interval estimation - Hypothesis testing - Linear relationships - regression and correlation

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Statistical Methods (MATH11070)	Other requirements	MSc Financial Mathematics students only.

Course Delivery Information

Academic year 2015/16, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 75 ( Lecture Hours 15, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 56 )
Additional Information (Learning and Teaching)	Examination takes place at Heriot-Watt University.
Assessment (Further Info)	Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment)	See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)	Statistical Inference (MATH11129)		2:00

Learning Outcomes
On completion of this course the student should be able to: - demonstrate knowledge of, and a critical understanding of, statistical methodologies (including the main concepts and methods of inference and modelling) - understand and apply a range of statistical techniques based on the main theories and concepts which comprise the syllabus, including the central limit theorem - determine properties of estimators: efficiency, Cramer-Rao lower bound, (approx.) large sample distributions of MLEs - perform inference on parameter estimates, including constructing confidence intervals and testing hypotheses on the values of parameters - fit a linear regression model and critically evaluate other proposed models; test hypotheses concerning correlation coefficients - show an awareness of how different statistical models and techniques can be applied to financial problems - communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry and elsewhere) on matters relating to and/or requiring the use of statistical methods