A first course in mathematical statistics with emphasis on applications; probability, random variables, moment generating functions and correlation, sampling distributions, estimation of parameters by the methods of moments and maximum likelihood, hypothesis testing, application of the central limit theorem to large sample inference.

Upon successful completion of the requirements for this course, students will achieve an understanding of and facility in the following topics:

• Introductory probability including combinatorics and Bayes' theorem
• Discrete random variables and their probability distributions
• Continuous random variables and their probability distributions
• Multivariate random variables and their probability distributions
• Sampling distributions and the central limit theorem
• The method of moments and maximum likelihood estimation
• Confidence estimation and hypothesis testing

A sound understanding in this course will facilitate a strong progression toward a solid foundation in mathematical statistics. Furthermore, mastering this course will provide a springboard for students wishing to excel in later courses such as Stochastic Modelling, Generalised Linear Models, Statistical Inference, Risk Theory, Survival Modelling and Time Series. Coupled with Probability Theory (MATH3029), this course will provide a deep understanding of Probability and its applications, a fundamental keystone for pursuing courses in stochastic calculus and statistics.

• Final Examination (60% or 80%)
• Mid-Semester Examination (20% or 0%) (redeemable in favour of the Final)
• Two Assignments (10% each)   

Four contact hours per week plus private study time.

Either MATH1113 Mathematics Foundations for Statistics or MATH1116 Mathematics for Applications 2 (H) or MATH1014 Mathematics and Applications, and either STAT1003 Statistical Techniques or STAT1008 Quantitative Research Methods.

Please refer to Course Website