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 be able to:

• Calculate probabilities using set theory, combinatorics, the sample point method and the event composition method
• Solve statistical problems involving discrete distributions
• Solve statistical problems involving continuous and multivariate distributions
• Approximate probabilities using the central limit theorem
• Construct point and interval estimates and conduct hypothesis tests

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)   

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