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Qualification Regulations
Part I
These regulations are to be read in conjunction with all other Statutes and Regulations of the University including General Regulations for Postgraduate Degrees, Postgraduate Diplomas, and Postgraduate Certificates.
Part II
Admission
1. Admission to the Degree of Master of Applied Statistics requires that the candidate will:
(a) meet the University admission requirements as specified; and
(b) shall have been awarded or qualified for a Bachelor’s degree with a major in Statistics, or equivalent, having achieved a B grade average or higher over the qualifying highest level courses.
Qualification requirements
2. Candidates for the Degree of Master of Applied Statistics shall follow a parts-based programme of study, which shall consist of courses totalling at least 180 credits, comprising:
(a) completion of Part One and Part Two as defined by the Schedule to the Degree;
(b) courses selected from the Schedule to the Degree; and including:
(c) any Compulsory Courses listed in the Schedule to the Degree.
Specialisations
3. The Degree of Master of Applied Statistics is awarded without specialisations.
Student progression
4. For progression from Part One to Part Two, candidates must have achieved a minimum Grade Average of B over the Part One courses.
5. In cases of sufficient merit, the Degree of Master of Applied Statistics may be awarded with Distinction or Merit.
Completion requirements
6. The timeframes for completion as outlined in the General Regulations for Postgraduate Degrees, Postgraduate Diplomas, and Postgraduate Certificates will apply.
7. Candidates may be graduated when they meet the Admission, Qualification and Academic requirements within the prescribed timeframes; candidates who do not meet the requirements for graduation may, subject to the approval of Academic Board, be awarded the Postgraduate Diploma in Science and Technology (Statistics) should they meet the relevant Qualification requirements.
Unsatisfactory academic progress
8. The general Unsatisfactory Academic Progress regulations will apply.
Schedule for the Master of Applied Statistics
Course planning key
- Prerequisites
- Courses that need to be completed before moving onto a course at the next level. For example, a lot of 200-level courses have 100-level prerequisite courses.
- Corequisites
- Courses that must be completed at the same time as another course are known as corequisite courses.
- Restrictions
- Some courses are restricted against each other because their content is similar. This means you can only choose one of the offered courses to study and credit to your qualification.
Key terms for course planning
- Courses
- Each qualification has its own specific set of courses. Some universities call these papers. You enrol in courses after you get accepted into Massey.
- Course code
- Each course is numbered using 6 digits. The fourth number shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).
- Credits
- Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.
- Specialisations
- Some qualifications let you choose what subject you'd like to specialise in. Your major or endorsement is what you will take the majority of your courses in.
Part One
Compulsory courses (Choose 30 credits from)
Course code: 161705 Advanced Statistical Inference 15 credits
Properties of estimators: unbiasedness, consistency, efficiency and sufficiency. Methods of estimation with particular emphasis given to the method of maximum likelihood. Hypothesis testing and interval estimation. Nonparametric tests. Computationally intensive methods such as numerical likelihood estimation and Monte Carlo inference. Resampling methods.
View full course detailsCourse code: 161770 Statistical Consulting 15 credits
Students are given the opportunity to serve as a consultancy intern with close supervision of staff involved in consultancy activities. Instruction and experience in consultant/client interaction, communication skills, statistical practice, statistical computation and technical writing.
View full course detailsElective courses (Choose 90 credits from)
Course code: 161704 Bayesian Statistics 15 credits
Introduction to the Bayesian paradigm. Markov Chain Monte Carlo estimation using WinBUGS. Comparison with frequentist statistics. Noninformative and improper priors. Inference and model selection. Linear and generalized linear models. Hierarchical Bayes.
View full course detailsCourse code: 161709 Topic in Statistical Theory 15 credits
A topic in the theory of statistics, such as probability theory, Bayesian statistical theory, statistical decision theory, martingales and stochastic integrals.
View full course detailsCourse code: 161725 Statistical Quality Control 15 credits
Revision of statistical process control procedures, evaluation of control chart performance and statistical design of charts, control of high quality process, multivariate process control, new process capability indices, statistical intervals. Industrial experimentation topics, evolutionary operation, analysis of means (ANOM) etc. Revision of acceptance sampling, continuous and special purpose sampling plans. Use of statistical packages.
View full course detailsCourse code: 161743 Statistical Reliability and Survival Analysis 15 credits
Lifetime data occur in a wide variety of contexts: medical, demographic, industrial, economic. This course gives an introduction to the theory and practice of analysing lifetime data, commonly called survival analysis in medical contexts and reliability analysis in engineering.
View full course detailsCourse code: 161744 Statistical Genetics 15 credits
Statistical methods for biological sequence analysis, analysis of gene expression data, and inference of biological networks. Applications will also be described in evolution and population genetics.
View full course detailsCourse code: 247792 Special Topic 15 credits
A course of study which will be designed to either meet the individual requirements of a student in a special circumstance or be used to facilitate development of a new course.
View full course detailsCourse code: 247793 Special Topic 15 credits
A course of study which will be designed to either meet the individual requirements of a student in a special circumstance or be used to facilitate development of a new course.
View full course detailsIncluding up to 30 credits from:
Course code: 161762 Multivariate Analysis for Big Data 15 credits
Research methods suitable for the analysis of big datasets containing many variables. The fundamentals of data visualisation, customer segmentation, factor analysis and latent class analysis with examples taken from business and health fields. Emphasis will be placed on achieving a conceptual understanding of the methods in order to implement and interpret the outcomes of multivariate analyses.
View full course detailsCourse code: 161777 Practical Data Mining 15 credits
A practical approach to data mining with large volumes of complex data; prepare, cleanse and explore data; supervised and unsupervised modelling with association rules and market basket analysis, decision trees, multi-layer neural networks, k-nearest neighbours, k-means clustering and self-organising maps, ensemble and bundling techniques, text mining; use of leading software tools; business examples and research literature.
View full course detailsPart Two (Choose 60 credits from)
Course code: 161893 Research Report 60 credits
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