MATH3851 is a Mathematics Level III course. See the course overview below.
Units of credit: 6
Prerequisites: MATH2801 or MATH2901 and MATH2831 or MATH2931
Cycle of offering: Term 3
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: This recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus.
The Online Handbook entry contains up-to-date timetabling information.
If you are currently enrolled in MATH3851, you can log into UNSW Moodle for this course.
The course is divided into two parts: Experimental Design, and Categorical Data Analysis.
In Experimental Design, students will learn about the importance of experimental design and about principles that allow them to extract maximum amount of information for a given sample size from available sources. They will study how to set optimally their factorial and randomised designs in scientific or engineering work.
In Categorical Data Analysis, students will learn about statistical tools and techniques that are specifically tailored towards analysing discrete valued data such as counts, frequencies, survey data. They will be able to answer questions about presence or absence of association between categorical variables using cross-tabulated data. They will also learn how to model the association between the categorical variables by using techniques such as Logistic, Poisson regression and Log-linear models. They will develop an understanding of the methodology and will be able to apply it to practical analysis of real datasets.
This course focuses on the principles of good experimental design and the statistical tools appropriate for discrete valued data. Topics include factorial designs and their analysis, response surface designs for product and process optimization, random effects models and components of variance, exploratory and graphical analysis of data using modern statistical packages, data visualization, analysis of cross-tabulated data, logistic and Poisson regression for analysis of binary and count data and log-linear models for contingency tables.