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Faculty of Science Handbook, Academic Session 2025/2026
to industry-level, open source computing Assessment:
tools such as R; Data management; Continuous Assessment: 40%
Graphical visualisation including spatial Summative Assessment: 60%
data; Analysis and interpretation of real
data sets with varying degrees of SIT2010
complexity using appropriate statistical STOCHASTIC PROCESSES
methods.
Definition and examples of stochastic
Assessment: processes: Gambler’s ruin problem,
Continuous Assessment: 50% Brownian motion and Poisson process.
Summative Assessment: 50% Introduction to simple random walk.
Discrete time Markov Chains. Transition
SIT2008 probability. Properties of class. Transience
FURTHER MATHEMATICAL STATISTICS and recurrence properties. Absorbing
probability. Stationary distribution and
The exponential family. Sufficient, limiting probability. Markov chain
complete and ancillary statistics. Minimum simulations and applications.
variance unbiased estimators. Bayesian
estimation. Delta method for asymptotic Assessment:
approximation. Distributions of certain Continuous Assessment: 40%
quadratic forms: one and two factors Summative Assessment: 60%
analysis of variance. Probability measure
space. law of large numbers. Borel-Cantelli
lemma. SIT2011
STATISTICS AND COMMUNITY
Assessment:
Continuous Assessment: 40% This course exposes students to some
Summative Assessment: 60% aspects of statistics in community. The main
aim is to highlight the role of official
SIT2009 statistics in society. The topics chosen for
REGRESSION ANALYSIS this course come from a variety of different
areas, for example, statisticians and their
Simple linear regression: Estimation, work, statistics and technology, and
hypothesis testing, analysis of variance, statistics and society. Students will work in
confidence intervals, correlation, residuals groups on projects related to the topics
analysis, prediction. Model inadequacies, discussed in lectures. Students will use
diagnostics, heterogeneity of variance, elements of statistics in the planning a
nonlinearity, distributional assumption, community project including designing
outliers, transformation. Selected topics questionnaire, collecting/
from matrix theory and multivariate normal managing/analyzing data and reporting the
distribution. Multiple linear regressions: findings. Each group is required to identify
Estimated multiple linear regression. and plan activities for a community
Hypothesis testing, ANOVA, Confidence partnership that will not only help them to
Interval, Model selection criteria, enhance their understanding or gain a
Diagnostics for influential observations and different perspective of their project but
multicollinearity. Introduction to logistic will also be beneficial to the community
and Poisson regression. partner. Each student will be required to
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