Overview

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Academic contacts

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Offerings

MURDOCH-S1-EXT-2018-2024
MURDOCH-S1-FACE2FACE-2025-ONGOING
MURDOCH-S1-INT-2018-2024
MURDOCH-S1-ONLINEFLEX-2025-ONGOING

Enrolment rules

Student must have a calculus background equivalent to at least either MAS161 Calculus and Matrix Algebra OR MAS221/MAS208 Mathematical Modelling.
Enrolment in a postgraduate IT course is an alternative pre-requisite condition if students have not completed any of the pre-requisite units.

Other learning activities

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Learning activities

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Learning outcomes

1.

Recognize and understand the nature of correlated data, and the computational methods for their analysis. 

2.

Recognize autocorrelated data or data which are correlated over time (as is in the nature of many time series data) and learn techniques - of both a simple nature and of a more complex nature, by way of which one may analyse such data. These techniques can include straight forward regression in time or further more appropriate methods in some contexts which involve what is known as the “Time Domain Approach”.

3.

Recognize cyclical patterns that are analysed in the frequency domain. Ideas of spectral analysis and the “Frequency Domain Approach” are countenanced, and numerical approaches.

4.

Understand bivariate processes that can be analysed using the Cross Spectrum and these lead into the theory of Linear Systems in both the Time Domain and the Frequency Domain.

Assessments

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Additional information

Unit content:Topics covered in this unit include: • Correlation and the multivariate normal density • Examples of time series • Simple descriptive techniques • Some time-series models • Fitting time series models in the time domain • Forecasting • Stationary processes in the frequency domain • Spectral analysis • Bivariate processes • Linear systems