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Unit of Study STAT2003 - Data Collection and Quantitative Analysis (2023)

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Unit Snapshot

  • Enrolment information

    N/A

Unit description

Successful scientific surveys and experiments rely on a sound design, sampling strategy and analysis of data. This unit introduces students to intermediate-level concepts and practice for planning, implementing, analysing and interpreting surveys and experiments in the natural environment and laboratory. The logical approach to survey design is emphasised and undertaken in practical exercises, which may involve working in groups. Common statistical methods and graphical illustration will be applied to the analysis of class data. It is expected that students undertaking this unit will have existing knowledge of introductory statistics and basic skills in data management.

Unit content

1) Hypothesis testing with experiments and surveys

2) Experiment and survey design

3) Sampling and data collection strategies and techniques

4) Power and cost-benefit analyses

5) Intermediate statistical analyses 

6) Multivariate analyses and a glimpse into Big Data

Availabilities

Location Domestic International
China - Guangxi UST N/A
Term4
Gold Coast
Term4
Term4
Lismore
Term4
Term4
Melbourne N/A
Term4
National Marine Science Centre Coffs Harbour
Term4
Term4
Online
Term4
Term4
Papua New Guinea - IBSU Port Moresby N/A
Term4
Perth N/A
Term4
Sydney N/A
Term4

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

apply discipline-specific skills in study design and systematic data collection

select and apply appropriate practical and theoretical methods, techniques and tools to collect and accurately record data through systematic inquiry

synthesise and interpret scientific data to draw rigorous conclusions and solve problems relevant to the discipline

work collaboratively, responsibly and safely as part of a team.

Prescribed learning resources

Prescribed Texts
  • No prescribed texts.
Prescribed Resources/Equipment

  • You will need access to a Windows, MacOS or Linux computer and install the software package R (https://www.r-project.org/).

Prescribed Learning Resources may change in future Teaching Periods.