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Unit Summary

Unit type

PG Coursework Unit

Credit points

12

AQF level

9

Level of learning

Introductory

Unit aim

Provides students with a fundamental understanding of experimental logic and statistical methods commonly-used in marine and forest science.  Students will gain experience with fundamental statistical analyses in SPSS and PRIMER/PERMANOVA.

Unit content

  • Parameters for defining populations
  • A logical framework for scientific research
  • Student's t-test, chi-squared tests and analysis of variance
  • Type I/Type II errors and power analysis
  • Multi-factor analysis of variance in SPSS
  • Multivariate and univariate analysis in PRIMER/PERMANOVA
  • A primer to quantitative fisheries modelling

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.

GA1: , GA2: , GA3: , GA4: , GA5: , GA6: , GA7:
On completion of this unit, students should be able to: GA1 GA2 GA3 GA4 GA5 GA6 GA7
1 Interpret, summarise, analyse and graphically represent scientific data using SPSS
2 Interpret, analyse and graphically represent scientific data using PRIMER/PERMANOVA
3 Design appropriate experiments to address key issues in marine or forestry science
4 Critically evaluate of the experimental design and statistical analysis of published scientific papers

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

  1. Interpret, summarise, analyse and graphically represent scientific data using SPSS
    • GA1:
    • GA4:
  2. Interpret, analyse and graphically represent scientific data using PRIMER/PERMANOVA
    • GA1:
    • GA4:
  3. Design appropriate experiments to address key issues in marine or forestry science
    • GA1:
    • GA2:
    • GA4:
    • GA5:
  4. Critically evaluate of the experimental design and statistical analysis of published scientific papers
    • GA1:
    • GA4:
    • GA5: