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Unit of Study MAT03088 - Statistics for Science (2016)

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Unit Snapshot

Unit description

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

Availabilities

Not currently available in 2016

Learning outcomes and graduate attributes

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:

GA1: , GA2: , GA3: , GA4: , GA5: , GA6: , GA7:
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: