SCIEN512-23A (HAM)

Data Analysis and Experimental Design

15 Points

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Division of Health Engineering Computing & Science
School of Science

Staff

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Convenor(s)

Andrew Barnes

Andrew Barnes
4460
F.G.16B
See Moodle
andrew.barnes@waikato.ac.nz

Lecturer(s)

Ang McGaughran

Ang McGaughran
5046
R.2.22
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angela.mcgaughran@waikato.ac.nz

Conrad Pilditch

Conrad Pilditch
9393
R.2.21
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conrad.pilditch@waikato.ac.nz

Administrator(s)

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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What this paper is about

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This paper covers methods for analysing data from scientific experiments and field studies using linear modelling techniques, with a general emphasis on environmental sciences. The paper will provide students with the means to design testable experiments, select appropriate statistical models to analyse data from a range of scenarios, and to conduct these analyses and graphically present results using the R statistical software.
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How this paper will be taught

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This paper will be taught via a series of ten interactive computer labs comprising a mixture of both lectures and computer exercises in each session. Attendance in computer labs is absolutely essential and forms the core of this paper. There will also be tutorials on coding and troubleshooting in R.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Appropriately graph and present results from statistical analyses using R
    Linked to the following assessments:
  • Assess whether statistical models have been fitted correctly and perform model selection using AIC
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  • Confidently design scientific experiments and observational studies that can be statistically analysed using appropriate statistical models
    Linked to the following assessments:
  • Perform a range of linear modelling techniques including ANOVA, regression, and GLM in the statistical software, R, and confidently decide on which statistical method should be applied for a range of contexts
    Linked to the following assessments:
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Assessments

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How you will be assessed

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This course is entirely assessed through internal assignments. You will complete one quiz and four assignments which will be completed alongside the labs, adding up to 50% of the course weighting, and have a final assignment worth 50%. The final assignment should be treated as a 'take home test', and worked on independently of others. The dates indicated for assessment procedures will normally be adhered to. Any changes to the dates will be made in consultation with the class at least one week prior to the original date.
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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Regression and ANOVA
23 Mar 2023
No set time
15
  • Online: Submit through Moodle
2. Multi-factor models
6 Apr 2023
No set time
15
  • Online: Submit through Moodle
3. ANCOVA, interactions & model selection
11 May 2023
No set time
15
  • Online: Submit through Moodle
4. Modelling non-normal data
25 May 2023
No set time
15
  • Online: Submit through Moodle
5. Quiz: Experimental design and Sampling
31 May 2023
No set time
5
  • Online: Submit through Moodle
6. Major assignment
9 Jun 2023
No set time
35
  • Online: Submit through Moodle
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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