SCIEN512-22A (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)

Conrad Pilditch

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

Terry Tang

Terry Tang
See Moodle
terry.isson@waikato.ac.nz

Administrator(s)

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: cheryl.ward@waikato.ac.nz

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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Paper Description

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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 biological and 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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Paper Structure

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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 a tutorial on coding and troubleshooting in R.
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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • 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:
  • Assess whether statistical models have been fitted correctly and perform model selection using AIC.
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  • Appropriately graph and present results from statistical analyses using R.
    Linked to the following assessments:
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Assessment

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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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Assessment Components

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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
31 Mar 2022
No set time
15
  • Online: Submit through Moodle
2. Multi-factor models
14 Apr 2022
No set time
15
  • Online: Submit through Moodle
3. ANCOVA, interactions & model selection
19 May 2022
No set time
15
  • Online: Submit through Moodle
4. Modelling non-normal data
2 Jun 2022
No set time
15
  • Online: Submit through Moodle
5. Quiz: Experimental design and Sampling
9 Jun 2022
No set time
5
  • Online: Submit through Moodle
6. Major assignment
17 Jun 2022
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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Required and Recommended Readings

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Recommended Readings

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Logan, M. (2010) Biostatistical Design and Analysis Using R: A Practical Guide. John Wiley & Sons, Inc.

Crawley, M.J. (2013)The R Book, Second Edition. John Wiley & Sons, Inc.

E-books are available from the library.

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Online Support

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Online support will be provided via Moodle, which is accessible to all students who are enrolled in the paper.

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Workload

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Ten scheduled seminars, each up to two hours, one online quiz, and five assignments. The assignments will be undertaken independently, and outside of classes, and should take approximately 8 hours per seminar associated with each. Around 24 hours should be spent on the major assignment.
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Linkages to Other Papers

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Prerequisite(s)

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: BIOL503

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