SCIEN512-20A (HAM)

Data Analysis and Experimental Design

15 Points

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Division of Health Engineering Computing & Science
School of Science

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: cheryl.ward@waikato.ac.nz

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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.
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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 relevant 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.
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  • 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.
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Assessment

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The assessment in this course is all through 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. Quiz: Experimental design and Sampling
27 Mar 2020
9:59 PM
6
  • Online: Submit through Moodle
2. Regression and ANOVA
6 Apr 2020
9:59 PM
11
  • Online: Submit through Moodle
3. Multi-factor models
11 May 2020
9:59 PM
11
  • Online: Submit through Moodle
4. ANCOVA, interactions & model selection
25 May 2020
9:59 PM
11
  • Online: Submit through Moodle
5. Modelling non-normal data
8 Jun 2020
9:59 PM
11
  • Online: Submit through Moodle
6. Major assignment
15 Jun 2020
9:59 PM
50
  • 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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