Not currently offered
Credit points: 15
Students will learn to design and analyse experiments in the life sciences and agriculture. The topics covered in this subject include a brief review of non-parametric methods; randomisation, blocking and randomised block designs; one-way and two-way layouts; multiple comparison procedures; fixed and random effects; mixed models; multiple linear regression; analysis of covariance; factorial designs; fractional factorial designs; and an introduction to cluster analysis. This subject makes use of the freely available software package R. STA3BS is co-taught with STA2BS. For STA3BS there is greater emphasis on inquiry with an expectation that students independently analyse some of the subject data.
SchoolSchool Engineering&Mathematical Sciences
Subject Co-ordinatorAndriy Olenko
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Prerequisites STA2ABS or STA2AMS or STA2MD or STM2PM
Incompatible subjects AGR41EXP, AGR4AED, STA2BS
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Biostatistics with R||Recommended||Shahbaba, Babak 2012||SPRINGER, AVAILABLE ONLINE IN LATROBE EBL EBOOK LIBRARY|
|Readings||Introduction to Linear Regression Analysis||Recommended||Montgomery, DC, Peck, EA and Vining, G 2006||WILEY, 4TH EDITION. AVAILABLE ONLINE IN LATROBE EBL EBOOK LIBRARY|
Graduate capabilities & intended learning outcomes
01. Present clear, well structured and rigorous proofs of important fundamental linear model results. This includes appropriate use of statistical and mathematical vocabulary and notation.
- Weekly problem classes involve theoretical derivations of results introduced in lectures. 10 almost weekly assignments consist mainly of theoretical derivations. The second hour of the problem class allows for students to work through the assignment questions with guidance from the lecturer and fellow students.
02. Formulate appropriate hypotheses and experimental designs.
- Formulation of hypotheses is discussed and modelled via example in lectures. Experimental design concepts are discussed in lectures and weekly problem classes involve derivation of key theoretical and applied components to design of experiments. Assignments earlier in the semester largely involve hypothesis testing. Assignments later in the subject involve a mixture of hypothesis testing and experimental design. The second hour of the problem class allows for students to work through the assignment questions with guidance from the lecturer and fellow students.
03. Utilize randomization and blocking appropriately in the design of statistical experiments.
- Randomization is a key component to much of the theory throughout the entire subject and is modelled/discussed in lectures and implemented by the students in weekly problems under guidance from the lecturer. Similarly, blocking is considered in weeks 3-6. Assignments consist mainly of theoretical derivations. The second hour of the problem class allows for students to work through the assignment questions with guidance from the lecturer and fellow students.
04. Construct statistical experiments using factorial and fractional factorial designs with an emphasis on the construction of simple estimators of effects associated with two-level factors.
- Introduced in lectures in weeks 10, 11 and 12. Key applications of theoretical results are shown during the problem classes of these weeks. Assignments 9 and 10 involve the student designing/recognising factorial and fractional factorial designs. The second hour of the problem class allows for students to work through the assignment questions with guidance from the lecturer and fellow students.
05. Research, model and analyse data using known underlying factors.
- One question on each of 5 assignments is different from the corresponding STA2BS assignments. It provides little guidance and therefore requires students to research possible solutions and methods to analyse new data. Two subproblems in the final STA2BS exam are replaced by specific STA3BS problems that require deeper understanding of the underlying statistical principles.
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