APPLIED BIOSTATISTICS

STA2ABS

2017

Credit points: 15

Subject outline

Building on the understanding of applied statistical methods developed in first year statistics subjects, STA2ABS provides an understanding of these methods at an intermediate level. In terms of content, STA2ABS is similar to STA2AMS, but the subject places a special emphasis on biological applications. There are specific questions in the assignments, project, test and examination that reflect such an emphasis. This subject does not require a knowledge of calculus. An introduction to the open source statistical computing package R is included.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAgus Salim

Available to Study Abroad StudentsYes

Subject year levelYear Level 2 - UG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites STA1PSY or STA1LS or STA1SS or ECO1ISB

Co-requisitesN/A

Incompatible subjects STA2AS, STA2MS, STA2RSP, STA2AMS

Equivalent subjectsN/A

Special conditionsN/A

Graduate capabilities & intended learning outcomes

01. Apply appropriate statistical and probabilistic methods for data analysis.

Activities:
Discussed and demonstrated in lectures and lecture/workshops. Related problems solved by students in practice classes. Assignment questions, with feedback.
Related graduate capabilities and elements:
Writing (Writing)
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)

02. Discuss the importance of "thinking ahead" when planning and designing experiments.

Activities:
Discussed and demonstrated in lectures and lecture/workshops. Related problems solved by students in practice classes. Assignment questions, with feedback.
Related graduate capabilities and elements:
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)
Critical Thinking (Critical Thinking)
Inquiry/ Research (Inquiry/ Research)

03. Execute statistical software functionality for data analysis and interpret the output accurately and meaningfully.

Activities:
Some discussion in lectures and lecture/workshops. Related problems solved by students in computer laboratory classes. Computer laboratory project, with guidance and feedback.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)

04. Assess the effectiveness of statistical methods using simulation.

Activities:
Some discussion in lectures and lecture/workshops. Related problems solved by students in computer laboratory classes. Computer laboratory project, with guidance and feedback.
Related graduate capabilities and elements:
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)

05. Explain the codes of conduct that govern professional competence and integrity in the field of statistics.

Activities:
Discussed and demonstrated in lectures and lecture/workshops.
Related graduate capabilities and elements:
Ethical Awareness (Ethical Awareness)

06. Formulate hypothesis testing related to biological problems; draw and explain the conclusions that follow from a rigorous and systematic analysis.

Activities:
Discussed and demonstrated in lectures and lecture/workshops. Related problems solved by students in practice classes. Assignment questions, with feedback.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

Subject options

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Start date between: and    Key dates

Melbourne, 2017, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAgus Salim

Class requirements

Computer Laboratory Week: 11 - 22
One 1.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
One 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Lecture/Workshop Week: 10 - 22
One 1.0 hours lecture/workshop per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Practical Week: 11 - 22
One 1.0 hours practical per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
2.5 hour exam55 01, 02, 05, 06
In-class computer test (equiv to 700 words)15 03, 04
Computing project (equivalent to 500 words)10 03, 04
Four written assignments (equivalent to 800 words in total)20 01, 02, 06