33116 Statistical Design and Analysis
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particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2021 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Anti-requisite(s): 35151 Introduction to Statistics AND 37151 Introduction to Data Analysis
Description
This subject focuses on data analysis. Students learn how to collect and analyse data, and how to draw valid conclusions from the data. The subject begins with a discussion of how to sample from a population, and how to describe the data collected. This is followed by a discussion of how to form and test hypotheses about the population using the data collected from the sample.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | select and produce appropriate graphical, tabular and numerical summaries of variables in a data set, and summarise such information verbally |
|---|---|
| 2. | distinguish between observational and experimental studies, and draw conclusions appropriate to each type of study |
| 3. | determine whether an interval or a test is more appropriate for addressing a particular question, and apply these concepts to answer questions using real data involving a single variable |
| 4. | choose the appropriate type of inference to answer questions using real data involving several variables |
| 5. | analyse, assess and critique statistical arguments of the type found in the popular press and in scholarly publications |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Apply: Demonstrate knowledge of discipline relevant topics (1.1)
- Analyse: Explain the basic applications of discipline knowledge within context (1.2)
- Synthesise: Integrate discipline knowledge and apply it to key processes (1.3)
- Apply: Investigate effective application of experimental design and sampling for hypothesis testing (2.1)
- Analyse: Develop critical thinking skills including critiquing, interpreting and questioning scientific evidence (2.2)
- Synthesise: Apply the scientific method to real world problems and evaluate experimental outcomes (2.3)
- Apply: Participate in team based data collection, recording and management, with an understanding of ethical limitations (3.1)
- Analyse: Use an appropriate range of techniques to investigate data and test hypotheses within the context of the workplace (3.2)
- Apply: Demonstrate interpersonal communication skills with peer and professional colleagues (5.1)
- Analyse: Identify and practice appropriate communication approaches using a variety of methods and media (5.2)
- Synthesise: Create effective communication protocols to convey appropriate scientific information to a range of audiences (5.3)
Contribution to the development of graduate attributes
This subject provides students with the skills and understanding to apply appropriate statistical techniques and methods in solving problems in a variety of professional fields. It also helps students appreciate the need for critical and independent evaluation of statistical problems and the effective communication of the results of the statistical analysis. Thus this subject is contributing to the following graduate attributes.
Graduate Attribute 1 - Disciplinary knowledge
The lectures, computer laboratory classes, and exercises provide an opportunity for students to develop knowledge and skills, and apply both to a variety of problems.?
Graduate Attribute 2 - Research, inquiry and critical thinking
The lectures discuss various ways to address a particular question, and students get practice at determining the correct approach themselves in the lectures and laboratory classes.
Graduate Attribute 3 - Professional, ethical and social responsibility
The ability to work effectively and responsibly in a group is emphasised in the group work components of the in-class assessments. The use of specialist statistical software to implement straight-forward analyses of problems is assessed in the weekly problems.
Graduate Attribute 5. Communication
Presentation of written and oral solutions to problems using appropriate professional language is emphasised in the in-class assessments.
Teaching and learning strategies
The presentation of this subject consists of a total of 2 hours of lectures and a 2 hour computer laboratory class each week. These sessions will be available as on-line learning modules during Autumn and Summer offerings when Covid-19 protocols are in effect. The material will incorporate a range of teaching and learning strategies including the presentation of worked examples, critiquing readings, collaborative group work (remotely) and individual problem solving. Students are to prepare each topic by reviewing any relevant material available on Canvas for each session and complete any associated tasks before attending the corresponding session. Students will use specialised staistical software in the laboratory classes to analyse real data sets, both in groups and individually. There will be regular online exercises as per the Program for which feedback on student attempts is immediate.
Teaching and Learning strategies relevant for Block Mode are noted in Additional Information.
Content (topics)
The major topics covered in this subject are:
- Data collection - methods and limitations
- Data analysis
- Statistical estimation and inference
- Critical thinking about data-based claims
Assessment
Assessment task 1: Weekly Online Exercises
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge 2. Research, inquiry and critical thinking |
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| Objective(s): | This assessment task addresses subject learning objective(s): 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 2.2 and 2.3 |
| Groupwork: | Individual |
| Weight: | 25% |
| Criteria: | Students will be assessed on:
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Assessment task 2: Critiquing Tasks
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge 2. Research, inquiry and critical thinking 3. Professional, ethical and social responsibility 5. Communication |
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| Objective(s): | This assessment task addresses subject learning objective(s): 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 2.2, 3.1 and 5.1 |
| Groupwork: | Group, group and individually assessed |
| Weight: | 15% |
| Criteria: | Students will be assessed on:
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Assessment task 3: Presentation Tasks
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 1.3, 2.3, 3.1, 3.2, 5.1 and 5.3 |
| Groupwork: | Group, group assessed |
| Weight: | 20% |
| Criteria: | Students will be assessed on:
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Assessment task 4: Final Examination
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 1.2, 1.3, 2.1, 2.3 and 5.2 |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: | Students will be assessed on:
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Minimum requirements
To pass the subject, a student must achieve a final result of 50% or more. The final result is simply the sum of the marks gained in each piece of assessment.
Recommended texts
Textbook: Statistics: The Art and Science of Learning from Data, Global Edition (4e) By Agresti, Alan, Pearson, 2017 This can be ordered as an eBook directly from Pearson at the following link. pearson.com.au/9781292164830 Students will use Pearson MyStatLab for quizzes and other online resources associated with this textbook, but these will be available even if students do not buy the book.