33116 Design, Data, and Decisions
Warning: The information on this page is indicative. The subject outline for a
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 2025 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 |
|---|---|
| 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:
- Demonstrate theoretical and technical knowledge of broad science concepts and explain specialised disciplinary knowledge. (1.1)
- Evaluate scientific evidence and apply effective experimental design and/or mathematical reasoning, analysis, and critical thinking to apply science and/or mathematic methodologies to real world problems. (2.1)
- Work autonomously or in teams to address workplace or community problems utilising best scientific practice, with consideration to safety requirements and ethical guidelines. (3.1)
- Present and communicate complex ideas and justifications using appropriate communication approaches from a variety of methods (oral, written, visual) to communicate with discipline experts, scientists, industry, and the general public. (5.1)
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 professional responsibility to correctly apply statistical design and analysis tools is emphasised throughout the subject. The use of specialist statistical software to implement straight-forward analyses of problems is assessed in the weekly problems, while correct interpretation is assessed in the weekly problems and the final exam.
Graduate Attribute 5. Communication
Presentation of written solutions to problems using appropriate professional language is emphasised in the in-class assessments and the final exam.
Teaching and learning strategies
This subject makes use of CANVAS to provided guided learning materials throughout the subject. The site is organised into topics and 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. Each week, the formal activities will be a 2 hour lecture-style workshop and a 2 hour computer lab. The lecture-style workshop is available on-campus; and recordings will be loaded to CANVAS for each session. These sessions will include the presentation and discussion of key concepts as well as their application in worked examples. The 2 hour computer lab will be guided problem solving making use of Excel as a computational tool for statistical analysis on real data sets. There will be regular online exercises as per the Program for which feedback on student attempts is immediate. This provides formative assessment throughout the subject to guide and aid learning.
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 3. Professional, ethical and social responsibility 5. Communication |
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| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1, 3.1 and 5.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 60% |
| Criteria: | Students will be assessed on:
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Assessment task 2: 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, 2.1 and 5.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: | Students will be assessed on:
|
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 accessed as an e-book from the library. The textbook is not necessary to study this subject, but some students like to do additional reading around the topics. Note that we are using Excel as our computing tool and not the software referred to in the textbook.