37013 Understanding Data: Linear Regression Models for Interpretation and Prediction
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Credit points: 2 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 60117 Understanding Data and Statistical Design
Recommended studies:
Students taking this subject must be familiar with basic statistical concepts such as random variables, sample and population statistics and t and F-tests.
Description
The motivation behind modelling data is to make judgements about the relationship between a response variable and predictor variables. This subject introduces linear regression, a statistical tool used to model continuous response variables and lays the foundation for further study in data modelling. The statistical tool is linear regression, a procedure that allows data to be modelled as lines, curves or surfaces of "best fit". The linear regression models considered involves combinations of continuous and categorical predictor variables. A major focus of the subject is analysis of the adequacy of the fit of the models to the underlying data, including procedures used to assess whether the modelling assumptions have been satisfied.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | formulate relevant hypotheses and design appropriate experiments and data collection plans to test those hypotheses |
|---|---|
| 2. | apply univariate and multivariate statistical data analysis methodology to hypothesis testing and experimentation |
| 3. | implement statistical analysis methodology in R programming language |
| 4. | communicate analysis results and conclusions clearly |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Technically Proficient: Master of Professional Practice graduates research and evaluate information, concepts and theories from new or multiple disciplines. They predict and judge the impact of innovations, solutions and strategies using ethical, social, commercial and environmental frameworks and metrics. (D.1)
- Collaborative and Communicative: Master of Professional Practice graduates communicate in a variety of ways to interpret, develop, sustain and lead discussion, adapting communication to acknowledge and address diversity. They exercise leadership to activate collaboration, build and maintain team cohesion and influence and manage people. (E.1)
Contribution to the development of graduate attributes
This subject contributes to the development of the following FEIT graduate attributes:
D. Technically Proficient - technically knowledgeable and adept in discipline-specific methodologies
E. Collaborative and Communicative - collegial, cooperative, ethical and constructive
This subject contributes to the development of the following Science graduate attributes:
1. Disciplinary knowledge
Knowledge of statistics to demonstrate depth, breadth, application, and interrelationships of relevant discipline areas.
2. Research, inquiry and critical thinking
The ability to frame conjectures and hypotheses using a scientific approach, to test current statistics knowledge through critical evaluation and data analyses, and to solve problems through theoretical work and/or experimental observation.
3. Professional, ethical and social responsibility
A capacity to work ethically and professionally using technical, practical, and collaborative statistics skills within the context of the workplace, and apply these to meet the current and future needs of society.
5. Communication
Effective and professional communication skills for a range of scientific audiences.
Teaching and learning strategies
This subject will be presented in online mode and will run over 4 weeks. Theoretical material will be presented in the lecture and students will work on practical problems during the PC labs using the R programming language. To ensure maximum flexibility for participants working full time, the lectures and PC labs will be pre-recorded in MP4 screencast format for study at a suitable time.
Classes will consist of 4 weekly 2-hour lectures and 4 weekly 1.5-hour PC labs. In addition to the weekly scheduled class time of 3.5 hours, students will require an additional 3.5 hours of independent study and 1 hour spent completing the weekly lab worksheet. A further 8 hours will be required to complete the data analysis assignment. This is roughly one-third of the requirements for a 6 credit point subject.
In each weekly PC lab, students will be provided with a data set and a list of questions. Students will use R to process this data set and answer these questions, submitting their work after the lab. Their submissions will be marked and returned with written feedback prior to the following week’s lab. Students are encouraged to seek extra feedback where that might clarify issues arising from their work.
Content (topics)
The following topics will be covered:
- Simple and multiple linear regression model fitting using the method of least squares.
- Statistical properties of model parameters and hypothesis testing using t and F-tests.
- Assessment of model fit and modelling assumptions.
- Statistical properties of modelling predictions.
All topics will be explored in terms of scientific applications and illustrated with practical examples using R.
Assessment
Assessment task 1: Lab Worksheets
| Intent: | This assessment task contributes to the development of the following FEIT graduate attributes: D. Technically Proficient - technically knowledgeable and adept in discipline-specific methodologies E. Collaborative and Communicative - collegial, cooperative, ethical and constructive
This assessment task contributes to the development of the following Science graduate attributes: |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): D.1 and E.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | Application of appropriate theoretical content, accuracy of analysis and implementation R, clarity of communication of solutions and conclusions. |
Assessment task 2: Data Analysis Assignment
| Intent: | This assessment task contributes to the development of the following FEIT graduate attributes: D. Technically Proficient - technically knowledgeable and adept in discipline-specific methodologies E. Collaborative and Communicative - collegial, cooperative, ethical and constructive
This assessment task contributes to the development of the following Science graduate attributes: |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): D.1 and E.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | Application of appropriate theoretical content, accuracy of analysis and implementation in R, clarity of communication of solutions and conclusions. |
Minimum requirements
In order to pass this subject, students much acheive at least 50% of the total marks available.