| Effective Term: | 2024/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Regression and Multivariate Data Analysis |
| Unit ID: | STATS7101 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (STATS5000) |
| Co-requisite(s): | Nil |
| Exclusion(s): | Nil |
| ASCED: | 010103 |
| Other Change: | |
| Brief description of the Unit |
This course introduces you to two widely used concepts in statistical data analysis: regression analysis and multivariate methods. It is designed as an applied course for individuals to solve real-world statistical problems in multiple disciplines, with emphasis on developing an understanding of the concepts and methodologies such as statistical forecasting, factor analysis and clustering of multi-dimensional data. We have chosen to feature the R programming environment for all analyses and visualisations in this course. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: No |
| Supplementary Assessment:No |
| Supplementary assessment is not available to students who gain a fail in this Unit. |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | | | | | | Intermediate | | | | |  | | | Advanced | | | | | | |
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| Learning Outcomes: |
| Knowledge: |
| K1. | Solve problems using appropriate statistical data analysis techniques. |
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| K2. | Identify statistical limitations of regression and forecasting techniques and determine appropriate mitigation strategies. |
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| K3. | Differentiate the role of statistical decomposition strategies and clustering methods for multivariate data analyses. |
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| Skills: |
| S1. | Analyse statistical data using R software. |
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| S2. | Perform appropriate data assessment procedures to determine the most appropriate data analysis methods for a given problem. |
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| S3. | Communicate results from data analyses using statistical summaries and technical reports. |
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| Application of knowledge and skills: |
| A1. | Construct regression models for real life applications and apply those models to predict future events and conditions. |
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| A2. | Analyse and visualise patterns in data using statistical multivariate techniques. |
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| Unit Content: |
Topics include: Review of basic statistical concepts The R environment for regression. Multiple linear and logistic regressions Time series forecasting MANOVA Linear discriminant analysis Principal component analysis Clustering |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 1. | K1, S1, A1 | Assignment 1 comprises the first three weeks of lecture materials on the topics 'Review of Basic Statistics Concept' and 'Simple Linear Regression'. | Report | 10-20% | | 2. | K1, K2, S1-S3, A1 | Assignment 2 may comprise materials on multiple regressions and model building, as well as on time series forecasting, covered in Lectures 4-7. | Report | 10-20% | | 3. | K1, K3, S1-S3, A2 | Assignment 3 will be based on materials covered in Lectures 8-11 and may compise topics on Linear Discriminant Analysis, Principal Component Analysis and Clustering. | Report | 10-20% | | 4. | K1-K3, S1-S3 and A1-A2 | Summative tasks covering fundamentals of different regression and multivariate analysis methods and their applications. | Test/Exam | 40% - 60% |
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