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Regression and Multivariate Data Analysis
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STATS7101 - Regression and Multivariate Data Analysis
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20250600
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3.1
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2026-09-10
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2026-09-10
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Engineering
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Institute of Innovation, Science & Sustainability
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Engineering
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Postgraduate - Coursework
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15
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.125
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010103
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Statistics
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010103 - Statistics
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<a href="http://www.federation.edu.au/locate/unit/stats7101">View unit fees</a>
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Graded (e.g. HD, D, C, etc)
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9
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Masters Degree
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<p>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.</p>
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<p>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</p>
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<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
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2027
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2026
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STATS7101 - Regression and Multivariate Data Analysis
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STATS7101 - Regression and Multivariate Data Analysis
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Overview
Key information
Requisites
Learning outcomes
Assessment
Parent Region
Overview
Unit description
<p>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.</p>
Unit content
<p>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</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
010103 - Statistics
Offering:
<p><strong>Semester 1</strong><p>Ballarat - Mt Helen (On Campus)</p><p>On-line Learning</p></p> <p><strong>Semester 2</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/stats7101">View unit fees</a>
Requisites
Prerequisite
<p>must have completed "<a href="/ords/r/curriculum/catalogue/course?p6_code=STATS5000" target="_blank">STATS5000</a>"</p>
Corequisite
None
Exclusion
None
Learning outcomes
Learning outcomes
<p>On successful completion of this unit, students are expected to gain the following [K]nowledge, [S]kills and [A]pplication of knowledge & skills</p>
Learning Outcomes
Ref
Outcome
K1
<p>Solve problems using appropriate statistical data analysis techniques.</p>
K2
<p>Identify statistical limitations of regression and forecasting techniques and determine appropriate mitigation strategies.</p>
K3
<p>Differentiate the role of statistical decomposition strategies and clustering methods for multivariate data analyses.</p>
S1
<p>Analyse statistical data using R software.</p>
S2
<p>Perform appropriate data assessment procedures to determine the most appropriate data analysis methods for a given problem.</p>
S3
<p>Communicate results from data analyses using statistical summaries and technical reports.</p>
A1
<p>Construct regression models for real life applications and apply those models to predict future events and conditions.</p>
A2
<p>Analyse and visualise patterns in data using statistical multivariate techniques.</p>
Assessment
Learning Tasks and Assessment
Ref
Learning task
Assessment type
Weighting
Learning outcomes assessed
1
<p>Assignment 1 comprises the first three weeks of lecture materials on the topics 'Review of Basic Statistics Concept' and 'Simple Linear Regression'.</p>
<p>Report</p>
<p>10-20%</p>
K1, S1, A1
2
<p>Assignment 2 may comprise materials on multiple regressions and model building, as well as on time series forecasting, covered in Lectures 4-7.</p>
<p>Report</p>
<p>10-20%</p>
K1, K2, S1, S2, S3, A1
3
<p>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.</p>
<p>Report</p>
<p>10-20%</p>
K1, K3, S1, S2, S3, A2
4
<p>Summative tasks covering fundamentals of different regression and multivariate analysis methods and their applications.</p>
<p>Test/Exam</p>
<p>40% - 60%</p>
K1, K2, K3, S1, S2, S3, A1, A2
Supplementary assessment available:
Yes
Supplementary assessment information:
<p>Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment.</p>
Key information
Institute / school:
Institute of Innovation, Science & Sustainability
Discipline:
Engineering
Study level:
Postgraduate - Coursework
Credit points:
15
EFTSL:
0.125
Field of education:
010103 - Statistics
Offering:
<p><strong>Semester 1</strong><p>Ballarat - Mt Helen (On Campus)</p><p>On-line Learning</p></p> <p><strong>Semester 2</strong><p>Ballarat - Mt Helen (On Campus)</p><p>Melbourne City Campus (On Campus)</p><p>On-line Learning</p></p>
Grade scheme:
Graded (e.g. HD, D, C, etc)
Placement component:
No
Fee information:
<a href="http://www.federation.edu.au/locate/unit/stats7101">View unit fees</a>
Additional information
Add Info Static Text
Internal reference: STATS7101 - U3.1