MA717-7-AU-CO:
Applied Regression and Experimental Data Analysis
2024/25
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
15
24 September 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G30412 Data Science,
MSC G304PP Data Science with Professional Placement,
DIP G30009 Statistics,
MSC G30012 Statistics,
MPHDG30048 Statistics,
PHD G30048 Statistics,
MPHDG30448 Data Science,
PHD G30448 Data Science,
MSCIG199 Mathematics and Data Science
This module is concerned with the application of regression models to the analysis of data.
The underlying assumptions are discussed and general results are obtained using matrices. The standard approach to the analysis of normally distributed data using ANOVA is introduced. Methods for the design and analysis of efficient experiments are introduced. The general methodology is extended to nonlinear regression, generalised regression and the analysis of multidimensional contingency tables.
The aims of this module are:
- To build on the essential foundations of regression models by studying important topics of statistical modelling.
- To enable students to engage with an in-depth study of the main methods to analyse experimental data.
By the end of this module, students will be expected to:
- Comprehensively understand how to represent linear regression in matrix form.
- Have a critical awareness and ability to check model assumptions and identify influential observations.
- Have a critical awareness of the advanced experimental design and the ability to construct factorial experiments in blocks.
- Have a systematic understanding of basic nonlinear regression and its applications in real-life examples.
- Have developed practical skills to carry out generalised linear regression analysis and the ability to analyse cross-tabulated data using log-linear models.
- Be able to analyse linear models using R.
Indicative syllabus
Multiple regression
- Multiple regression (least square) in matrix formulation and sum of squares.
- Model assumptions and fitting diagnostics – residual plots, leverage, multicollinearity, serial correlation.
- Model selection and regulations. Cp plots and BIC.
- Curvilinear (polynomial) regression.
- Weighted least squares.
- ANOVA for model fitting.
Designed experiments:
- Completely randomised experiment. One factor ANOVA and ANCOVA
- Randomised blocks and Latin square with related ANOVA.
- Balanced incomplete block design with related ANOVA.
- Factorial experiments design with related ANOVA.
Non-linear models:
- Non-linear models and linearisation.
- Generalised Linear Regression (Logistic regression, Poisson regression, link function, exponential families, and log-linear model for contingency table).
This module will be delivered via:
- One 2-hour lecture per week.
- One 1-hour class per fortnight.
- One 1-hour lab per fortnight.
There will also be 5 hours of lectures to recap basic statistics and simple linear regression in the first week There will also 3 hours of revision lectures for the summer exam.
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Faraway, J.J. (2015)
Linear models with R. Second edition. Boca Raton, FL: CRC Press. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=1640577.
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Kabacoff, R. (2022)
R in action: data analysis and graphics with R and Tidyverse. Third edition. Shelter Island, NY: Manning Publications Co. Available at:
https://learning.oreilly.com/library/view/r-in-action/9781617296055/?sso_link=yes&sso_link_from=university-of-essex.
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James, G.
et al. (2021)
An introduction to statistical learning: with applications in R. Second edition. New York, NY: Springer. Available at:
https://link-springer-com.uniessexlib.idm.oclc.org/book/10.1007/978-1-0716-1418-1.
-
-
Dobson, A.J. and Barnett, A.G. (2018)
An introduction to generalized linear models. Fourth edition. Boca Raton: CRC Press. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5488477.
-
Sen, A. and Srivastava, M. (1994)
Regression Analysis?: theory, methods, and applications. Berlin: Springer. Available at:
https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=6595922.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assignment |
13/12/2024 |
|
| Exam |
Main exam: In-Person, Open Book (Restricted), 180 minutes during Summer (Main Period)
|
| Exam |
Reassessment Main exam: In-Person, Open Book (Restricted), 180 minutes during September (Reassessment Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Wenxing Guo, email: wg22745@essex.ac.uk.
Dr Oludare Ariyo; Dr Wenxing Guo
maths@essex.ac.uk
Yes
No
Yes
Dr Murray Pollock
Newcastle University
Director of Statistics / Senior Lecturer
Available via Moodle
Of 26 hours, 26 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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