MA335-5-SP-CO:
Modelling experimental and observational data
2024/25
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 13 January 2025
Friday 21 March 2025
15
23 May 2024
Requisites for this module
(none)
(none)
(none)
(none)
(none)
BA Q120 Linguistics with Data Science,
BA Q121 Linguistics with Data Science (Including Foundation Year),
BA Q122 Linguistics with Data Science (Including Placement Year),
BA Q123 Linguistics with Data Science (Including Year Abroad),
BSC Q120 Computational Linguistics,
BSC Q121 Computational Linguistics (Including Foundation Year),
BSC Q122 Computational Linguistics (Including Placement Year),
BSC Q123 Computational Linguistics (Including Year Abroad)
This module will introduce the principles for the application of linear modelling methodologies for the analysis of experimental and observational data.
The first strand of the module will study the assumptions of the general linear model. Collinearity, influential data, assessing the fitted model and model selection techniques will be discussed. The second strand will introduce statistical methods for the efficient analysis of experiments when the data are normally distributed, for example one-way ANOVA. The methodology will be extended to logistic regression. The third strand of the module will study various multivariate methods for the analysis of large and high-dimensional data sets.
The aims of this module are:
- To provide the fundamental understanding of the underlying statistical methodologies.
- To provide capabilities of applying these methodologies to real experimental and observational data.
- To provide knowledge of conducting a robust statistical analysing of the data.
- To provide capabilities of interpreting the results effectively.
By the end of this module, students will be expected to:
- Have a systematic understanding of study designs that produce observational and experimental data and interpretations on the results from observational or experimental data.
- Understand comprehensively common generalized linear regression models, including multiple linear regression, logistic regression, ANOVA.
- Be familiar to the well-established principles for the assessment of model fitting, including model selection, classification accuracy and cross validation.
- Have an ability to deal with common obstacles for ordinary statistical model fitting, including multicollinearity and missing values.
- Have an ability to use R for the application of the generalised linear regression and machine learning techniques for the experimental and observational data analysis.
Syllabus
- Observational versus experimental data
- Confounding
- Randomized clinical trials
- Real-world data and causality
- Multiple linear regression
- Parameter estimation and interpretation
- Model assessment and model selection
- Dimension reduction and variable selection
- Model assumptions
- Parameter estimation and interpretation
- Missing values and imputation
- One-, two-way ANOVA
- Contingency table analysis
- Training and test datasets, leave-one-out and cross-validation
- Precision, recall, accuracy, ROC curves, confusion matrix
- Logistic regression for classification
- Motivation and problem statement
Teaching in the School will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.
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 |
Lab Test |
|
65% |
Coursework |
Written test |
|
35% |
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 Na You, email: na.you@essex.ac.uk.
Dr Na You
maths@essex.ac.uk
Yes
Yes
Yes
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 28 hours, 28 (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.
Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can
be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements,
industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist
of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules.
The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.