MA335-5-SP-CO:
Modelling experimental and observational data

The details
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)

Key module for

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)

Module description

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.

Module aims

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.

Module learning outcomes

By the end of this module, students will be expected to:



  1. Have a systematic understanding of study designs that produce observational and experimental data and interpretations on the results from observational or experimental data.

  2. Understand comprehensively common generalized linear regression models, including multiple linear regression, logistic regression, ANOVA.

  3. Be familiar to the well-established principles for the assessment of model fitting, including model selection, classification accuracy and cross validation.

  4. Have an ability to deal with common obstacles for ordinary statistical model fitting, including multicollinearity and missing values.

  5. 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.

Module information

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




  • Logistic regression




    • Model assumptions

    • Parameter estimation and interpretation

    • Missing values and imputation




  • One-, two-way ANOVA

  • Contingency table analysis




    • Chi-square test




  • Classification




    • Training and test datasets, leave-one-out and cross-validation

    • Precision, recall, accuracy, ROC curves, confusion matrix

    • Logistic regression for classification




  • Cluster analysis




    • Motivation and problem statement

Learning and teaching methods

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.

Bibliography

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

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Na You, email: na.you@essex.ac.uk.
Dr Na You
maths@essex.ac.uk

 

Availability
Yes
Yes
Yes

External examiner

Dr Yinghui Wei
University of Plymouth
Resources
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.

 

Further information

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