MT5761 Applied Statistical Modelling using GLMs

Academic year

2025 to 2026 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

Lecture: 3 – 4pm Mon, Tues, Thurs, Fri + practical Tues 4-5 + tutorial (one of 2-3 or 4-5 Thurs)

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr H Worthington

Dr H Worthington
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

TBD

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This applied statistics module covers the main aspects of linear models (LMs) and generalized linear models (GLMs). In each case the course describes model specification, various options for model selection, model assessment and tools for diagnosing model faults. Common modelling issues such as collinearity and residual correlation are also addressed, and as a consequence of the latter the Generalized Least squares (GLS) method is described. The GLM component has emphasis on models for count data and presence/absence data while GLMs for multinomial (sometimes called choice-based models) are also covered for nominal and ordinal response outcomes. The largest part of the course material is taught inside an environmental impact assessment case study with reality-based research objectives. Political and medical examples are used to illustrate the multinomial models.

Relationship to other modules

Pre-requisites

UNDERGRADUATES MUST HAVE PASSED AT LEAST ONE OF MT4113, MT4527, MT4528, MT4530, MT4531, MT4537, MT4539, MT4606, MT4608 MT4609, MT4614

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT4607 OR TAKE MT5753

Assessment pattern

2-hour Written Examination = 50%, Coursework = 50%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

4 lectures & 1 practical & 1 tutorial (x 5 weeks)

Scheduled learning hours

117

The number of compulsory student:staff contact hours over the period of the module.

Intended learning outcomes

  • Understand Generalised Least Squares (GLS) models and how they relate to Linear regression Models (LMs), and be able to apply GLS models appropriately
  • Understand how Generalised Linear Models (GLMs) extend LMs and GLS models, recognise problems whose solution requires GLMs, and be able to apply GLMs appropriately to address such problems
  • Learn to apply GLMs for continuous response data, count data, binary data, ordinal data and categorical data
  • Be able to perform objective model selection and conduct diagnostics for GLS models and GLMs, to check models' adequacy for the data at hand
  • Be able to interpret fitted GLS model and GLMs output to draw appropriate conclusions about the problem being addressed using these models
  • Become competent in using the statistical programming language R to solve problems using GLS models and GLMs

MT5761 Applied Statistical Modelling using GLMs

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

Lecture: 3 – 4pm Mon, Tues, Thurs, Fri + practical Tues 4-5 + tutorial (one of 2-3 or 4-5 Thurs)

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr H Worthington

Dr H Worthington
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Elham Mirfarah

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This applied statistics module covers the main aspects of linear models (LMs) and generalized linear models (GLMs). In each case the course describes model specification, various options for model selection, model assessment and tools for diagnosing model faults. Common modelling issues such as collinearity and residual correlation are also addressed, and as a consequence of the latter the Generalized Least squares (GLS) method is described. The GLM component has emphasis on models for count data and presence/absence data while GLMs for multinomial (sometimes called choice-based models) are also covered for nominal and ordinal response outcomes. The largest part of the course material is taught inside an environmental impact assessment case study with reality-based research objectives. Political and medical examples are used to illustrate the multinomial models.

Relationship to other modules

Pre-requisites

UNDERGRADUATES MUST HAVE PASSED AT LEAST ONE OF MT4113, MT4527, MT4528, MT4530, MT4531, MT4537, MT4539, MT4606, MT4608 MT4609, MT4614

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT4607 OR TAKE MT5753

Assessment pattern

2-hour Written Examination = 50%, Coursework = 50%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

4 lectures & 1 practical & 1 tutorial (x 5 weeks)

Scheduled learning hours

30

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

117

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand Generalised Least Squares (GLS) models and how they relate to Linear regression Models (LMs), and be able to apply GLS models appropriately
  • Understand how Generalised Linear Models (GLMs) extend LMs and GLS models, recognise problems whose solution requires GLMs, and be able to apply GLMs appropriately to address such problems
  • Learn to apply GLMs for continuous response data, count data, binary data, ordinal data and categorical data
  • Be able to perform objective model selection and conduct diagnostics for GLS models and GLMs, to check models' adequacy for the data at hand
  • Be able to interpret fitted GLS model and GLMs output to draw appropriate conclusions about the problem being addressed using these models
  • Become competent in using the statistical programming language R to solve problems using GLS models and GLMs