Module Specification
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Linear Statistical Models
2. Module Code MATH363
3. Year Session 2023-24
4. Originating Department Mathematical Sciences
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr K Zychaluk Mathematical Sciences Kamila.Zychaluk@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Dr L Yuan Mathematical Sciences Linglong.Yuan@liverpool.ac.uk
Dr SA Fairfax Mathematical Sciences Simon.Fairfax@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 24

         24

 48
17.

Private Study

 102
18.

TOTAL HOURS

 150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

MATH101 Calculus I; MATH103 Introduction to Linear Algebra; MATH102 CALCULUS II; MATH163 Introduction to Statistics using R; MATH253 Statistics and Probability I
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:
25. Programme(s) (including Year of Study) to which this module is available on a required basis:
26. Programme(s) (including Year of Study) to which this module is available on an optional basis:
27. Aims
 

- To understand how regression methods for continuous data extend to include multiple continuous and categorical predictors, and categorical response variables.

- To provide an understanding of how this class of models forms the basis for the analysis of experimental and also observational studies.

- To understand generalized linear models.

- To develop skills in using an appropriate statistical software package.
 
28. Learning Outcomes
 

(LO1) Be able to understand the rationale and assumptions of linear regression and analysis of variance.
 

(LO2) Be able to understand the rationale and assumptions of generalized linear models.
 

(LO3) Be able to recognise the correct analysis for a given experiment.
 

(LO4) Be able to carry out and interpret linear regressions and analyses of variance, and derive appropriate theoretical results.
 

(LO5) Be able to carry out and interpret analyses involving generalised linear models and derive appropriate theoretical results.
 

(LO6) Be able to perform linear regression, analysis of variance and generalised linear model analysis using an appropriate statistical software package.
 

(LO7) Be able to perform additional research of statistical methods beyond the material covered in videos or research history of maths related to this module.
 
29. Teaching and Learning Strategies
 

Material is provided in advance of classes for students to study asynchronously. The contact hours consist of 2 hours of active learning sessions (focused on theory) and 2 hours of supported study sessions in computer labs (mainly focused on group project).
 
30. Syllabus
   

- General Linear Models.

- Simple linear regression; one-way analysis of variance; estimation and inference; two and three-way analysis of variance; more complex designs.

- Generalized Linear Models: Foundations; exponential family of distributions; estimation and inference; binary response variables; normal response variables; contingency tables and log-linear models; other applications.
 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment
32. EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
   
33. CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  Group Project Reassessment: individual task. 0 60
  class test 1 60 20
  class test 2 60 20