ST208 Mathematical Methods
Please note that all lectures for Statistics modules taught in the 202223 academic year will be delivered on campus, and that the information below relates only to the hybrid teaching methods utilised in 202122 as a response to Coronavirus. We will update the Additional Information (linked on the right side of this page) prior to the start of the 2022/23 academic year.
Throughout the 202122 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and oncampus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information. You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.
All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at http://go.warwick.ac.uk/STassessmenthandbook
ST20812 Mathematical Methods
Introductory description
This module runs in Term 1 and is core for students with their home department in Statistics and optional for students studying discrete mathematics. It may be possible for nonfinalists from other courses to take this module as an unusual option subject to permission from the module leader and the home department.
This module has prerequisites: MA106 Linear Algebra and MA137 Mathematical Analysis or equivalent.
Module aims
This is a course of techniques which are in everyday use in probability and statistics, and which are essential to a proper understanding of any second or third year course in these subjects. It will provide the mathematical background for optimization, convergence, regression and best approximation, and develop mathematical thinking.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
 Preliminaries: subsets of R^2, inverse images; partial derivatives; determinants.
 Multiple integration: calculation of areas and volumes; Fubini's theorem; change of variable; limits of integration; links with probability.
 Real Symmetric matrices, quadratic forms, positive definiteness; orthonormal basis of eigenvectors; diagonalisation; covariance matrices.
 Differentiation in R^n: classification of critical points; types of extrema; constrained optimisation.
 Linear algebra: vector spaces; inner products; orthogonal and orthonormal bases; GramSchmidt orthogonalisation; isometries; projections; spectral decomposition.
 Metric spaces: open and closed sets; convergence; continuity; compactness.
Learning outcomes
By the end of the module, students should be able to:
 Compute areas and volumes in two and three dimensions; manipulate and solve multiple integrals using Fubini's theorem; compute expectations of functions of random variables.
 Determine whether a real matrix is positive/negative definite / semidefinite; use spectral theory to diagonalise a matrix, apply spectral ideas to recognise the structure of a covariance matrix.
 Find and classify the critical points of a multivariate function; solve constrained optimisation problems by using Lagrange multipliers.
 Describe the key properties of an inner product space; orthogonalise a set of vectors; determine when a mapping represents a projection and compute the projection onto a given subspace.
 Explain the key concepts associated with metric spaces: convergence, continuity, compactness; determine whether a set is open or closed.
Indicative reading list
View reading list on Talis Aspire
Subject specific skills
TBC
Transferable skills
TBC
Study time
Type  Required  Optional 

Lectures  30 sessions of 1 hour (25%)  2 sessions of 1 hour 
Tutorials  4 sessions of 1 hour (3%)  
Private study  62 hours (51%)  
Assessment  26 hours (21%)  
Total  122 hours 
Private study description
Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D3
Weighting  Study time  

Multiple Choice Quiz 1  3%  4 hours 
A multiple choice quiz which will take place during the term that the module is delivered. 

Multiple Choice Quiz 2  3%  4 hours 
A multiple choice quiz which will take place during the term that the module is delivered. 

Multiple Choice Quiz 3  4%  4 hours 
A multiple choice quiz which will take place during the term that the module is delivered. 

Written assignment  10%  12 hours 
The assignment will contain a number of questions for which solutions and / or written responses will be required. The preparation and completion time noted below refers to the amount of time in hours that a wellprepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 

Oncampus Examination  80%  2 hours 
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms  Moodle

Assessment group R1
Weighting  Study time  

Inperson Examination  Resit  100%  
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.

Feedback on assessment
Answers to problems sets will be marked and returned to you in a tutorial or seminar taking place the following week when you will have the opportunity to discuss it.
Solutions and cohort level feedback will be provided for the examination.
Courses
This module is Core for:

USTAG302 Undergraduate Data Science
 Year 2 of G302 Data Science
 Year 2 of G302 Data Science
 Year 2 of USTAG304 Undergraduate Data Science (MSci)
 Year 2 of USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
 Year 2 of USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 2 of GG14 Mathematics and Statistics
 Year 2 of GG14 Mathematics and Statistics

USTAY602 Undergraduate Mathematics,Operational Research,Statistics and Economics
 Year 2 of Y602 Mathematics,Operational Research,Stats,Economics
 Year 2 of Y602 Mathematics,Operational Research,Stats,Economics
This module is Option list B for:

UCSAG4G1 Undergraduate Discrete Mathematics
 Year 2 of G4G1 Discrete Mathematics
 Year 2 of G4G1 Discrete Mathematics
 Year 2 of UCSAG4G3 Undergraduate Discrete Mathematics