Each APTS module has associated to it three types of resource: preliminary material, lecture material, and assessment material. Material for each module will be made available below at an appropriate time.

Preliminary material

1. Preliminary material specific to each module

Associated with each APTS module will be some preliminary web-based material. This will be provided by module leaders to help students get up to speed with any prerequisite material. More details can be found in the APTS module specifications.
Please note the following:

• The web-based material will be made available on-line five weeks before the start of the relevant APTS week:
• you can keep track of this using the key dates page
  
  • registered students will also be emailed when new material is made available
  • A number of the modules will involve computer lab sessions, and a working knowledge of the use of R will be assumed for these. See the note below about preparation in R.

 You are expected to spend up to one week per module working through the preliminary material
- please do this, since otherwise you are likely to be unprepared for the module lectures

2. R Programming for APTS Students

Several of the modules --- in particular the modules Statistical Computing, Statistical Modelling and Computer Intensive Statistics --- will make use of the R statistical computing environment. Students who are either unfamiliar with or not confident in the use of R should work through R Programming for APTS Students, a web course in R kindly provided by University of Oxford, Department of Statistics. Reaching at least this level of familiarity with R is essential preparation for these modules.

Lecture material

Each module leader will provide some material (such as slides from lectures, or a summary of the course) following the APTS week in which their module is delivered. This should provide pointers to sources and resources for future work.

Assessment material

Following each APTS week, you may be asked to undertake a form of light assessment. This will be provided by the APTS module leader, but will be marked by somebody in your department. The assessment tasks provide the opportunity for your department to fit APTS training into its own framework for monitoring student progress, as well as providing useful feedback for future development of APTS modules. Hopefully the assessment should not take too long, and will enhance both your interest in the area of the module and your ability to find out more for yourself.

Generally, assessment materials will be made available within 7 days of the conclusion of an APTS week; attendees will be notified by email if there is a longer delay for any reason as well as when the materials are made available (a single email will be sent when all of the materials for a given week are present for simplicity).

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Module Resources: Download

The various materials mentioned above will appear here during the APTS year.

Module leaders are asked to provide module material in an accessible format. If you need any of the material below in alternative forms to those provided in the table below for accessibility then please let us know at apts.admin@warwick.ac.uk .

APTS week	Module title	Module leader	Preliminary material	Lecture material	Assessment material
1 Warwick	Statistical Computing	Simon Wood	Preliminary notes (PDF) R Solutions	Notes (PDF) Labs 1 (PDF) Labs 1 Solutions (PDF) Labs 2 (PDF) Labs 2 Solutions (PDF) R code	Assessment (PDF)
1 Warwick	Statistical Inference	Michael Goldstein	Preliminary Notes (html)* *22/23 relevant for 23/24	Ideas of Inference (PDF) The Likelihood Principle (PDF) Statistical Decision Theory (PDF) Likelihood Estimators (PDF) Likelihood and Inference (PDF) Perspectives (PDF)	Assessment (PDF)
2 Nottingham	Applied Stochastic Processes	Nicholas Georgiou and Hugo Lo	Preliminary Notes (PDF) Preliminary Notes (HTML)	Lecture notes and Exercises	Assessment (PDF)Link opens in a new window
2 Nottingham	Statistical Modelling	Ioannis Kosmidis	Preliminary Notes (HTML) PDF	Notes + Labs (with solutions) (PDF) (HTML) Slides (all HTML): Introduction Model Selection Beyond GLMs Nonlinear models Latent variables	Assessment (PDF) (HTML)
3 Durham	Computer Intensive Statistics	Richard Everitt	Preliminary Notes (webpage) PDFLink opens in a new window	Notes (PDF)Link opens in a new window SlidesLink opens in a new window Practical 1Link opens in a new window Practical 1 sols Practical 2Link opens in a new window	Assessment (PDF)
3 Durham	High-dimensional Statistics	Po-Ling Loh	Preliminary Notes (PDF)Link opens in a new window	Notes (PDF)	Assessment (PDF)
4 Oxford	Causal Inference	Vanessa Didelez and Robin Evans	Preliminary reading	Notes (html) Slides	Assessment (PDF)
4 Oxford	Statistical Machine Learning	Louis Aslett	Preliminary Notes (webpage) Setup Information	Notes (html)	Assessment (HTML)

This list of software useful for Statistics PhDs might also be of interest.

Resources for the academic year 2022-23 have been archived here.

 

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