2023/24 Taught Postgraduate Module Catalogue

BLGY5121M Advanced Statistics

15 creditsClass Size: 50

Module manager: Dr Anna Riach
Email: A.Riach@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

Pre-requisite qualifications

Knowledge of basic statistical concepts gained from a statistics, skills or research methods module previously studied at university level.

Module replaces

BLGY5112M Advanced Statistics, 10c, S

This module is not approved as an Elective

Module summary

This module gives people an opportunity to experience how coding in the R language is used to perform a broad range of statistical analyses. The aim is to increase students’ confidence so they can envisage what analyses are possible for a given dataset similar to those encountered during research projects.

Objectives

Students will ‘get hands-on’ with R software through synchronous coding and self-paced tasks. Understanding of the R language will be gained through explanations of example code, experiencing how to ‘de-bug’ code and tasks that require adapting code from the internet. Students will be helped to understand how complex statistical analyses are performed and to take meaning from the results.

Learning outcomes
1. Choose a suitable statistical analysis when presented with new datasets and research questions.

2. Write reproducible code in R to import, clean and analyse data, including checking any assumptions.

3. Write a clear and succinct account of how the data was analysed and an accurate interpretation of the R output in relation to the research question.

4. Write R code to produce a visualisation of data that communicates the results appropriately.

Skills outcomes
‘Hands-on’ R practicals will introduce or enhance skills needed to adapt and ‘de-bug’ code. Through the study of examples, advanced statistical analyses will be applied to new datasets using R. The majority of the examples will be ecological data which often require advanced statistical techniques. Skills in the running, interpretation and presentation of these analyses will be assessed.

Syllabus

The module will focus on how R software can be used to handle and analyse data. Examples of datasets that require various types of generalised linear models, mixed models, multivariate analysis or other modern statistical methods will be presented.

Teaching methods

Delivery type	Number	Length hours	Student hours
Practicals	10	3.00	30.00
Private study hours			120.00
Total Contact hours			30.00
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Computer workshops will create opportunities for one-to-one feedback from the lecturer and demonstrators when specific coding problems are encountered. There will be at least one formative, self-paced task per practical which should be completed in the presence of the lecturer and demonstrators so they can provide support when needed. This will identify concepts and common problems which may require further explanation.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	A written report describing the statistical analyses used and presentation of the results with reproducible code.	100.00
Total percentage (Assessment Coursework)		100.00

The format could take the form of a statistical analysis part of a methods sections in a paper; the results section of a paper and the code as presented in the supplementary material of a paper. 1000-3000 words. Being succinct is valued and appropriate lengths for such a report which is succinct while including all necessary information varies according to the statistical analysis that is needed.

Reading list

There is no reading list for this module

Last updated: 17/03/2023

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