ST121 Statistical Laboratory
ST12110 Statistical Laboratory
Introductory description
This module runs in Term 2.
This module is not available to students within the Dept of Statistics, who take ST117 instead. This module is available for external students who have taken the necessary prerequisites. This module will be useful for ST231 Statistical Modelling and other modules which use statistical data analysis such as ST340 Programming for Data Science and ST323 Multivariate Statistics.
Prerequisites for nonStatistics Students: ST120 Introduction to Probability.
Module aims
To introduce students to the R software package, making use of it for exploratory data analysis and simple simulations. This should deepen and reinforce the understanding of probabilistic notions being learnt in ST120.
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.
Introduction to R
Exploratory data analysis: methods of visualisation and summary statistics
Sampling from standard discrete and continuous distributions (Bernoulli, Geometric, Poisson, Gaussian, Gamma)
Generic methods for sampling from univariate distributions
The use of R to illustrate probabilistic notions such as conditioning, convolutions and the law of large numbers
Examples of modelling real data (but without formal statistical inference) and the use of visualisations to assess fit
Learning outcomes
By the end of the module, students should be able to:
 Gain familiarity with the R software package, making use of it for exploratory data analysis.
 Use R to simulate samples from a variety of probability distributions.
 Gain the ability to propose appropriate probabilistic models for simple data sets.
Indicative reading list
"R for Data Science" by H.Wickham; G.Grolemund 2016
"HandsOn Programming with R" by G.Grolemund 2014
"Statistical methods" by Rudolf J. Freund; William J. Wilson; Donna L. Mohr 2010.
"Problem solving: a statistician's guide" by Christopher Chatfield 2017.
"Introduction to the practice of statistics" David S. Moore; George P. McCabe; Bruce Craig 2012.
"Introductory statistics with R" Peter Dalgaard 2008.
View reading list on Talis Aspire
Subject specific skills
Select and apply appropriate mathematical and/or statistical techniques.
Create structured and coherent arguments communicating them in written form.
Communicate subjectspecific information effectively and coherently.
Analyse problems, abstracting their essential information formulating them using appropriate mathematical language to facilitate their solution.
Select and apply appropriate statistical programming language (for example, R) for exploratory data analysis
Transferable skills
Critical thinking: extracting patterns from incomplete data and using them to form evidencebased conclusions.
Problem solving: use of logical reasoning to build arguments grounded in evidence and with explicit underlying assumptions.
Selfawareness: monitoring of your own learning and seeking feedback.
Communication: verbal discussion of ideas in seminars and among peers; written communication in assignments.
Information literacy: evaluation of data and uncertainty in a modelbased way.
Digital literacy: use of computational tools to understand and visualise data, and to produce reports.
Professionalism: selfmotivation, taking charge of your own learning, and prioritising effectively.
Study time
Type  Required 

Lectures  20 sessions of 1 hour (20%) 
Practical classes  4 sessions of 1 hour (4%) 
Private study  44 hours (44%) 
Assessment  32 hours (32%) 
Total  100 hours 
Private study description
Weekly revision of lecture slides and materials, wider reading and practice exercises, developing familiarity with R programming language 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.
Assessment group D
Weighting  Study time  

Laboratory Report 1  15%  15 hours 
The first report will emphasise on R coding skills and/or other statistical questions. 

Laboratory Report 2  15%  15 hours 
The second report will emphasise on R as a simulation and visualisation tool and/or other statistical questions. 

Inperson Examination  70%  2 hours 
You will be required to answer all questions on this examination paper.

Assessment group R
Weighting  Study time  

Inperson Examination  Resit  100%  
You will be required to answer all questions on this examination paper.

Feedback on assessment
Reports will be marked and returned to students within 20 working days.
Solutions and cohort level feedback will be provided for the examination.
Antirequisite modules
If you take this module, you cannot also take:
 ST11715 Introduction to Statistical Modelling
Courses
This module is Option list B for:
 Year 1 of UMAAG105 Undergraduate Master of Mathematics (with Intercalated Year)

UMAAG100 Undergraduate Mathematics (BSc)
 Year 1 of G100 Mathematics
 Year 1 of G100 Mathematics
 Year 1 of G100 Mathematics

UMAAG103 Undergraduate Mathematics (MMath)
 Year 1 of G100 Mathematics
 Year 1 of G103 Mathematics (MMath)
 Year 1 of G103 Mathematics (MMath)
 Year 1 of UMAAG106 Undergraduate Mathematics (MMath) with Study in Europe
 Year 1 of UMAAG1NC Undergraduate Mathematics and Business Studies
 Year 1 of UMAAG1N2 Undergraduate Mathematics and Business Studies (with Intercalated Year)
 Year 1 of UMAAGL11 Undergraduate Mathematics and Economics
 Year 1 of UMAAG101 Undergraduate Mathematics with Intercalated Year
Catalogue 
Resources 
Feedback and Evaluation 
Grade Distribution 
Timetable 
Assessments dates for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook.