ST104-12 Statistical Laboratory I
This module runs in the second half of term 2 and first half of term 3.
This module is core for students with their home department in Statistics and is also available for external students who have taken the necessary prerequisites. This module will be useful for ST221 Statistical Modelling and other modules which use statistical data analysis such as Programming for Data Science and Multivariate Statistics.
If you are an external student considering transferring to a course in Data Science, Mathematics and Statistics or MORSE you are encouraged to take ST104 Statistical Laboratory, but if you have not taken it, then the expectation is that you will learn independently how to program in R.
Statistics Students: ST115 Introduction to Probability
Non-Statistics Students: ST111 Probability A and ST112 Probability B
Results from the coursework from this module may be partly used to determine of exemption eligibility in the computer based assessment components of the Institute and Faculty of Actuaries modules CS1, CS2, CM1 and CM2. (Independent application to the IFoA may be required.)
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 ST115 and ST111/2.
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
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 stimulate samples from a variety of probability distributions.
- Gain the ability to propose appropriate probabilistic models for simple data sets.
Indicative reading list
Subject specific skills
|Lectures||29 sessions of 1 hour (24%)|
|Practical classes||8 sessions of 1 hour (7%)|
|Private study||47 hours (39%)|
|Assessment||38 hours (31%)|
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.
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 D1
|Laboratory Report 1||15%||18 hours|
Due in Term 2 Week 10.
|Laboratory Report 2||15%||18 hours|
Due in Term 2 Week 3.
|2 hour examination (Summer)||70%||2 hours|
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
Reports will be marked and returned to students within 20 working days.
Solutions and cohort level feedback will be provided for the examination.
There is currently no information about the courses for which this module is core or optional.