Semester 2 Credit Value: | 10 |
ECTS Credits: | 5.0 |
Code | Title |
---|---|
MAS1041 | Mathematical Methods A |
MAS1042 | Mathematical Methods B |
A-level Mathematics (or equivalent)
Code | Title |
---|---|
MAS2341 | Introduction to Probability |
MAS2041, MAS2042 are acceptable in place of MAS1041, MAS1042 respectively
To develop ideas and methods essential for the study of probability and statistics. To develop a familiarity with ideas of continuous probability models and interpretation, and inference through likelihood. To further develop use of the statistical package R. To further develop writing skills through project work.
Module Summary
The course will be mainly concerned with using probability theory and observed data together in order to estimate the properties of a population. For example, we will see how we can use information on the actual survival rates of a group of patients to make statements about survival rates of patients in general, and how confident we can be about the accuracy of such statements. Key statistical ideas will be introduced in the examination of these questions.
Continuous probability models. Calculation and interpretation of mean and variance. Practical illustrations and calculation of probabilities using R. Introduction to statistical inference: estimation of population quantities and properties of estimators. Introduction to likelihood inference: maximum likelihood estimation and the motivation and use of the asymptotic properties of the maximum likelihood estimator. Introduction to hypothesis tests: simple hypotheses, critical regions and power. Motivation and use of a one-sample t-test.
Students will be familiar with ideas of statistical modelling, data analysis, interpretation and introductory likelihood methods. They will know the rudiments of hypothesis testing, such as size, power and critical regions.
Students will be able to use probability distributions to model real-life situations. They will be able to apply elementary statistical methods such as the one-sample t-test, in the analysis of data. They will be able to use likelihood methods in the analysis of data.
Graduate Skills Framework Applicable: | Yes |
Category | Activity | Number | Length | Student Hours | Comment |
---|---|---|---|---|---|
Guided Independent Study | Assessment preparation and completion | 2 | 4:00 | 8:00 | Project |
Guided Independent Study | Assessment preparation and completion | 4 | 2:00 | 8:00 | CBAs |
Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Formal lectures |
Guided Independent Study | Assessment preparation and completion | 2 | 8:00 | 16:00 | Written assignments |
Scheduled Learning And Teaching Activities | Lecture | 2 | 1:00 | 2:00 | Revision lectures |
Scheduled Learning And Teaching Activities | Lecture | 4 | 1:00 | 4:00 | Problem classes |
Guided Independent Study | Assessment preparation and completion | 1 | 11:00 | 11:00 | Revision for unseen Exam |
Guided Independent Study | Assessment preparation and completion | 1 | 1:30 | 1:30 | Unseen Exam |
Scheduled Learning And Teaching Activities | Practical | 2 | 1:00 | 2:00 | N/A |
Scheduled Learning And Teaching Activities | Drop-in/surgery | 2 | 1:00 | 2:00 | Tutorials in the lecture room |
Scheduled Learning And Teaching Activities | Drop-in/surgery | 12 | 0:00 | 0:00 | Office Hours in a staff office |
Guided Independent Study | Independent study | 2 | 2:00 | 4:00 | Assignment review |
Guided Independent Study | Independent study | 1 | 21:30 | 21:30 | Studying, practising and gaining understanding of course material |
Total | 100:00 |
Code | Title |
---|---|
MAS1342 | Introduction to Statistics |
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the studentsâ€™ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and for simulation to give insight into the ideas/methods studied. Office hours provide an opportunity for more direct contact between individual students and the lecturer.
The format of resits will be determined by the Board of Examiners
Description | Length | Semester | When Set | Percentage | Comment |
---|---|---|---|---|---|
Written Examination | 90 | 2 | A | 70 | unseen |
Module Code | Module Title | Semester | Comment |
---|---|---|---|
2 | N/A |
Description | Semester | When Set | Percentage | Comment |
---|---|---|---|---|
Written exercise | 2 | M | 10 | Project work |
Prob solv exercises | 2 | M | 10 | N/A |
Computer assessment | 2 | M | 10 | CBAs |
A substantial formal unseen examination is appropriate for the assessment of the material in this module. Approximately two written assignments of approximately equal weight (worth approximately 10% in total), approximately two computer based assessments of approximately equal weight (worth approximately 10% in total), and project work (worth 10%) allow the students to develop their problem solving techniques, to practise the methods learnt in the module and to receive feedback; this is thus formative as well as summative assessment.
N/A
Disclaimer: The information contained within the Module Catalogue relates to the 2016/17 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2017/18 entry will be published here in early-April 2017. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.