Postgraduate Programme and Module Handbook 2022-2023 (archived)

# Module MATH30520: Statistical Methods

## Department: Mathematical Sciences

### MATH30520: Statistical Methods

Type | Tied | Level | 3 | Credits | 20 | Availability | Not available in 2022/23 | Module Cap | None. |
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Tied to | G1K509 |
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#### Prerequisites

- Statistical Concepts

#### Corequisites

- None

#### Excluded Combination of Modules

- None

#### Aims

- To provide a working knowledge of the theory, computation and practice of multivariate statistical methods, with focus on the linear model.

#### Content

- Introduction to statistical software for data analysis.
- Multivariate normal distribution.
- Multivariate analysis, including principal component analysis.
- Regression: linear model, inference, variable selection, analysis of variance, factorial experiments, diagnostics, influence, weighted least squares, transformations.

#### Learning Outcomes

Subject-specific Knowledge:

- By the end of the module students will:
- be able to solve novel and/or complex problems in Statistical Methods.
- have a systematic and coherent understanding of the theory and mathematics underlying the statistical methods studied.
- be able to formulate a given problem in terms of the linear model and use the acquired skills to solve it.
- have acquired a coherent body of knowledge on regression methodology, based on which extensions of the linear model such as generalized or nonparametric regression models can be easily learnt and understood.

Subject-specific Skills:

- In addition students will have specialised mathematical skills in the following areas which can be used with minimal guidance: Modelling, Computation.

Key Skills:

- Synthesis of data, critical and analytical thinking, computer skills

#### Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Computer practicals consolidate the studied material and enhance practical understanding.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Formatively assessed assignments provide practice in the application of logic and high level of rigour as well as feedback for the students and the lecturer on students' progress.
- The two end-of-term computer-based examination components assess the ability to use statistical software and basic programming to solve predictable and unpredictable problems.
- The end-of-year written examination assesses the acquired knowledge from a more conceptual viewpoint, including mastery of theoretical aspects underpinning the studied methodology.

#### Teaching Methods and Learning Hours

Activity | Number | Frequency | Duration | Total/Hours | |
---|---|---|---|---|---|

Lectures | 42 | 2 per week for 20 weeks and 2 in term 3 | 1 Hour | 42 | |

Problems Classes | 8 | Four in each of terms 1 and 2 | 1 Hour | 8 | |

Preperation and Reading | 150 | ||||

Total | 200 |

#### Summative Assessment

Component: Examination | Component Weighting: 70% | ||
---|---|---|---|

Element | Length / duration | Element Weighting | Resit Opportunity |

Written Examination | 2 hours 30 minutes | 100% | |

Component: Practical Assessment | Component Weighting: 30% | ||

Element | Length / duration | Element Weighting | Resit Opportunity |

Two computer-based examinations | 2 hours each | 100% |

#### Formative Assessment:

Eight written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University