Total Credits: 20
Level: Level 4
Target Students: Single Honours students, and students taking MSc in Statistics and MSc in Statistics and Applied Probablity in the School of Mathematical Sciences. Available to JYA/Erasmus students.
|Spring||Assessed by end of Spring Semester|
Prerequisites: G14FOS is required as a pre-requisite for MSc students only instead of G12SMM
|G12SMM||Statistical Models and Methods|
Summary of Content: This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real time series data will be described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. Students will gain experience of using a statistical package and interpreting its output. The module will cover:
Method and Frequency of Class:
|Activity||Number Of Weeks||Number of sessions||Duration of a session|
|Lecture||10 weeks||1 per week||1 hour|
|Lecture||10 weeks||1 per week||2 hours|
|Workshop||10 weeks||1 per week||1 hour|
Method of Assessment:
|Exam 1||80||2 hour 30 minute written examination|
|Project 1||20||Individual investigation using a computer package|
Professor A Wood
Education Aims: The purpose of this module is to deepen and broaden the studentsí knowledge and experience of statistics by studying the theory and methods used in time series and forecasting.
This module is in the Statistics Pathway and builds upon the statistical ideas and methods and probability techniques introduced in the modules G12SMM and G12PMM or in the module G14FOS. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.
A student who completes this module successfully will be able:
L1 - to state and prove standard results in the time domain relating to the theory, models and methods of time series and forecasting, and apply them to examples;
L2 - to derive, calculate and explain properties of time domain models and methods;
L3 - to derive appropriate point and interval estimators, and construct suitable test procedures;
L4 - to use statistical softaware packages to fit models to data sets, assess their fit, make predicitions, and identify models underlying data sets
L5 - to analyse and explain statistical results in the context of time series and forecasting.
L6 - to present a systematic account of concepts for time series and forecasting
L7 - research and synthesize a topic related to forecasting and time series.
Offering School: Mathematical Sciences
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