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MAT-20027 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 14 February 2023

MAT-20027 - Linear Statistical Models
Coordinator: Milica Bucknall Room: MAC2.17 Tel: +44 1782 7 34852
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

Level 4 SH/DH Mathematics or equivalent
Level 5 Probability


Barred Combinations

None

Description for 2022/23

This module presents an introduction to statistical inference and illustrates the theory with practical applications to real life data sets.
The first half of the module will encorporate hypothesis testing, confidence interval and parameter estimation. The second half of the module considers regression and anova methods, and demonstrates how statistical models can be used to draw conclusions from observations and experimental data collected in the physical and social sciences.
This module develops the following Keele Graduate attributes:
1. An open and questioning approach to ideas, demonstrating curiosity and independence of thought.
4. The ability to solve problems creatively using a range of different approaches and techniques, and to determine which techniques are appropriate for the issue at hand.
6. The ability to communicate clearly and effectively in written and verbal form.

Aims
To give a formal introduction to statistical inference and the construction of statistical methods, including hypothesis testing and confidence interval estimation.
To provide a theoretical treatment of the linear regression model and of the estimation, model selection and testing procedures required to fit the model to data; practical aspects of fitting the models in standard statistical software (most likely SPSS) will also be covered.

Intended Learning Outcomes

understand concepts of statistical inference, calculate confidence and prediction intervals and perform hypothesis tests for model parameters: 1,2
perform hypothesis tests using analysis of variance and assess the lack of fit of a linear regression model: 1,2
perform least squares estimation and understand and derive the properties of the least squares estimators for parameters of the linear regression model: 1,2
report and interpret results, including model selection and model checking, derived from data analysis using standard statistical package(s): 1,2

Study hours

24 hours lectures
24 hours tutorial/examples classes
30 hours coursework preparation
70 hours private study
2 hours unseen examination

School Rules

None

Description of Module Assessment

1: Coursework weighted 20%
Assignment x 2 given out in class, with one week given for completion of each
There will be two assignments, each carrying 10% weight towards the final module mark. Assignments will be given in teaching week 5 and in teaching week 10. Students will be given 1 week to complete each assignment. The assignments will consist of questions covering both the theoretical and practical aspects of the material covered up to the point when the assignments are handed out.

2: Unseen Exam weighted 80%
2-hour unseen examinations
The examination paper will consist of no less than five and not more than eight questions, all of which are compulsory.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 14 February 2023