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

Programme/Approved Electives for 2020/21

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

Level 4 SH/DH Mathematics or equivalent
Level 5 Probability

Barred Combinations

None

Description for 2020/21

This module presents an introduction to statistical inference and linear regression models, and illustrates the theory with practical applications to real life data sets.
The first half of the module will focus on hypothesis testing, confidence interval and parameter estimation. The second half of the module will consider analysis of variance, least squares estimation, model selection and model checking and will demonstrate how statistical models can be used to draw conclusions from observational and experimental data collected in the medical, 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.

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 will also be considered.

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): 2

Study hours

36 hours lectures
12 Examples Classes
30 hours coursework preparation
70 hours private study
2 hours unseen examination

School Rules

None

Description of Module Assessment