Statistics 3
| Code | School | Level | Credits | Semesters |
| MATH2110 | Mathematical Sciences | 2 | 10 | Spring UK |
- Code
- MATH2110
- School
- Mathematical Sciences
- Level
- 2
- Credits
- 10
- Semesters
- Spring UK
Summary
This module introduces a wide class of statistical techniques to model the functional relationship between a variable and multiple other variables. The focus is on linear models, which are fundamental to the study of regression and prediction models. Topics covered include:
- The general linear model.
- Least squares estimation and normal linear models.
- Inference for linear models.
- Assessment of model adequacy.
- Analysis of variance and covariance.
- Practical data analysis using statistical computing software.
Target Students
Single Honours and Joint Honours students from the School of Mathematical Sciences.
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
Classes
- One 1-hour lecture
- One 2-hour lecture
Teaching will be through a variety of methods, with the delivery tailored to the material on a week-by-week basis.
Assessment
- 40% Coursework: Summative assessment based on tasks distributed through the semester.
- 60% Exam (2-hour): Written examination – Spring.
Assessed by end of spring semester
Educational Aims
The purpose of this course is to introduce a wide range of statistical concepts and methods fundamental to applications of statistics in many fields and applications, but also to introduce the key concepts and theory of linear models. The module covers estimation and inference strategies for these models. Along with the theory, students will be exposed to practical examples drawn from real-life situations. Students will acquire knowledge and skills of relevance to higher level modules in statistics.Learning Outcomes
A student who completes this module successfully will be able to:
L1 – Develop and justify a mathematical framework to analyse data using linear models and clearly state assumptions and limitations of the results.
L2 – Reason logically and work analytically to check model fit, diagnose errors, and perform model selection amongst the class of linear models.
L3 – Use suitable inferential procedures to describe a model, perform hypothesis testing and prediction.
L4 – Present conclusions, verbally or in writing, using structured and mathematically rigorous arguments and contextually appropriate language.
L5 – Make effective use of a statistical software package to conduct analyse and fit linear models to data. Visualise results for a range of audiences.