Foundations of Statistics (Distance Learning)
| Code | School | Level | Credits | Semesters |
| MATH4081 | Mathematical Sciences | 4 | 20 | Autumn UK |
- Code
- MATH4081
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
In this course the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. The course will cover the core foundations of statistical theory consisting of:
- probability distributions and techniques;
- statistical concepts and methods;
- linear models.
The course highlights the importance of computers, and in particular, statistical packages, in performing modern statistical analysis. Students will be introduced to the statistical package R as a statistical and programming tool and will gain experience in interpreting and communicating its output.
Target Students
Available to MSc Statistics Science (Distance Learning) students.
Classes
- Two 1-hour tutorials each week for 5 weeks
- Two 1-hour computings each week for 5 weeks
This module is designed for distance learning programmes where delivery of material will largely be asynchronous through course notes support by lecture videos, Moodle quizzes and exercises. The tutorial will be used to support and reinforce the asynchronous learning. The computer software learning will be through specially designed workbooks with support sessions through online computer labs.
Assessment
- 20% Project: Written report to assess writing structure and basic R knowledge. Individual report approximately 8 pages
- 80% Exam 1 (3-hour): Written exam to assess statistical knowledge and understanding.
Assessed by end of autumn semester
Educational Aims
The purpose of this course is to provide the initial underpinning of the core fundamentals of statistics and will consist of three strands: probability techniques; statistical inference and statistical modelling. This will be supported by an introduction to core skills training: Introduction to the statistical software R and training in scientific writing for effective communication of statistical methods and analysis.Learning Outcomes
A student who completes this course successfully will be able to:
L1 - derive and explain properties of basic statistical inference; linear regression models and probability techniques;
L2 - perform exploratory data analysis; summarising their analysis and proposing further investigations;
L3 - derive point and interval estimators, and perform hypothesis tests for a variety of situations;
L4 - apply the theory and methods for statistical inference, linear regression models and probability techniques to a wide range of practical examples;
L5 - use the statistical package R to derive results concerning statistical inference;
L6 - communicate their statistical analysis in a written report.