Statistical Foundations
| Code | School | Level | Credits | Semesters |
| MATH4065 | Mathematical Sciences | 4 | 20 | Autumn UK |
- Code
- MATH4065
- 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 a 'common core' consisting of:
- statistical concepts and methods;
- linear models;
- probability techniques;
- Markov chains.
Students will gain experience of using a statistical package and interpreting its output. The common core material will be covered primarily at the beginning of the semester.
Target Students
Available to students taking MSc Financial and Computational Mathematics students, MSc Machine Learning in Science, MSc Statistics and MSc Statistics & Applied Probability, MSc Statistics with Machine Learning, MSci Natural Sciences.
Classes
- One 1-hour workshop each week for 11 weeks
- Three 1-hour lectures each week for 11 weeks
- One 1-hour computing each week for 11 weeks
Assessment
- 20% Coursework 1: Coursework
- 80% Exam 1 (3-hour): Exam
Assessed by end of autumn semester
Educational Aims
The purpose of thiscourse is to provide the initial underpinning of the fundamentals of :statistical inference;linear regression models;probability techniques;Markov chains.In addition there will be hands-on experience of modern statistical computing software. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.Learning Outcomes
A student who completes this course successfully will be able to:
L1 - derive and explain properties of basic statistical inference (frequentist and Bayesian); linear regression models, probability techniques and Markov chains;
L2 - derive point and interval estimators, and perform hypothesis tests for a variety of situations;
L3 - apply the theory and methods for statistical inference, linear regression models and probability techniques to a wide range of practical examples;
L4 - use a statistical package to derive results concerning statistical inference.