Applied Statistics and Probability (20 cr)
| Code | School | Level | Credits | Semesters |
| MTHS4005 | Mathematical Sciences | 4 | 20 | Autumn UK |
- Code
- MTHS4005
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
The module covers introductory topics in statistics and probability that could be applied to data analysis in a broad range of subjects. Topics include common univariate probability distributions, joint and conditional distributions, parameter estimation (e.g. via maximum likelihood), confidence intervals, hypothesis testing and statistical modelling. Consideration is given to issues in applied statistics such as data collection, design of experiments, and reporting statistical analysis. Topics will be motivated by solving problems and case studies, with much emphasis given to simulating and analysing data using computer software to illustrate the methods.
Target Students
Natural Sciences students studying Mathematics, Liberal Arts, MSc Machine Learning in Science, MSc Data Science students, and students not from the School of Mathematical Sciences with A-Level maths.
Classes
- One 2-hour workshop each week for 12 weeks
- Two 1-hour lectures each week for 12 weeks
Assessment
- 40% Report: A statistical report demonstrating the use of statistical software to analyse a data set and an ability to report statistical conclusions (5000 words)
- 60% Exam 1 (2-hour): Written exam.
Assessed by end of autumn semester
Educational Aims
The module covers introductory topics in statistics and probability that could be applied to data analysis in a broad range of subjects. Topics include common univariate probability distributions, joint and conditional distributions, parameter estimation (e.g. via maximum likelihood), confidence intervals, hypothesis testing and statistical modelling. Consideration is given to issues in applied statistics such as data collection and design of experiments, and reporting statistical analysis. Topics will be motivated by solving problems and case studies, with much emphasis given to simulating and analysing data using computer software to illustrate the methods.Learning Outcomes
L1 - Summarise data using sample statistics and graphical plots;
L2 - Estimate parameters needed to fit common probability distributions to data;
L3 - Fit statistical models, such as linear multiple regressions, to data;
L4 - Test hypotheses concerning parameter values, model goodness-of-fit and comparisons between models using the likelihood ratio test;
L5 - Analyse data using statistical software and interpret output;
L6 - Communicate statistical analysis in a written report.