Statistical Methods in Data Analysis - PHY00009M
- Department: Physics
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2022-23
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Autumn Term 2022-23 |
Module aims
This module aims to convey the understanding and experience in the use of statistical methods in physics necessary for unbiased evaluation of data (either experimental or
theoretical). The module introduces advanced methods in data analysis, which includes areas of Maximum Likelihood, fitting methods, and confidence regions.
Module learning outcomes
At the end of this module successful students will be able to:
- Describe the basic statistics involved in the analysis of physical data.
- Demonstrate an understanding of the principles underlying data analysis.
- Define the appropriate statistic for use in concrete fitting of data, including X ² and maximum likelihood methods.
- Perform data fitting and evaluate the fit results, including error matrices, confidence limits and goodness-of-fit.
- Demonstrate the use of maximum likelihood methods.
- Evaluate confidence intervals or confidence regions in data analysis in general.
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 20 |
| Essay/coursework | 80 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 80 |
Module feedback
Our policy on how you receive feedback for formative and summative purposes is contained in our Department Handbook.
Indicative reading
- R. J. Barlow. Statistics: A guide to the use of statistical methods in the physical sciences. John Wiley & Sons, Inc., 1989.