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# Statistical Methods in Data Analysis - PHY00009M

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• Department: Physics
• Module co-ordinator: Dr. Alessandro Pastore
• Credit value: 10 credits
• Credit level: M
• Academic year of delivery: 2022-23

## Module will run

Occurrence Teaching cycle
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.

## Assessment

Task Length % of module mark
Essay/coursework
Physics Practice Questions
N/A 20
Essay/coursework
Statistical Methods in Data Analysis 1 week assignment
N/A 80

None

### Reassessment

Task Length % of module mark
Essay/coursework
Statistical Methods in Data Analysis 1 week assignment
N/A 80

## Module feedback

Our policy on how you receive feedback for formative and summative purposes is contained in our Department Handbook.