Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 11 x 1 Hour Lectures |
| Practical | 11 x 2 Hour Practicals |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Weekly Coursework | 50% |
| Semester Assessment | Theory Exercise | 30% |
| Semester Assessment | Programming exercise | 20% |
| Supplementary Assessment | As determined by the Departmental Examining Board | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Recognise when binomial, Poisson, uniform, or Gaussian distribution describes data, and calculate their mean, standard deviation, and other expectation values.
2. Inspect and assess various sources of random error in experimental data, and recognise the effect of inter-dependence of measurements and extreme values on data sets.
3. Combine different errors to derive an error on the mean, and identify the most important source of error in an experiment and evaluate ways how that error can be reduced.
4. Analyse data by fitting a straight line to experimental data, evaluating the standard error in the slope and intercept, and discussing the null hypothesis.
5. Be able to write a simple program to solve basic statistical problems.
6. Manipulate with image processing software.
Brief description
This module is a lecture/laboratory-based course where the handling of data is treated in parallel with a course in the theory of measurement, the nature of experimental errors, random and systematic. The course provides an introduction to the basic statistics encountered in physics, including the binomial, poisson and normal distributions, and simple least-squares regression. The estimate of standard error, the combination of errors and the optimum design of experiments to reduce the final error in the most efficient way are covered. Applications of these concepts will be made through practical and computational work.
Content
Theory of measurement
Random and systematic errors
Accuracy and precision
Mean and standard deviation
Gaussian, poisson and binomial distribtions
Combining uncertainties
The least squares principle, graphing data and fitting a straight line to data
Hypothesis testing
Transferable skills
Problem solving and numerical calculation in statistics.
Simple modelling by programming
Notes
This module is at CQFW Level 5