Gwybodaeth Modiwlau

Module Identifier
PH24010
Module Title
Data Handling and Statistics
2019/2020
Co-ordinator
Semester
Intended for use in future years
Pre-Requisite
PH15720 or FG15720; or PH15510 or FG15510
Other Staff

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

Solving statistical problems by programming

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

Applying basic statistical principles.
Problem solving and numerical calculation in statistics.
Simple modelling by programming

Notes

This module is at CQFW Level 5