# Module Information

Module Identifier
PH24010
Module Title
Data Handling and Statistics
2016/2017
Co-ordinator
Semester
Semester 1
Pre-Requisite
PH15720 or FG15720; or PH15510 or FG15510
External Examiners
• Professor Pete Vukusic (Professor - Exeter University)

Other Staff

#### 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 Programming exercise  20%
Semester Assessment Theory Exercise  30%
Semester Assessment Weekly Coursework  50%
Supplementary Assessment As determined by the Departmental Examining Board  100%

### Learning Outcomes

After taking this module students should be able to:

• explain the nature of random error in experimental data
• use the Gaussian distribution and appreciate why it applies in so many cases
• calculate the mean and standard deviation of data following a simple, unbiased Gaussian
• recognise the effect of inter-dependence of measurements and extreme values on data sets
• combine several different errors to derive a final error
• identify the most important source of error in an experiment and concentrate on reducing that error
• fit a straight line to experimental data and evaluate the standard error in the slope and intercept.
• write a simple program to model a physical system.

### 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
Writing lab reports.

### Notes

This module is at CQFW Level 5