Module Information
Module Identifier
PH24010
Module Title
Data Handling and Statistics
Academic Year
2015/2016
Co-ordinator
Semester
Semester 1
Pre-Requisite
PH15720 or PH15510, and successful completion of Part 1.
Other Staff
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 11 x 2 Hour Lectures |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Programming exercise | 25% |
| Semester Assessment | Experimental Data Analysis | 25% |
| Semester Assessment | Weekly Coursework | 25% |
| Semester Assessment | Theory Exercise | 25% |
| 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 laboratory-based course where the handling of data in selected experiments is treated in parallel with a course on 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
Experiments
Simple experiments to measure basic physical quantities, such as gravity, friction, Young's modulus, spring constant or sound speed.
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
Experiments
Simple experiments to measure basic physical quantities, such as gravity, friction, Young's modulus, spring constant or sound speed.
Transferable skills
Applying basic statistical principles.
Problem solving and numerical calculation in statistics.
Simple modelling by programming
Writing lab reports.
Problem solving and numerical calculation in statistics.
Simple modelling by programming
Writing lab reports.
Notes
This module is at CQFW Level 5