Delivery Type	Delivery length / details
Lecture
Practical	44 Hours. Laboratory. 22 laboratory sessions

Assessment Type	Assessment length / details	Proportion
Semester Assessment	Course Work: MathCad exercises	20%
Semester Assessment	Course Work: Theory exercises	20%
Semester Assessment	Course Work: Young's Modulus experiment	30%
Semester Assessment	Course Work: Photometry experiment	30%
Supplementary Assessment	Supplementary assessment As determined by Departmental Examination Board	100%

Learning Outcomes

After taking this module student 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 MathCad 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 work and computational work using MathCad.

Content

Use of Mathcad for statistical problems.

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.

Experiments
1. Photometry experiment. Determine the temperature of an incandescent filament by optical measurements.
2. Young's Modulus experiment.
Determination of Young's Modulus, with special care taken to estimate the random uncertainty
in the final result. Identification of the parameter contributing most to the final error.

Transferable skills

Applying basic statistical principles.
Simple modelling using MathCad.
Writing lab reports.

Course Delivery

Assessment

Learning Outcomes

Brief description

Content

Transferable skills

Reading List

Notes