Individual course details
Study programme General Physics, Theoretical and Experimental Physics, Applied and Computer Physics
Chosen research area (module)  
Nature and level of studies Academic studies of first degree
Name of the course Processing the measurement results
Professor (lectures) Srdjan Bukvic
Professor/associate (examples/practical) Milos Skocic
Professor/associate (additional)  
ECTS 6 Status (required/elective) required
Access requirements  
Aims of the course To introduce students to the basics of modern processing of experimental data based on probabilistic (statistical) principles, classical and modern numerical methods and characteristics of measuring system and ambient conditions. Special attention was paid to the presentation of the results in a form in which contemporary scientific reports and papers are written.
Learning outcomes Students are trained for basic independent analysis and processing of experimental data, as well as their presentation.
Contents of the course
Lectures Classification of measurements. Basic properties of measuring instruments (range, readability, resolving power, accuracy, precision, linearity, drift, hysteresis.) How to report uncertainties.  Significant figures.  Absolute and relative error. Types of experimental errors. Propagation of uncertainties.  Sums and differences; Products and quotients. Estimation of the maximum error in indirect measurements. Graphical presentation of experimental data and fitting to the straight line by graphical method; estimation of errors by graphical method. The Least-square fitting to straight line. Random and systematic error. The mean and standard deviation. The standard deviation as the uncertainty in a single measurement. Histograms and distributions. Limiting distribution.  The Normal distribution. The standard deviation as 68% confidence limit. The  weighted average. The random error in indirect measurements. Justification of addition in quadrature.  The standard deviation of the mean. The weighted least-square fitting.  Calculations of the constants A and B. Uncertainty in constants A i B. The weighted least-square fitting to straight line if uncertainties in x are not negligible. 
Examples/ practical classes  
Recommended books
1 J. R. Taylor, An Introduction to Error Analysis (University Science Books, 1997)
2 P. R. Bevington, D. K. Robinson, Data Reduction and Error Analysis (Mc Graw Hill, 2003).
3  
4  
5  
Number of classes (weekly)
Lectures Examples&practicals   Student project Additional
2 2      
Teaching and learning methods Lectures, exercises (homework assignments), colloquium.
Assessment (maximal 100)
assesed coursework mark examination mark
coursework 10 written examination 30
practicals   oral examination 50
papers 10    
presentations