Individual
course details |
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Study programme |
General
Physics, Theoretical and Experimental Physics, Applied and Computer Physics |
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Chosen research area (module) |
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Nature and level of studies |
Academic
studies of first degree |
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Name of the course |
Processing
the measurement results |
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Professor (lectures) |
Srdjan
Bukvic |
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Professor/associate (examples/practical) |
Milos
Skocic |
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Professor/associate (additional) |
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ECTS |
6 |
Status
(required/elective) |
required |
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Access requirements |
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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. |
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Learning outcomes |
Students
are trained for basic independent analysis and processing of experimental
data, as well as their presentation. |
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Contents of the course |
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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. |
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Examples/ practical classes |
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Recommended books |
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1 |
J. R.
Taylor, An Introduction to Error Analysis (University Science Books, 1997) |
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2 |
P. R.
Bevington, D. K. Robinson, Data Reduction and Error Analysis (Mc Graw Hill,
2003). |
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3 |
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4 |
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5 |
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Number of classes (weekly) |
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Lectures |
Examples&practicals |
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Student
project |
Additional |
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2 |
2 |
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Teaching and learning methods |
Lectures,
exercises (homework assignments), colloquium. |
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Assessment (maximal 100) |
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assesed coursework |
mark |
examination |
mark |
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coursework |
10 |
written
examination |
30 |
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practicals |
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oral
examination |
50 |
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papers |
10 |
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presentations |
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