SylabUZ
| Course name | Measurement data analysis |
| Course ID | 13.2-WF-FizP-MDA-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | First-cycle studies leading to Bachelor's degree |
| Beginning semester | winter term 2020/2021 |
| Semester | 6 |
| ECTS credits to win | 5 |
| Available in specialities | General physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Acquaint students with the basics of the data analysis and statistical inference. Development of skills in application of the standard techniques used for data analysis and for simulations supporting this analysis with the use of the public domain software.
Experience from the first and second physics laboratory, knowledge of mathematical methods of physics, elements of the statistical analysis.
- Measurement uncertainty: significant digits and their rounding, the distribution of the population and the distribution of the sample, calculation of the mean, median, mode, standard deviation, range of variation and the average deviation.
- Probability distributions: calculation of the moments of a random variable with a known probability distribution, the cumulative distribution function and estimation of probabilities.
- Error Analysis: instrumental and statistical uncertainties, the equation of propagation of error, variance and covariance, the particular cases of error propagation, variance and covariance, computer implementations.
- Estimation of averages and errors: estimation of a mean, standard deviation and standard error, weighted estimates, relative estimates, testing of statistical hypothesis: Student's t-test and χ2.
- Monte Carlo Techniques: random numbers generators, generation of random numbers from various probability distributions by the transformation of a homogeneous distribution, examples of simulations of simple measuring systems and experiments.
- Fitting to a stright line with the least squares method: linear regression exercises, solving normal equations and graphics science.
- Least squares method for polynominal fitting: solving of normal equations with determinant and matrix methods, fitting by using discrete orthogonal polynomials and Legendre polynomials.
- Least squares method: Marquardt'a-Levenberg method as the optimal method for linear and non-linear fit.
- Fit testing: χ2 test, χ2 distribution, correlation coefficient, multi-dimensional correlations,
F-test, confidence intervals, the Monte Carlo test.
- Grace - the program for presentation and analysis of data: data loading, operations on data, the graphic presentation, linear regression, curves fitting.
Conventional lecture, probabilistic experiment.
Tutorials, programming exercises, computer simulations.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture: final exam.
Classes: passing two tests, and performing the statistical project.
Final evaluation of laboratory exercises: arithmetic mean of the tests and the project.
Final evaluation of the course: arithmetic mean of the exam and laboratory exercises.
[1] H. Szydłowski (red), Teoria pomiarów, PWN, Warszawa 1981.
[2] S. Brandt, Analiza danych, PWN, Warszawa 1998.
[1] R. Nowak, Statystyka dla fizyków, PWN, Warszawa 2002.
[2] P. R. Bevington, D. K. Robinson, Data reduction and error analysis for the physical science, McGraw-Hill., Inc., New York 1992.
[3] J. Koronacki, J. Mielniczuk, Statystyka dla studentów kierunków technicznych i przyrodniczych, WNT, Warszawa 2001.
Modified by dr hab. Piotr Lubiński, prof. UZ (last modification: 04-06-2020 15:12)
