Course code 07 53 1103 70
ECTS credits 3
Course title in the language of instruction
Metody analizy danych doświadczalnych
Course title in Polish Metody analizy danych doświadczalnych
Course title in English
Experimental Data Analysis
Language of instruction Polish
Course level first-cycle programme
Course coordinator dr inż. Ewa Pastorczak
Course instructors dr hab. inż. Jaromir Tosiek
Delivery methods and course duration
Lecture Tutorials Laboratory Project Seminar Other Total of teaching hours during semester
Contact hours 15 15 15 0 45
E-learning No No No No No No
Assessment criteria (weightage) 0.00 0.00 0.00 0.00
Course objective
  1. To familiarize students with statistical methods of analysis of experimental data.
  2. To familiarize students with the theory of errors.
  3. To familiarize students with the preparation of measurement systems and procedures.
  4. To prepare students for the proper collection and analysis of experimental data.
Learning outcomes
  1. After completion of course student should be able to optimally design and assemble the measuring system (FFT1A_W12, FFT1A_W20)
  2. After completion of course student should be able to properly proceed measurements (FFT1A_W12, FFT1A_U25)
  3. After completion of course student should be able to identify sources of measurement errors (FFT1A_W04)
  4. After completion of course student should be able to interpret the results of statistical tests (FFT1A_W04, FFT1A_U15)
  5. After completion of course student should be able to carry out statistical analysis of measurement results and errors (FFT1A_U15, FFT1A_U22)
  6. After completion of course student should be able to properly collect and present the results of the measurements (FFT1A_U08, FFT1A_U22)
Assessment methods
LO 1,3: written test
LO 4-5: solving problems in class
LO 2: performing measurements in the laboratory
LO 6: preparing reports

Final assessment is the weighted average from written test (30%), the mean
assessment of the exercices (40%) and lab reports (30%)
Prerequisites
Knowledge of probability theory
Course content with delivery methods
LECTURE
1. Deduction and induction. Organization of the experimental research. Research planning.
2. Theory of errors. Kinds of error. Uncertainty class. Applications of the theory of errors. Reducing computational errors.
3. Selected topics from the probability and statistics. Statistical distributions applied in physics and data analysis.
4. Statistical hypotheses. Parameters of the distribution. Parametric estimation - most important estimators. 
5. Statistical hypothesis testing. Parametrical and non-parametrical tests. The analysis of variance.
6. Correlation and regression. Two-variable case. The least squares method and the maximum likelihood method.
7. Interpolation and extrapolation. Finding and rejecting outliers.
8. How to present the data? Plots, illustrations  and tables. Report and article writing, giving talks.

EXERCISES
Analysis of experimental data sets.

LABORATORY

Students perform simple measurements and present the results in the form of reports.
Basic reference materials
  1. Wojtatowicz T., ''Metody analizy danych doswiadczalnych. Wybrane zagadnienia'', skrypt IF PL, Lódz 1998.
  2. Brandt S. ''Analiza danych'', PWN, Warszawa 1998.
Other reference materials
  1. Linnik J.W., "Metoda najmniejszych kwadratów i teoria opracowywania obserwacji", PWN, Warszawa 1962.
  2. Gajek L., Kałuszka M., ''Wnioskowanie statystyczne. Modele i metody'', WNT, Warszawa 1994.
  3. Squires G.L., ''Praktyczna fizyka'', PWN, Warszawa 1992.
  4. Abramowicz H., ''Jak analizować wyniki pomiarów'', PWN, Warszawa 1992.
Average student workload outside classroom
37
Comments
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