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 
firstcycle 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 
Elearning 
No 
No 
No 
No 
No 
No 

Assessment criteria (weightage) 
0.00 
0.00 
0.00 


0.00 


Course objective 
 To familiarize students with statistical methods of analysis of experimental data.
 To familiarize students with the theory of errors.
 To familiarize students with the preparation of measurement systems and procedures.
 To prepare students for the proper collection and analysis of experimental data.

Learning outcomes 
 After completion of course student should be able to optimally design and assemble the measuring system (FFT1A_W12, FFT1A_W20)
 After completion of course student should be able to properly proceed measurements (FFT1A_W12, FFT1A_U25)
 After completion of course student should be able to identify sources of measurement errors (FFT1A_W04)
 After completion of course student should be able to interpret the results of statistical tests (FFT1A_W04, FFT1A_U15)
 After completion of course student should be able to carry out statistical analysis of measurement results and errors (FFT1A_U15, FFT1A_U22)
 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 45: 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 nonparametrical tests. The analysis of variance.
6. Correlation and regression. Twovariable 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 
 Wojtatowicz T., ''Metody analizy danych doswiadczalnych. Wybrane zagadnienia'', skrypt IF PL, Lódz 1998.
 Brandt S. ''Analiza danych'', PWN, Warszawa 1998.

Other reference materials 
 Linnik J.W., "Metoda najmniejszych kwadratów i teoria opracowywania obserwacji", PWN, Warszawa 1962.
 Gajek L., Kałuszka M., ''Wnioskowanie statystyczne. Modele i metody'', WNT, Warszawa 1994.
 Squires G.L., ''Praktyczna fizyka'', PWN, Warszawa 1992.
 Abramowicz H., ''Jak analizować wyniki pomiarów'', PWN, Warszawa 1992.

Average student workload outside classroom 
37 
Comments 
Brak uwag 
Last update 
