Analysis of Experimental Uncertainty 390-FS1-1RNP

Profile : academic
Form: stationary
Subject: obligatory, module "Rudiments of physics"
Branch of science and Discipline of science: Physical sciences, physics
Year/Semester: 1 year/2 semester, first degree (undergraduate) study (general physics)
Prerequisites: passed exams on Introduction to physics, skill in using Worksheet.
Didactic units: lecture 15 hrs., laboratory 15 hrs.
Didactic methods: Lecture in the form of a multimedia presentations (lecture notes available on e-learning); laboratory: performing computer excersises related to lecture subjects.
ECTS credits: 2
Balance the workload of the average student: participation in lectures (15 hrs.), OSH training - 1 hr., participation in laboratory excercises (15 hrs.), active participation in the consultations (3 hrs.), preparation to computer classes - 15 hrs., preparing for written test and participation in the test - 6 hrs. 55 hrs. in total.
Quantitative indicators: classes with academic teacher - 34 hrs., 2 ECTS, practical classes (with students activity) - 15 hrs. (ca. 1 ECTS).

Lecture topics:
1. Introduction, system of physical units, methods of experimental data presentation.
2. Basic definitions related to experiment, simple and complex quantities, sources and classification of experimental errors and uncertainties.
3. Rounding and comparison of the results, precision of the results, position and significant digits.
4. Basis of statistical data analysis.
5. Examples of probability distribution function.
6. Statistical analysis of uncertainties of direct and indirect measurements (type A evaluation of uncertainties).
7. Type B evaluation of uncertainties.
8. Analysis of linearly dependent data.
9. Examples of evaluation of parameters of nonlinear functions matching the distribution of some measurement results.
10. Testing of statistical hypothesis using χ^2 test and t-Student test. Planning of the measurements.

Laboratory topics:
1. Methods of experimental data presentation .
2. Rounding and comparison of the experimental results, precision of the results, position and significant digits.
3. Evaluation of parameters of some probability distribution functions.
4. Analysis of random uncertainties of direct and indirect measurements (type A).
5. Type B evaluation of uncertainties.
6. Analysis of linearly dependent data.
7. Examples of fitting of composite curves to the experimental data.
8. Testing of statistical hypothesis using χ^2 test and t-Student test.