Infopaket

Statistical methods of processing the experiment

ВК2

Вибіркова дисципліна для ОП

Second

2023/2024

5 Semester

3

Mastering basic concepts such as: mathematical expectation, variance and standard deviation of continuous and discrete random values, correlation coefficient of random variables, region of joint confidence probability, laws of large numbers, central limit theorem for determining the convergence of distribution laws to the normal law. Be able to approximate experimental data using the method of least squares in the case of equal and unequal measurements, use computer tools to approximate the data of a physical experiment, carry out statistical verification of the accuracy of various methods measurements, carry out analysis of residuals, check the adequacy of the selected model

Full-time form

1. Know the basic foundations of mathematical analysis and probability theory. 2. To be able to apply previous knowledge from the courses of mathematical analysis, probability theory to the array of data.

The course "Statistical methods of processing experimental results" examines modern approaches to the description of experimental results and their extension theoretical knowledge and development of students' practical skills regarding the analysis of measurement errors of physical quantities, finding confidence intervals, statistical testing of hypotheses, correlation analysis, choosing the best model. The purpose of studying the discipline is to master the basic knowledge of probability theory and mathematical statistics with the aim of applying them to process the results of a physical experiment and choose the best model. Educational the task of the course is to master the basic methods of analyzing errors in measuring physical quantities, finding confidence intervals, statistical testing of hypotheses, correlation analysis, and choosing the best model. Learning outcomes are the ability to find probabilities, mathematical expectation, variance, and root mean square deviation of continuous and discrete random variables, as well as to approximate experimental data using the least squares method in the case of equal and unequal measurements.

1. Bilotserkivskyi O. B. Probability theory and mathematical statistics: workshop / O. B. Bilotserkivskyi; National technical University "Kharkiv Polytechnic Institute". – Kharkiv: NTU "KhPI", 2018. – 170 p. 2. Pokrovsky, E. O., Pokrovsky, S. E., & Savchuk, O. V. (2021). Probability Theory and Mathematical Statistics.. 3. Tyurin, O. V., Akhmerov, O. Yu., Tyurin, A. V., & Akhmerov, A. Yu. (2019). Probability Theory and Mathematical Statistics. 4. Gupta, S. C., & Kapoor, V. K. (2020). Fundamentals of mathematical statistics. Sultan Chand & Sons. 1344 pages. 5. Kondilenko O.Sh., Mishchenko M.I. Measurement errors of physical quantities. Guidelines. - Kyiv, 2018. 6. Bobyk O.I., Berehova G.I., Kopytko B.I. Probability theory and mathematical statistics.-Kyiv "Professional", 2017.

The total volume is 90 hours, including: Lectures - 16 hours. Practical classes - 16 hours. Independent work - 58 hours

- semester assessment: 1. Survey during the first content module (10 points). 2. Modular control work 1 (30 points). 1. Survey during the second content module (10 points). 2. Modular control work 2 (30 points). - final evaluation in the form of credit (20 points) A student is not allowed to take credit if he scored less than 50 points during the semester The credit score cannot be less than 10 points to obtain an overall positive score for the course.

Ukrainian