Introduction to Experimental Data Handling
Fizikus mérnök BSc
Number of lectures per week:
Number of practices per week:
Number of laboratory exercises per week:
Further knowledge transfer methods:
Special grading methods:
Dr. Dávid Légrády, associate professor, PhD
Lecturers and instructors:
Basic concepts of probability theory. Measurement result, distribution function, mean, standard deviation, covariance. Poisson distribution, Gaussian distribution, Student distribution, chi-square distribution, confidence intervals. Parameter estimation. Concept of statistics, estimated parameters. Properties of estimates: unbiasedness, efficiency, consistency. Least squares method. Normal equations and their solution. Estimation of the standard deviation of estimated parameters. Examples for evaluating measurements from different fields of physics. Linear regression. Smoothing curves. Handling nonlinear joints, iteration. Corrections, e.g. dead time correction. Basic metrological concepts. Systematic and statistical error. Consideration of corrections. Concept of measurement uncertainty, estimation methods. Examples of the format for presenting measurement results. Creating graphs. Erroneous measurements. Detection and management of scattered-out points. Laboratory measurement evaluation tasks.
John Mandell:Tha Statistical Analysis of Experimental Data, Dover Books on Mathematics, 1984 ISBN 0486646661
List of competences:
Please find the detailed list, as quoted from the Hungarian training and outcome requirements of the Physicist Engineer program, in the Hungarian version of the course description.