# MZM1 - Mathematical processing of experimental data

Course specification
Course titleMathematical processing of experimental data
AcronymMZM1
Study programme
Module
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB5.0Status
ConditionCredits from courses equivalent to Mathematics I and Mathematics IIОблик условљености
The goalThe goal of this course is to teach students basic concepts and theoroms from the following areas: Probability Theory, Mathematical Statistics and Methods of Deriving Empirical Formulae.
The outcomeThis course provides knowledge that can be applied to other natural science and technical-technological courses taught in the department. The course is intended to enable students to successfully apply the acquired mathematical knowledge in solving techical and technological problems, as well as in mathematical processing of experimental data. ; ;
Contents
Contents of lecturesProbability – definition, characteristics, total probability theorem, Bayes’ theorem, random variable, the most important discrete and continuous probability distributions, multidimensional random variables, the most important multidimensional distributions, numerical characteristics of distributions, numerical characteristics of multidimensional distributions, law of large numbers and central limit theorem of the calculus of probabilities; Statistics – random sample, examples of the most important statistics, tabular and graphical representation of statistical data, point estimation of distribution parameters, methods of obtaining point estimations , confidence intervals for parameters of normal distribution , parametric hypothesis testing, non-parametric tests, regression (linear, non-linear, multidimensional).
Contents of exercisesSolving examples and tasks that illustrate various concepts presented in the theoretical contens as well as their mutual relations. Moreover, the practical examples give an opportunity to exercise applying acquired theoretical knowledge to problems of natural and technical-technological sciences.
Literature
1. Tom M. Apostol, Calculus, volume II, Blaisdell Publishing Company, 1964
2. Bertsekas, Tsitsiklis, Introduction to Probability , MIT lecture notes, 2000
3. Hogg, Craig, Introduction to Mathematical Statistics, Macmillan, 1978
Number of hours per week during the semester/trimester/year
LecturesExercisesOTCStudy and ResearchOther classes
220
Methods of teachingLectures
Knowledge score (maximum points 100)
Pre obligationsPointsFinal examPoints
Activites during lecturesTest paper60
Practical lessonsOral examination
Projects
Colloquia40
Seminars