# MZM1 - Mathematical processing of experimental data

Course specification | ||||
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Course title | Mathematical processing of experimental data | |||

Acronym | MZM1 | |||

Study programme | ||||

Module | ||||

Lecturer (for classes) | ||||

Lecturer/Associate (for practice) | ||||

Lecturer/Associate (for OTC) | ||||

ESPB | 5.0 | Status | ||

Condition | Credits from courses equivalent to Mathematics I and Mathematics II | Облик условљености | ||

The goal | The 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 outcome | This 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 lectures | Probability – 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 exercises | Solving 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 | ||||

- Tom M. Apostol, Calculus, volume II, Blaisdell Publishing Company, 1964
- Bertsekas, Tsitsiklis, Introduction to Probability , MIT lecture notes, 2000
- Hogg, Craig, Introduction to Mathematical Statistics, Macmillan, 1978
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Number of hours per week during the semester/trimester/year | ||||

Lectures | Exercises | OTC | Study and Research | Other classes |

2 | 2 | 0 | ||

Methods of teaching | Lectures | |||

Knowledge score (maximum points 100) | ||||

Pre obligations | Points | Final exam | Points | |

Activites during lectures | Test paper | 60 | ||

Practical lessons | Oral examination | |||

Projects | ||||

Colloquia | 40 | |||

Seminars |