Subject: Statistical methods (17 - Z203)

Basic Information

Scientific or art field:Teorijska i primenjena matematika
Native organizations units

Department of Fundamentals Sciences
Course specification

Course is active from 01.10.2005..

Course which have preconditioned courses Statistical methods

Course idMandatoryMandatory
Advanced Statistical ModellingYesNo
Enabling students for abstract thinking and acquisition of basic knowledge in the field of Probability and Mathematical Statistics. The course objective is to develop special way of thinking in students while studying massive phenomena in the field of environmental engineering. The course character is applicational and the importance is given to the knowledge which can explain quantitative approach to the issues from the field of study. Students are also able to use statistical programs. The objective is to enable students to choose adequate statistical methods, to do statistical analysis and to essentially elaborate it. This knowledge is the foundation for better understanding of the professional literature and for successful advancement in studies.
The student should use acquired knowledge in further education and in professional courses. He/she can make and solve mathematical models using the knowledge acquired in this course. Mastering theoretical knowledge in the field of probability and mathematical statistics studied in this course and skills of calculating and analyzing calculated statistical indicators.
Theoretical lectures: Probability: Axioms of probability. Conditional probability. Bayes formula. Random variable of discrete and continuous type. Random vector of discrete type and common distribution. Conditional distribution. Transformation of random variables. Mathematical expectation. The variance and standard deviation. Moments. Covariance, correlation coefficient. Conditional expectations. Large numbers law. Central limit and linear theorem. Correlation and linear regression. Sample distribution, the mean value and dispersion. Statistics: basic concepts. Population, sample. Statistics. Descriptive statistical analysis (basic concepts, data editing, table and graphic presentation of data, data analysis using methods of descriptive statistics, software support to statistical analysis). Assessment of unknown parameters (point assessment: The method of moments and maximum likelihood method. Interval rates). Parametric and nonparametric hypothesis and tests. Practical lecture (practice): During the lectures adequate examples from theoretical lectures are done, thus practicing the knowledge and contributing to the better understanding of the lectured knowledge.
Lectures: Numerical computing practice, computer practice. Consultations. Lectures are combined. During the lectures theoretical part of the course followed by characteristic examples are presented for better understanding of the lectured material. During the practice, which accompanies lectures, typical problems are solved and the knowledge from the lectures is deepened. During the computer practice processing of obtained data is done using the statistical software. Besides lectures and practice, consultations are held on a regular basis. A part of the course, which represents a logical whole, can be taken during the teaching process in the form of the next two modules (the first module: Probability; the second module: Statistics. In order to take the final examination, the student has to complete computer practice.
Stojaković, M.Matematička statistika2000Fakultet tehničkih nauka, Novi SadSerbian language
Jevremović, V., Mališić, J.Statističke metode u metorologiji i inženjerstvu2002Savezni hidrometorološki zavod, BeogradSerbian language
Kovačević, I., Novković, M.Matematičke metode 4 - skripta1999neautorizovana skripta, Novi SadSerbian language
Novković, M., Rodić, B., Kovačević, I.Zbirka rešenih zadataka iz verovatnoće i statistike2004Fakultet tehničkih nauka, Novi SadSerbian language
Grupa autoraZbirka rešenih zadataka iz statistike2005Fakultet tehničkih nauka, Novi SadSerbian language
Course activity Pre-examination ObligationsNumber of points
Complex exercisesYesYes15.00
Exercise attendanceYesYes3.00
Final exam - part oneNoNo50.00
Final exam - part twoNoNo50.00
Written part of the exam - tasks and theoryNoYes50.00
Lecture attendanceYesYes2.00
Name and surnameForm of classes
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Gilezan Silvia
Full Professor

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Vuković Manojlo
Assistant - Master

Practical classes
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Dragić Đorđe
Assistant - Master

Practical classes
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Prokić Aleksandar
Assistant - Master

Computational classes
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Prodanović Irena
Assistant - Master

Computational classes