Faculty of Technical Sciences

Subject: Statistics (17.SE001)

General information:
 
Category Professional-applicative
Scientific or art field Teorijska i primenjena matematika
ECTS 5

Training students in abstract thinking and basic knowledge in Probability and Mathematics Statistics. Students should be able to understand and apply statistical methods on large data, particularly in informatics. Importance is in practical knowledge on quantitative approach to problems, so a statistical software is used. Aim is to train students in choosing a statistical method, making statistical analysis and presenting it in best possible way.

Student is competent to design and solve mathematical models in the field of probability and statistics in further education and professional courses.

Basic definitions in probability, conditional probability and Bayes’ formula. Random variable of discrete and continuous type, distribution functions. Two dimensional random variable. Numerical characteristics – expectation, dispersion, covariance, correlation. Limit theorems. Population, sample. Sampling techniques. Descriptive statistics, point estimate and interval estimate. Parametric and nonparametric hypotheses and significance testing. Statistical inference. Regression analysis, linear, nonlinear and logistic regression. Statistical data visualization, graphing data. Statistical models in Computer Science. Statistical package.

Lectures; Numerical computing practice. Consultations. In lectures, theoretical part of the course is followed by typical examples for better understanding. In practice, which accompanies lectures, typical problems are solved and knowledge from the lectures is deepened. Besides lectures and practice, consultations are held on a regular basis. Part of the course, presenting a logical whole, can be passed during the teaching process in the form of the following 2 modules (the first module: probability, the second module: statistics). The oral part of the examination is obligatory.

Authors Title Year Publisher Language
W. N. Venables, D. M. Smith and the R Core Team An Introduction to R 2017 R Core Team English
Chihara L., Hesterberg T Mathematical Statistics with Resampling and R 2011 John Wiley & Sons, Ltd English
Course activity Pre-examination Obligations Number of points
Computer excersise defence Yes Yes 10.00
Computer excersise defence Yes Yes 10.00
Written part of the exam - tasks and theory No Yes 50.00
Test Yes Yes 10.00
Oral part of the exam No Yes 20.00
API Image

Asst. Prof. Ovcin Zoran

Assistant Professor

Lectures
API Image

Prof. Mihailović Biljana

Full Professor

Lectures
API Image

Assoc. Prof. Ivetić Jelena

Associate Professor

Lectures

Assistant - Master Lukić Dunja

Assistant - Master

Practical classes

Assistant - Master Ciganović Radojka

Assistant - Master

Practical classes

Asst. Prof. Ilić Vladimir

Assistant Professor

Practical classes

Assistant - Master Kiš Maria

Assistant - Master

Practical classes

Assistant - Master Milošević Stepan

Assistant - Master

Computational classes

Assistant - Master Palalić Tamara

Assistant - Master

Computational classes

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

Contact:

Address: Trg Dositeja Obradovića 6, 21102 Novi Sad

Phone:  (+381) 21 450 810
(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.