Statistics

The objective is to provide mathematical knowledge and skills needed for qualitative and quantitative analyzing of concrete business/financial problems. The students learn the basic concepts of theory of probability and statistics, using Excel/GeoGebra.

None

1. Probability and statistics
   • Definition and basic concepts
   • Algebra of events
   • Random variables
   • Expected value, variance, median
   • Regression and correlation
   • Testing of statistical hypotheses:
   the process of testing hypotheses, basic and zero
   hypothesis, errors in testing hypotheses
   • Bivariate analysis: descriptive analysis of
   the relationship of two variables nominal
   and ordinal character (χ^2 test, Spearman
   correlation coefficient), the determination of
   numerical linear relationship of two
   variables (Pearson correlation coefficient,
   simple linear regression, the coefficient of
   determination).
   • Use computer programs Excel/GeoGebra for statistical analysis: displaying and editing data, calculating all the relevant parameters, testing hypotheses.

Knowledge and understanding:
By the end of this course students will be able to:
-select an appropriate statistical method for the analysis of a given dataset in Excel/GeoGebra and provide its interpretation, editing and displaying data
-calculate statistical parameters (expected value, average, variance, median, standard deviation, etc.)
-test statistical hypothesis
-determine the probability of an event

• A. Vadnjal, Elementarni uvod v verjetnostni račun, DZS, Ljubljana, 1979. Catalogue
• R. Jamnik, Matematika, DMFA, Ljubljana 1994. Catalogue
• M. Omladič, V. Omladič, Matematika in denar, Knjižnica Sigma, DMFA, Ljubljana, 1995.
• M. Omladič, D. Kobal, M. Jerman, Poslovna matematika, Visoka strokovna šola za podjetništvo, Portorož, 2002
• K. Košmelj, Uporabna statistika, Ljubljana: Univerza v Ljubljani, Biotehniška fakulteta, 2001. E-version
• F.M. Dekking, et al., A modern introduction to Probability and Statistics, Springer 2005. E-version

Seminar work and homeworks, Written exam 30/70

Principal education and research areas:

Theoretical and Applied Operations Research (OR); Data Analytics;
Applied Mathematics; Industrial Engineering; Optimization; Uncertainty Theory

Professional career:
Asst. Prof. Ahmad Hosseini did his PhD at Saarland University (Germany) and Sabanci University (Turkey) in Industrial Engineering-Operations Research (with a minor in Computer Science). After his doctoral studies, he participated in two national projects in Industrial Optimization, Logistics, Systems Engineering, and Forest Engineering (as a postdoctoral research associate) in different departments of Umeå University (Sweden), where he was also a lecturer/supervisor in the Industrial Engineering, Management, and Mathematics.
He is primarily interested in interdisciplinary research at the intersection of Industrial Engineering, Operational Research (OR), Computer Science, Applied Mathematics, and Data Analytics, with a focus on theory, design, and implementation. In particular, he is interested in modeling and solving combinatorial & industrial optimization problems in Supply Chains, System Engineering, Manufacturing, Logistics & Distribution, Forest Engineering/Planning, and Transportation. His research is mainly based on mathematical programming techniques, network optimization algorithms, heuristics, and a combination of heuristics/metaheuristics with exact/approximation/stochastic optimization techniques.
He is currently an Assistant Professor at the School of Engineering and Management (PTF) and the School of Viticulture and Enology at the University of Nova Gorica, being affiliated with the Centre for Information Technologies and Applied Mathematics.

Selected bibliography

1. ČESNIK, Urban, MARTELANC, Mitja, OVSTHUS, Ingunn, RADOVANOVIĆ, Tatjana, HOSSEINI, Ahmad, MOZETIČ VODOPIVEC, Branka, BUTINAR, Lorena.
   Functional characterization of Saccharomyces yeasts from cider produced in Hardanger.
   Fermentation. 2023, vol. 9, issue 9, [article no.] 824, str. 1-27. ISSN 2311-5637.
   DOI: 10.3390/fermentation9090824. [COBISS.SI-ID 164729091], [JCR, SNIP, Scopus]

2. HOSSEINI, Ahmad, WADBRO, Eddie, NGOC DO, Dung, LINDROOS, Ola
   A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry. Computers and electronics in agriculture. [Print ed.]. Sep. 2023, vol. 212, [article no.] 108059, str. 1-13, ilustr. ISSN 0168-1699. DOI: 10.1016/j.compag.2023.108059. [COBISS.SI-ID 159537155], [JCR, SNIP, WoS, Scopus]

3. HOSSEINI, Ahmad, WADBRO, Eddie.
   A hybrid greedy randomized heuristic for designing uncertain transport network layout. Expert systems with applications. [Print ed.]. Mar. 2022, vol. 190, [article no.] 116151, str. 1-10, ilustr. ISSN 0957-4174.
   DOI: 10.1016/j.eswa.2021.116151. [COBISS.SI-ID 141605891], [JCR, SNIP, WoS, Scopus]

4. HOSSEINI, Ahmad, PISHVAEE, Mir Saman.
   Capacity reliability under uncertainty in transportation networks: an optimization framework and stability assessment methodology. Fuzzy optimization and decision making. Sep. 2022, vol. 21, iss. 3, str. 479–512, ilustr. ISSN 1568-4539. DOI: 10.1007/s10700-021-09374-9. [COBISS.SI-ID 141610755], [JCR, SNIP, WoS, Scopus]

5. NAKHAEI, Niknaz, EBRAHIMI, Morteza, HOSSEINI, Ahmad.
   A solution technique to cascading link failure prediction. Knowledge-based systems. [Print ed.]. Dec. 2022, vol. 258, [article no.] 109920, str. 1-14, ilustr. ISSN 0950-7051.
   DOI: 10.1016/j.knosys.2022.109920. [COBISS.SI-ID 141591555], [JCR, SNIP, WoS, Scopus]

6. HOSSEINI, Ahmad, PISHVAEE, Mir Saman.
   Extended computational formulations for tolerance-based sensitivity analysis of uncertain transportation networks.
   Expert systems with applications. [Print ed.]. Nov. 2021, vol. 183, [article no.] 115252, str. 1-19, ilustr. ISSN 0957-4174. DOI: 10.1016/j.eswa.2021.115252. [COBISS.SI-ID 141608451], [JCR, SNIP, WoS, Scopus]

7. HOSSEINI, Ahmad, LINDROOS, Ola, WADBRO, Eddie.
   A holistic optimization framework for forest machine trail network design accounting for multiple objectives and machines. Canadian journal of forest research. 2019, vol. 49, no. 2, str. 111-120, ilustr. ISSN 0045-5067.
   DOI: 10.1139/cjfr-2018-0258. [COBISS.SI-ID 141605635], [JCR, SNIP, WoS, Scopus]