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M-AR-14-MSM1 - Matematicko statisticke metode 1

Course specification
Course title
Acronym M-AR-14-MSM1
Study programme
Module
Type of study
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 8.0 Status
      Condition Oblik uslovljenosti
      The goal The course should enable the student to acquire knowledge and understanding of sampling methods, inventory models, game theory, simulation modeling, organizational performance appraisals, and risk theory
      The outcome Skill application of methods of optimization and decision making and computer processing of set models and conclusion based on the obtained results.
      Contents
      Contents of lectures Introduction; Sample theory : methodology of data collection: sample and sampling, control of external influences; additional information on data, data types and measurement levels. Experimental research. Observation, questionnare and testing.
      Contents of exercises Practical classes are held for all areas.
      Literature
      1. Šomođi Š., Novković N., i dr. (2004): Uvod u naučni metod, Novi Sad. (Original title)
      2. Dejan Todorović:Osnovi metodologije psiholoških istraživanja, (1998), Beograd (Original title)
      3. Bergh, D. D., & Ketchen, D. J. J. (Eds.). (2009). Research Methodology in Strategy and Management. Bingley, UK: Emerald Group Publishing (Original title)
      4. Krčevinac S., Ćangalović Mirjana, Kovačević-Vujčić Vera, Martić M., Vujošević M., 2004. Operaciona istraživanja, Fakultet organizacionih nauka, Beograd (Original title)
      5. Cliff Ragsdale (2007): Spreadsheet modeling and decision analysis, 5th Edition , Virginia Polytechnic Institute and State University (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      3 3
      Methods of teaching Theoretical and practical teaching is held for all areas. Colloquiums follow practical lessons (total 2). Homework and data processing on a computer.
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons 15 Oral examination 55
      Projects
      Colloquia
      Seminars 30