| 1. | Course Title | Fundamentals of Statistical Modelling | |||||||||||
| 2. | Code | 4ФЕИТ08018 | |||||||||||
| 3. | Study program | 7-NKS, 13-PMA | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Katerina Hadzi-Velkova Saneva | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
Acquiring knowledge about various statistical models and methods, with special reference to regression statistical techniques. The student is trained for statistical thinking, for choosing the most appropriate statistical model, for estimating assumptions, for using software for statistical modeling, as well as for interpreting the obtained results and drawing conclusions from the statistical analysis of practical problems. |
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| 11. | Course Syllabus:
Introduction to data modeling. Simple and multivariate linear regression. Confidence intervals and hypothesis testing for linear regression models. Generalized regression models. Logistic regression. Lasso regression. Polynomial regression. Bayesian linear regression. Application of optimization techniques in regression problems. Checking the adequacy of the model. Analysis of residues. Analysis of variance (ANOVA). Principal component analysis (PCA). |
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| 12. | Learning methods:
Lectures, seminar papers, project and independent assignments, self study. |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 0 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | points | |||||||||||
| 17.4. | Final exam | 50 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 57 points | 6 (six) (E) | ||||||||||||
| from 58 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Regular attendance of classes / consultations | |||||||||||
| 20. | Forms of assessment | Preparation and presentation of a seminar paper / project assignment; written exam. The written exam has a maximum duration of 120 minutes. | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | D. C. Мontgomery, E. A. Peck, G. G. Vining | Introduction to Linear Regression Analysis | Wiley; 5th edition | 2012 | |||||||||
| 2. | W. J. Krzanowski | An Introduction to Statistical Modelling | Wiley | 2010 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | P. Bruce, A. Bruce | Practical Statistics for Data Scientists | O’Reilly Media | 2017 | |||||||||
| 2. | A. J. Dobson, A. G. Barnett | An Introduction to Generalized Linear Models | CRC Press | 2018 | |||||||||
| 3. | B. Everitt | Introduction to Optimization Methods and their Application in Statistics | Springer | 2012 | |||||||||
