Subject

Statistical modeling

Course Title

Statistical modeling
Statistical modeling

Code

F18L3S163

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

—

Prerequisites for enrolling in the course

Probability and Statistics or Business Statistics

10.

Objectives of the course programme (competences)

Students should learn to perform a proper and meaningful statistical analysis of data using both classical and Bayesian approaches. They should establish appropriate statistical models, test them, and interpret the resulting findings. The emphasis is on using open-source software (R, Python, etc.) to build models on real-world examples, drawing on the necessary theoretical results. The course should prepare students for other courses in which data from various studies are analyzed.

11.

Course content

All topics will be illustrated with appropriate real-life examples.
Introduction to data modeling. General methods for simulating random variables. Correlation and linear regression, estimating functional dependence from data, parametric models for the regression function. Simple linear regression. Analysis of variance (ANOVA) models.Multiple regression. Classification: logistic regression, binary and multiclass logistic regression. Classification model selection. Relations among variables and variable selection: principal component analysis, log-linear models. Nonparametric estimation of regression and classification functions: nearest neighbor method, naive Bayes method, Methods based on classification boundary estimation: support vector machines. Comparison of methods. Bayesian methods for parameter estimation and inference: Single-parameter Bayesian models, multi-parameter Bayesian models, data aggregation models for Bayesian modeling.

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lectures, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	Eighty points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 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.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature No. Author Title Publisher Year
22.2.	Additional literature No. Author Title Publisher Year