Statistical data Analysis

Последна измена: November 25, 2022
1. Course Title Statistical data Analysis
2. Code 4ФЕИТ08З013
3. Study program NULL
4. Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
5. Degree (first, second, third cycle) First cycle
6. Academic year/semester 7. Number of ECTS credits 6
8. Lecturer D-r Sanja Atanasova
9. Course Prerequisites
10. Course Goals (acquired competencies): The main purpose of this course is to provide students with an introduction to statistical data analysis. In addition to developing competencies in the topics, the course aims to further develop problem-solving skills using statistical terms and software packages and to provide a basis for applying the knowledge gained in previous mathematics courses. Upon successful completion of this course, the student will be able to effectively visualize and understand the data with the help of graphs, pie charts and histograms.
The student will also be able to determine and interpret confidence intervals for specific problems of interest and conduct a variety of hypothesis testing. The student will be able to use statistical packages in the problem solving process and interpret the results of the analysis of the collected data
11. Course Syllabus: Random variables. Distributions and transformations of random variables. Introduction to statistics. Collecting and analyzing data. Statistical software for data analysis.
Data visualization.
Confidence intervals.
Statistical tests.
Analysis of variance and covariance.
12. Learning methods: Lectures, presentations, classroom exercises, self-assessment projects
13. Total number of course hours 3 + 1 + 1 + 0
14. Distribution of course hours 180
15. Forms of teaching 15.1. Lectures-theoretical teaching 45
15.2. Exercises (laboratory, practice classes), seminars, teamwork 30
16. Other course activities 16.1. Projects, seminar papers 30
16.2. Individual tasks 45
16.3. Homework and self-learning 30
17. Grading 17.1. Exams 30
17.2. Seminar work/project (presentation: written and oral) 10
17.3. Activity and participation 0
17.4. Final exam 60
18. Grading criteria (points) up to 50 points 5 (five) (F)
from 51to 60 points 6 (six) (E)
from 61to 70 points 7 (seven) (D)
from 71to 80 points 8 (eight) (C)
from 81to 90 points 9 (nine) (B)
from 91to 100 points 10 (ten) (A)
19. Conditions for acquiring teacher’s signature and for taking final exam Аttend classes regularly and take tests
20. Forms of assessment During the semester, two partial written exams (at most 90 minutes each) are provided, at the middle and at the end of the semester, tests that are conducted during the classes and a project assignment. The student should prepare a project assignment and submit it by the end of the semester.
For students who have passed the partial exams and tests, a final oral exam may be conducted (maximum duration 60 min). The scores from the partial exams, tests, project assignment and the final oral exam are included in the final grade.
A written exam (maximum duration 135 min) is taken in the scheduled exam sessions. For students who have passed the written exam, a final oral exam can be conducted. The scores from the written exam and the final oral exam are included in the final grade.
21. Language Macedonian and English
22. Method of monitoring of teaching quality Self-evaluation and surveys
23. Literature
23.1. Required Literature
No. Author Title Publisher Year
1 Roxy Peck, Chris Olsen, Jay Devore Introduction to statistics and data analysis Thomson 2008
23.2. Additional Literature
No. Author Title Publisher Year
1 Hadley Wickham, Garrett Grolemund R for data science O’Reilly 2017
2 Jared P. Lander R for everyone Addison-Wesley 2017
3 Kenneth N.Berk, Patrick Carey Data Analysis with Microsoft Excel Brooks/Cole, Cengage Learning 2010
4 Darius Singpurwalla A handbook of Statistics Bookboon 2013