MATH306 Statistical Modeling
MATH306 Statistical Modeling
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Faculty of Engineering and Natural Sciences
Özge Büyükdağlı
Course Lecturer
Course Objectives
The aims of this course are to study common statistical techniques. The emphasis will be upon the understanding and use of statistical methodology, and the written communication of the results of data analysis. Students should gain practical experience in elementary data management and analysis techniques. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Upon completion of the course students should be able to use data to make recommedations and make infromed decisions regarding any process or phenomenon for which it is possible to collect data.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Modern Mathematical Statistics with Applications, 3rd Editio, by Jay L. Devore, Kenneth N. Berk, Matthew A. Carlton
Additional Literature
Applied Statistics and Probability for Engineers, 7th Edition, by D. C. Montgomery and G. C. Runger, Wiley, 2018Teaching Methods
Lecture slides that serve as a startig point for class discussions with examples
Active tutorial sessions for engaged learning
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to the course, probability review | |
| 2 | Overview and Descriptive Statistics | Chapter 1 |
| 3 | Statistics and Sampling Distributions | Chapter 6 |
| 4 | Point Estimation | Chapter 7 |
| 5 | Statistical Intervals Based on a Single Sample | Chapter 8 |
| 6 | Tests of Hypotheses Based on a Single Sample | Chapter 9 |
| 7 | Inferences Based on Two samples | Chapter 10 |
| 8 | Midterm | |
| 9 | Inferences Based on Two samples (cont. ) | Chapter 10 |
| 10 | Regression and Correlation | Chapter 12 |
| 11 | Regression and Correlation (cont. ) | Chapter 12 |
| 12 | Multiple Regression Analysis | Chapter 12 |
| 13 | Chi-Squared Tests: Goodness-of-Fit Tests | Chapter 13 |
| 14 | Chi-Squared Tests: Categorical Data Analysis | Chapter 13 |
| 15 | Review |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| MATH306.1 | Course | Wednesday 15:00 - 17:50 | A F1.26 | - | - |
| MATH306.1 | Tutorial | Friday 09:00 - 10:50 | A F1.11 | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 5
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
45 hours ⏳ (15 week × 3 h)
Active Tutorials
28 hours ⏳ (14 week × 2 h)
Home Study
28 hours ⏳ (14 week × 2 h)
In-term Exam Study
34 hours ⏳ (2 week × 17 h)
Final Exam Study
15 hours ⏳ (1 week × 15 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [MATH306] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Feb 02, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| MATH306 | Statistical Modeling | 3 | 2 | 6 | ||||||
| Prerequisite | MATH203 | It is a prerequisite to | - | |||||||
| Lecturer | Özge Büyükdağlı | Office Hours / Room / Phone | ||||||||
| obuyukdagli@ius.edu.ba | ||||||||||
| Assistant | Adin Jahic | Assistant E-mail | ajahic@ius.edu.ba | |||||||
| Course Objectives | The aims of this course are to study common statistical techniques. The emphasis will be upon the understanding and use of statistical methodology, and the written communication of the results of data analysis. Students should gain practical experience in elementary data management and analysis techniques. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Upon completion of the course students should be able to use data to make recommedations and make infromed decisions regarding any process or phenomenon for which it is possible to collect data. | |||||||||
| Textbook | Modern Mathematical Statistics with Applications, 3rd Editio, by Jay L. Devore, Kenneth N. Berk, Matthew A. Carlton | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Lecture slides that serve as a startig point for class discussions with examples. Active tutorial sessions for engaged learning. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to the course, probability review | |||||||||
| Week 2 | Overview and Descriptive Statistics | Chapter 1 | ||||||||
| Week 3 | Statistics and Sampling Distributions | Chapter 6 | ||||||||
| Week 4 | Point Estimation | Chapter 7 | ||||||||
| Week 5 | Statistical Intervals Based on a Single Sample | Chapter 8 | ||||||||
| Week 6 | Tests of Hypotheses Based on a Single Sample | Chapter 9 | ||||||||
| Week 7 | Inferences Based on Two samples | Chapter 10 | ||||||||
| Week 8 | Midterm | |||||||||
| Week 9 | Inferences Based on Two samples (cont. ) | Chapter 10 | ||||||||
| Week 10 | Regression and Correlation | Chapter 12 | ||||||||
| Week 11 | Regression and Correlation (cont. ) | Chapter 12 | ||||||||
| Week 12 | Multiple Regression Analysis | Chapter 12 | ||||||||
| Week 13 | Chi-Squared Tests: Goodness-of-Fit Tests | Chapter 13 | ||||||||
| Week 14 | Chi-Squared Tests: Categorical Data Analysis | Chapter 13 | ||||||||
| Week 15 | Review | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 30 | 1,2,3 | Not Allowed | |
| Quizzes | 2 | 30 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Active Tutorials | 2 | 14 | 28 | |||
| Home Study | 2 | 14 | 28 | In-term Exam Study | 17 | 2 | 34 | |||
| Final Exam Study | 15 | 1 | 15 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 17/02/2026 | |||||||||