BIO413 Biostatistics
BIO413 Biostatistics
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Genetics and Bioengineering
Muhamed Adilović
Course Lecturer
Course Objectives
This course provides a rigorous foundation in biostatistical reasoning for students of natural sciences, emphasizing both conceptual understanding and analytical application in clinical and public health contexts. It integrates core statistical methodologies with medical informatics to prepare students for quantitative analysis of biomedical data, critical evaluation of scientific literature, and evidence-based decision-making in clinical research and practice.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Pagano, Marcello, Kimberlee Gauvreau, and Heather Mattie. Principles of biostatistics. Chapman and Hall/CRC, 2022.
Additional Literature
#Rosner, Bernard A. Fundamentals of biostatistics. Vol. 6. Belmont, CA: Thomson-Brooks/Cole, 2015. #Motulsky, Harvey. Intuitive biostatistics: a nonmathematical guide to statistical thinking. oxford university press, 2014.Teaching Methods
Interactive lectures
Class discussions
Problem solving
Case study analyses
Tutorials
And integration with the computer laboratory.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Foundations of Biostatistics and Role in Medical Science | Ch 1 |
| 2 | Descriptive Statistics and Data Visualization I | Ch 2 |
| 3 | Descriptive Statistics and Data Visualization II | Ch 2 |
| 4 | Principles of Statistical Inference and Sampling; Quiz I | Ch 5-8 |
| 5 | Hypothesis Testing: Concepts and Framework | Ch 9-10 |
| 6 | Parametric Tests: t-tests and Assumptions | Ch 11-12 |
| 7 | Nonparametric Tests: Robust Alternatives to t-tests | Ch 13 |
| 8 | Midterm Exam | |
| 9 | Chi-Square and Categorical Data Analysis | Ch 14-15 |
| 10 | Correlation Analysis and Interpretation | Ch 16 |
| 11 | Linear Regression and Predictive Modeling I | Ch 17 |
| 12 | Linear Regression and Predictive Modeling II; Quiz II | Ch 18 |
| 13 | Logistic Regression and Classification | Ch 19-20 |
| 14 | Research Design and Ethical Considerations in Medical Statistics | Ch 22 |
| 15 | Critical Appraisal of Medical Literature | Scientific Articles |
Course Schedule (All Sections)
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 08:00 - 11:00 | A F1.33 | |
| Wednesday | 08:00 - 11:00 | A F1.33 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Project
AI: Consult InstructorAlignment 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:
Theoretical Lecture
28 hours ⏳ (14 week × 2 h)
Practical Lecture
28 hours ⏳ (14 week × 2 h)
Midterm Exam
18 hours ⏳ (2 week × 9 h)
Quizzes
32 hours ⏳ (4 week × 8 h)
Final Exam
20 hours ⏳ (2 week × 10 h)
Project
24 hours ⏳ (3 week × 8 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 [BIO413] 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 Mar 03, 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 | |||||||||
| BIO413 | Biostatistics | 2 | 2 | 6 | Monday 13:00-14:50 | |||||
| Prerequisite | Junior Standing | It is a prerequisite to | - | |||||||
| Lecturer | Muhamed Adilović | Office Hours / Room / Phone | Tuesday: 8:00-11:00 Wednesday: 8:00-11:00 |
|||||||
| madilovic@ius.edu.ba | ||||||||||
| Assistant | Raneem Aldadah | Assistant E-mail | raldadah@ius.edu.ba | |||||||
| Course Objectives | This course provides a rigorous foundation in biostatistical reasoning for students of natural sciences, emphasizing both conceptual understanding and analytical application in clinical and public health contexts. It integrates core statistical methodologies with medical informatics to prepare students for quantitative analysis of biomedical data, critical evaluation of scientific literature, and evidence-based decision-making in clinical research and practice. |
|||||||||
| Textbook | Pagano, Marcello, Kimberlee Gauvreau, and Heather Mattie. Principles of biostatistics. Chapman and Hall/CRC, 2022. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Interactive lectures, class discussions, problem solving, case study analyses, tutorials, and integration with the computer laboratory. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Foundations of Biostatistics and Role in Medical Science | Ch 1 | ||||||||
| Week 2 | Descriptive Statistics and Data Visualization I | Ch 2 | ||||||||
| Week 3 | Descriptive Statistics and Data Visualization II | Ch 2 | ||||||||
| Week 4 | Principles of Statistical Inference and Sampling; Quiz I | Ch 5-8 | ||||||||
| Week 5 | Hypothesis Testing: Concepts and Framework | Ch 9-10 | ||||||||
| Week 6 | Parametric Tests: t-tests and Assumptions | Ch 11-12 | ||||||||
| Week 7 | Nonparametric Tests: Robust Alternatives to t-tests | Ch 13 | ||||||||
| Week 8 | Midterm Exam | |||||||||
| Week 9 | Chi-Square and Categorical Data Analysis | Ch 14-15 | ||||||||
| Week 10 | Correlation Analysis and Interpretation | Ch 16 | ||||||||
| Week 11 | Linear Regression and Predictive Modeling I | Ch 17 | ||||||||
| Week 12 | Linear Regression and Predictive Modeling II; Quiz II | Ch 18 | ||||||||
| Week 13 | Logistic Regression and Classification | Ch 19-20 | ||||||||
| Week 14 | Research Design and Ethical Considerations in Medical Statistics | Ch 22 | ||||||||
| Week 15 | Critical Appraisal of Medical Literature | Scientific Articles | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 24 | 1,2,3 | Not Allowed | |
| Quizzes | 2 | 18 | 1,2,3,4 | Not Allowed | |
| Project | 1 | 18 | 1,2,3,4,5 | Consult Instructor | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Theoretical Lecture | 2 | 14 | 28 | Practical Lecture | 2 | 14 | 28 | |||
| Midterm Exam | 9 | 2 | 18 | Quizzes | 8 | 4 | 32 | |||
| Final Exam | 10 | 2 | 20 | Project | 8 | 3 | 24 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||