ECON211 Business Statistics I
ECON211 Business Statistics I
Syllabus | International University of Sarajevo - Last Update on Jan 01, 2026
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Economics
Nedim Gavranović
Course Lecturer
Course Objectives
This course will help students understand the elementary probability theory and how to apply it to analyze statistical problems. It also provides an opportunity for students to understand how various mathematical knowledge and techniques which they have learned in different courses unite together to serve a common purpose.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran (2017). Statistics for Business and Economics, Thirteenth Edition. Cengage Learning®
Additional Literature
Anderson, D.R., Sweeney, D.J., and Williams, T.A. (2011). Essentials of Statistics for Business and Economics. South-Western CENGAGE Learning, 6th Edition Berenson, M.L., Levine, D.M., and Krehbiel, T.C. (2012). Basic Business Statistics: Concepts and Applications. Prentice Hall, 12th Edition.Teaching Methods
The methods include lectures (which may involve powerpoint presentation
Video
And audio aids)
Project
And class discussions.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introductory Lecture | |
| 2 | Data and Statistics | Chapter 1 |
| 3 | Descriptive Statistics: Tabular and Graphical Presentations | Chapter 2 |
| 4 | Descriptive Statistics: Tabular and Graphical Presentations | Chapter 2 |
| 5 | Quiz 1, Descriptive Statistics: Numerical Measures | Chapter 3 |
| 6 | Descriptive Statistics: Numerical Measures | Chapter 3 |
| 7 | Introduction to Probability | Chapter 4 |
| 8 | Midterm Exam | |
| 9 | Discrete Probability Distributions | Chapter 5 |
| 10 | Discrete Probability Distributions | Chapter 5 |
| 11 | Continuous Probability Distribution | Chapter 6 |
| 12 | Continuous Probability Distribution | Chapter 6 |
| 13 | Quiz 2, Sampling and Sampling Distributions | Chapter 7 |
| 14 | Student Presentation | |
| 15 | Student Presentation |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| ECON211.1 | Course | Tuesday 15:00 - 17:50 | B F2.5 | - | - |
| ECON211.2 | Course | Tuesday 15:00 - 17:50 | B F1.17 | - | - |
| ECON211.1 | Tutorial | Thursday 09:00 - 10:50 | B F2.8 | - | - |
| ECON211.2 | Tutorial | Thursday 14:00 - 15:50 | B F2.8 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 17:00 - 18:50 | A F1.27 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 3 5
Project
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2 4
Assignment
AI: Not AllowedAlignment with Learning Outcomes : 1 2 4 6
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)
Home Study
30 hours ⏳ (15 week × 2 h)
Project
30 hours ⏳ (1 week × 30 h)
Quiz
10 hours ⏳ (1 week × 10 h)
Assignment
10 hours ⏳ (1 week × 10 h)
Midterm Exam
10 hours ⏳ (1 week × 10 h)
Final Exam
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 [ECON211] 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 Jan 01, 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 | |||||||||
| ECON211 | Business Statistics I | 3 | 2 | 6 | ||||||
| Prerequisite | MATH100 | It is a prerequisite to | ECON221 | |||||||
| Lecturer | Nedim Gavranović | Office Hours / Room / Phone | Tuesday: 17:50-18:50 |
|||||||
| ngavranovic@ius.edu.ba | ||||||||||
| Assistant | Anes Kadić | Assistant E-mail | akadic@ius.edu.ba | |||||||
| Course Objectives | This course will help students understand the elementary probability theory and how to apply it to analyze statistical problems. It also provides an opportunity for students to understand how various mathematical knowledge and techniques which they have learned in different courses unite together to serve a common purpose. |
|||||||||
| Textbook | David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran (2017). Statistics for Business and Economics, Thirteenth Edition. Cengage Learning® | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The methods include lectures (which may involve powerpoint presentation, video, and audio aids), project, and class discussions. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introductory Lecture | |||||||||
| Week 2 | Data and Statistics | Chapter 1 | ||||||||
| Week 3 | Descriptive Statistics: Tabular and Graphical Presentations | Chapter 2 | ||||||||
| Week 4 | Descriptive Statistics: Tabular and Graphical Presentations | Chapter 2 | ||||||||
| Week 5 | Quiz 1, Descriptive Statistics: Numerical Measures | Chapter 3 | ||||||||
| Week 6 | Descriptive Statistics: Numerical Measures | Chapter 3 | ||||||||
| Week 7 | Introduction to Probability | Chapter 4 | ||||||||
| Week 8 | Midterm Exam | |||||||||
| Week 9 | Discrete Probability Distributions | Chapter 5 | ||||||||
| Week 10 | Discrete Probability Distributions | Chapter 5 | ||||||||
| Week 11 | Continuous Probability Distribution | Chapter 6 | ||||||||
| Week 12 | Continuous Probability Distribution | Chapter 6 | ||||||||
| Week 13 | Quiz 2, Sampling and Sampling Distributions | Chapter 7 | ||||||||
| Week 14 | Student Presentation | |||||||||
| Week 15 | Student Presentation | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1-6 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 25 | 3,5 | Not Allowed | |
| Project | 1 | 25 | 1-6 | Not Allowed | |
| Quiz | 1 | 10 | 1,2,4 | Not Allowed | |
| Assignment | 1 | 10 | 1,2,4,6 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Home Study | 2 | 15 | 30 | |||
| Project | 30 | 1 | 30 | Quiz | 10 | 1 | 10 | |||
| Assignment | 10 | 1 | 10 | Midterm Exam | 10 | 1 | 10 | |||
| Final Exam | 15 | 1 | 15 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 29/01/2026 | |||||||||