MATH203 Introduction to Probability and Statistics
MATH203 Introduction to Probability and Statistics
Syllabus | International University of Sarajevo - Last Update on Oct 10, 2026
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Faculty of Engineering and Natural Sciences
Leila Miller
Course Lecturer
Course Objectives
The aims of this course are to become familiar with basic concepts of probability theory and, by the end of the class, to be able to imagine and explain how these concepts help us to make data driven decisions. To learn the language of probability theory and us it appropriately. Understand basic principles of statistical inference and become an informed consumer of statistical information.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
For the first half of teh semester we will use Elementary Statistics: A Step-by-Step Approach with Formula Card 9/E by Allan Bluman. For the second part there are plenty of online resources that are just about as good as any textbook … and that cost $0. Some good examples: http://math.dartmouth.edu/~prob/prob/prob.pdf
Additional Literature
http://onlinestatbook.com/2/index.html http://davidmlane.com/hyperstat/ http://vassarstats.net/textbook/ http://www.jerrydallal.com/LHSP/LHSP.html http://www.sportsci.org/resource/stats/Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Tutorials that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course description and presentation (Objectives, requirements, rules, students rights and responsibilities; Types of data, concepts of a popuation and a sample; different sampling designs; Uses and misuses of statistics in everyday life; Introduction to different study designs; | CH1 Bluman |
| 2 | Organizing data for visual presentation; Common statistical graphs and their interpretation; Reading and commenting on frequency distributions in graphical and tabular form; | CH2 Bluman |
| 3 📍 | Summarizing data using measures of central tedency, variability as well as measures of position (percentiles) to draw conclusions from the data. | CH3 Bluman |
| 4 | Using measures of position to draw graphs (Boxplots, Histograms) of data and based on their measures of position making conclusions on their shape | CH3 Bluman |
| 5 | Basic probabilistic concepts: events, probabilities, random variables, event spaces, random subsets, sampe spaces; Concept of a uniform disttibution on a countable finite discrete sampe space; | Slides, CH4 Bluman, Dartmouth Book |
| 6 | Discrete random variables and uniform distribution on a discrete/countable state space; Independent events together with union and intersection of events, calculations with probabilities (inclusion / exclusion formula), Indicator variables. | Slides, CH5 Bluman, Dartmouth Book |
| 7 | Sample spaces and probability; Addition and Multiplication rules; | Slides, CH4 Bluman, Dartmouth Book |
| 8 | Counting techniques; (Tree diagrams, Fundamental Rules of Counting); Relationship of counting and discrete sample spaces. Probability of an event as a result of counting; (MIDTERM) | Slides, CH5 Bluman, Dartmouth Book |
| 9 | Common probability distributions (Uniform, Binomial, Poisson, Multinomial, Hypergeometric…) | Slides, CH5 Bluman, Dartmouth Book |
| 10 | Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation) | Slides, CH5 Bluman, Dartmouth Book |
| 11 | Common probability distributions (Uniform, Binomial, Multinomial, Hypergeometric…) | Slides, CH5 Bluman, Dartmouth Book |
| 12 | Concept of expectation of a random variable and characteristics of expectation (linearity, additivity, interpretation (Law of Large Numbers). Also, expectation for common discrete probability distributions. | Slides, Dartmouth Book |
| 13 | Expectation (revisited), variance, covariance, correlation for most commoly encountered discrete probability distributions (Binomial, Hypergeometric, Poisson …) | Slides, Dartmouth Book |
| 14 | Continuous random variables and a notion of a density function and a distribution function (uniform distribution on a unit interval, unit square …, exponential distribution, standard normal distribution will be extensively treated). | Slides, Dartmouth Book |
| 15 | Central Limit Theorem, Normal Distribution and Law of Large numbers | Slides, CH5 Bluman, Dartmouth Book |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| MATH203.1 | Course | Monday 09:00 - 11:50 | A F1.24 - Amphitheater I | - | - |
| MATH203.2 | Course | Tuesday 09:00 - 11:50 | A F1.24 - Amphitheater I | - | - |
| MATH203.1 | Tutorial | Thursday 13:00 - 14:50 | B F1.8 | - | - |
| MATH203.2 | Tutorial | Tuesday 18:00 - 19:50 | A F1.24 - Amphitheater I | - | - |
| MATH203.3 | Tutorial | Wednesday 09:00 - 10:50 | B F2.15 - Amphitheater II | - | - |
| MATH203.4 | Tutorial | Friday 14:00 - 15:50 | B F1.23 - Amphitheater I | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 3 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 2
Oral presentation of a self-study solution
AI: Not AllowedAlignment with Learning Outcomes : 1 4
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
26 hours ⏳ (13 week × 2 h)
Home study
56 hours ⏳ (14 week × 4 h)
In-term exam study
10 hours ⏳ (1 week × 10 h)
Final Exam study
13 hours ⏳ (1 week × 13 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 [MATH203] 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 Oct 10, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| MATH203 | Introduction to Probability and Statistics | 3 | 2 | 6 | Monday 09:00-11:00 (Section1), Tuesday 09:00-11:00 (Section2) | |||||
| Prerequisite | MATH101 | It is a prerequisite to | IE301, IE306, MATH306 | |||||||
| Lecturer | Leila Miller | Office Hours / Room / Phone | Monday: 12:00-14:00 Tuesday: 11:00-12:00 Wednesday: 13:00-14:00 Thursday: 10:00-11:00 |
|||||||
| lmiller@ius.edu.ba | ||||||||||
| Assistant | Ilma Tarhanis-Papic | Assistant E-mail | itarhanis-papic@ius.edu.ba | |||||||
| Course Objectives | The aims of this course are to become familiar with basic concepts of probability theory and, by the end of the class, to be able to imagine and explain how these concepts help us to make data driven decisions. To learn the language of probability theory and us it appropriately. Understand basic principles of statistical inference and become an informed consumer of statistical information. | |||||||||
| Textbook | For the first half of teh semester we will use Elementary Statistics: A Step-by-Step Approach with Formula Card 9/E by Allan Bluman. For the second part there are plenty of online resources that are just about as good as any textbook … and that cost $0. Some good examples: http://math.dartmouth.edu/~prob/prob/prob.pdf | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Tutorials that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course description and presentation (Objectives, requirements, rules, students rights and responsibilities; Types of data, concepts of a popuation and a sample; different sampling designs; Uses and misuses of statistics in everyday life; Introduction to different study designs; | CH1 Bluman | ||||||||
| Week 2 | Organizing data for visual presentation; Common statistical graphs and their interpretation; Reading and commenting on frequency distributions in graphical and tabular form; | CH2 Bluman | ||||||||
| Week 3 | Summarizing data using measures of central tedency, variability as well as measures of position (percentiles) to draw conclusions from the data. | CH3 Bluman | ||||||||
| Week 4 | Using measures of position to draw graphs (Boxplots, Histograms) of data and based on their measures of position making conclusions on their shape | CH3 Bluman | ||||||||
| Week 5 | Basic probabilistic concepts: events, probabilities, random variables, event spaces, random subsets, sampe spaces; Concept of a uniform disttibution on a countable finite discrete sampe space; | Slides, CH4 Bluman, Dartmouth Book | ||||||||
| Week 6 | Discrete random variables and uniform distribution on a discrete/countable state space; Independent events together with union and intersection of events, calculations with probabilities (inclusion / exclusion formula), Indicator variables. | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Week 7 | Sample spaces and probability; Addition and Multiplication rules; | Slides, CH4 Bluman, Dartmouth Book | ||||||||
| Week 8 | Counting techniques; (Tree diagrams, Fundamental Rules of Counting); Relationship of counting and discrete sample spaces. Probability of an event as a result of counting; (MIDTERM) | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Week 9 | Common probability distributions (Uniform, Binomial, Poisson, Multinomial, Hypergeometric…) | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Week 10 | Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation) | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Week 11 | Common probability distributions (Uniform, Binomial, Multinomial, Hypergeometric…) | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Week 12 | Concept of expectation of a random variable and characteristics of expectation (linearity, additivity, interpretation (Law of Large Numbers). Also, expectation for common discrete probability distributions. | Slides, Dartmouth Book | ||||||||
| Week 13 | Expectation (revisited), variance, covariance, correlation for most commoly encountered discrete probability distributions (Binomial, Hypergeometric, Poisson …) | Slides, Dartmouth Book | ||||||||
| Week 14 | Continuous random variables and a notion of a density function and a distribution function (uniform distribution on a unit interval, unit square …, exponential distribution, standard normal distribution will be extensively treated). | Slides, Dartmouth Book | ||||||||
| Week 15 | Central Limit Theorem, Normal Distribution and Law of Large numbers | Slides, CH5 Bluman, Dartmouth Book | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 45 | 3,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 40 | 2 | Not Allowed | |
| Oral presentation of a self-study solution | 1 | 15 | 1,4 | 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 | 13 | 26 | |||
| Home study | 4 | 14 | 56 | In-term exam study | 10 | 1 | 10 | |||
| Final Exam study | 13 | 1 | 13 | 0 | ||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 12/10/2026 | |||||||||