MATH203 Introduction to Probability and Statistics
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 | ||||||
| Prerequisite | MATH101 | It is a prerequisite to | ||||||||
| 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 | Assistant E-mail | |||||||||
| Course Objectives | This course is designed to promote understanding and knowledge of statistical methods and concepts used in engineering and natural sciences. Students will be introduced to a wide range of statistical techniques for analyzing data. Students will learn how, when and why statistics are used and why it is necessary to understand them. The topics to be studied are conceptualization, operationalization, and measurement of phenomena from their applied area of studies. Students will learn how to summarize data with graphs and numbers, make generalizations about populations based on samples of the population, and describe the relationships between variables. Students are not expected to become expert statisticians, but they are expected to gain an understanding of how statistics can be used to contribute to their scientific argumentation and for other more general types of questions. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Students will also gain basic familiarity with the statistical software package R. | |||||||||
| Textbook | Probability, Random Variables and Stochastic Processes, Papoulis, Mc Graw Hill | |||||||||
| 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) | |||||||||
| Week 2 | Introduction to Random variables and axioms of probability | |||||||||
| Week 3 | Probability density function; Experimental design; | |||||||||
| Week 4 | Random Processes | |||||||||
| Week 5 | Defining dirac delta function. | |||||||||
| Week 6 | Understanding the importance of measuring variability (range, interquartile range, the variance, and the standard deviation) | |||||||||
| Week 7 | Sample spaces and probability; Addition and Multiplication rules; | |||||||||
| Week 8 | Midterm | |||||||||
| Week 9 | Continuous Probability Distributions | |||||||||
| Week 10 | Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation) | |||||||||
| Week 11 | Joint and Conditional Distributions | |||||||||
| Week 12 | Joint and Conditional Distributions | |||||||||
| Week 13 | Project Presentations | |||||||||
| Week 14 | Project Presentations | |||||||||
| Week 15 | Project Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs |
| Final Exam | 1 | 30 | 3,5 | |
| Semester Evaluation Components | ||||
| Midterm Exam | 1 | 30 | 2 | |
| Project | 1 | 25 | 1,4 | |
| HW | 1 | 15 | 2 | |
| *** ECTS Credit Calculation *** | ||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Active tutorials | 2 | 12 | 24 | |||
| Home study | 4 | 14 | 56 | In-term exam study | 12 | 1 | 12 | |||
| Final Exam study | 13 | 1 | 13 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 13/02/2024 | |||||||||