| School/Faculty/Institute | Faculty of Engineering | ||||
| Course Code | MATH 227 | ||||
| Course Title in English | Probability and Statistics for Engineering I | ||||
| Course Title in Turkish | Mühendislik için Olasılık ve İstatistik I | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Introductory | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 174 hours per semester | ||||
| Number of Credits | 7 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | none | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only undergraduate students | ||||
| Overall Educational Objective | To learn and use the basic concepts of probability in solving real life problems. | ||||
| Course Description | This course provides an introduction to the theory of probability and probabilistic models. The aim is to qualify the students for decision making under uncertainty and solving industrial engineering problems. The course content involves basic concepts of probability, conditional probability, expectation and variance, discrete and continuous random variables, joint probability distributions and conditional expectation, moment generating functions and limit theorems. | ||||
| Course Description in Turkish | Bu ders olasılık olasılık modelleri ve teorisini giriş ersi olarak tasarlanmıştır. Dersin amacı belirsizlik altında karar verme ve endüstri mühendisliği problemlerini çözme konularında öğrencileri geliştirmektir. Ders içeriğini olasılığın temel kuralları, koşullu olasılık, beklendik değer ve varyans. Kesikli ve sürekli rassal değişkenler, birleşik dağılımlar, koşullu beklendik değer, momentler ve limit kuramları oluşturmaktadır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) solve real life probability problems using basic concepts of probability; 2) recognize and distinguish the properties of important discrete probability distributions; 3) recognize and distinguish the properties of important continuous probability distributions; 4) comprehend the properties of joint probability distributions of multivariate random variables. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||
| 6) Internalization and dissemination of professional ethical standards. | ||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | UTKU KOÇ , December 2020 |
| Course Coordinator | UTKU KOÇ |
| Semester | Fall |
| Name of Instructor | Prof. Dr. YANİ SKARLATOS |
| Week | Subject |
| 1) | Introduction to probability and randomness. Definition of sample spaces, events and probability, Counting rules |
| 2) | Conditional probability, independence, Bayes’ theorem, Random variables (RV), discrete RVs and distributions. |
| 3) | Functions of RVs. Expected value and variance of RVs. Important Discrete RVs (discrete uniform, Bernoulli, binomial)) |
| 4) | Important Discrete RVs (geometric, negative binomial, hypergeometric, Poisson) |
| 5) | Moment Generating functions of Discrete RVs |
| 6) | Continuous RVs. Cumulative distribution functions, mean, variance and generating functions for continuous RVs |
| 7) | Important Continuous RVS (uniform, normal) |
| 8) | Normal approximation to binomial and Poisson |
| 9) | Normal approximation to binomial and Poisson |
| 10) | Relationships between continuous and discrete RVs |
| 11) | Moment Generating functions of continuous RVs, Joint probability distributions, |
| 12) | Marginal and conditional distributions, Functions of RVs. |
| 13) | Independent RVs, conditional expectation, |
| 14) | Covariance, Correlation |
| 15) | Final Exam/Project Presentation period |
| 16) | Final Exam/Project Presentation period |
| Required/Recommended Readings | Required: Applied Probability and Statistics for Engineers, D.C. Montgomery, G.C. Runger, John Wiley & sons, 2011 Recommended: Probability and Statistics for Engineers, R. L. Sheaffer, M. Mulekar. J.T. McClave, Duxbury Press, 2010 Recommended: Probability and Statistics for Engineers, R. L. Sheaffer, J.T. McClave, Duxbury Press, 1994 | |||||||||
| Teaching Methods | Flipped classroom | |||||||||
| Homework and Projects | ||||||||||
| Laboratory Work | Students are expected to design, analyze and criticize the results of software experiments related with probability computations for standard distributions. | |||||||||
| Computer Use | Students are expected to design, analyze and criticize the results of software experiments related with probability computations for standard distributions. | |||||||||
| Other Activities | none | |||||||||
| Assessment Methods |
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| Course Administration |
utku.koc@mef.edu.tr; sirin.ozlem@mef.edu.tr Attendance/participation: No attendance required. Instructors may give extra homework or project in class. In this case, non-attending students may get questions from attendees. Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor and teaching assistant are only responsible for the information sent/received through Black Board system and emails using @mef account. The course instructor assumes that any information sent through email will be received in 24 hours, unless a system problem occurs. Grading and evaluation: Evaluation will be based on the student learning outcomes. 4 Midterm exams will be used to evaluate students on the announced dates. Missing Midterm: With an excuse justified by the student affairs, a single make-up exam will be given for all midterms. Academic integrity: All students of MEF University are expected to be honest and comply with academic integrity. Students are expected to do their own work and neither give nor receive unauthorized assistance. Disciplinary action will be taken in case of suspicion. |
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| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 3 | 2 | 98 | ||
| Laboratory | 14 | 1 | 1 | 28 | |||
| Midterm(s) | 4 | 10 | 2 | 48 | |||
| Total Workload | 174 | ||||||
| Total Workload/25 | 7.0 | ||||||
| ECTS | 7 | ||||||