Information Package / Course Catalogue
| Business Administration | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Econ., Admin. and Social Sciences | ||||
| Course Code | MATH 203 | ||||
| Course Title in English | Probability and Statistics for Social Sciences I | ||||
| Course Title in Turkish | Sosyal Bilimler için Olasılık ve İstatistik I | ||||
| Language of Instruction | EN | ||||
| Type of Course | Lecture | ||||
| Level of Course | Introductory | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 150 hours per semester | ||||
| Number of Credits | 6 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Knowledge of introductory level mathematical concepts | ||||
| Co-requisites | None | ||||
| Registration Restrictions | None | ||||
| Overall Educational Objective | To learn the basic concepts of probability and statistics, recognize and distinguish the properties of important distributions and apply probability and statistics concepts in solving real life economic and business problems. | ||||
| Course Description | This is first part of the year-round undergraduate course on probability and statistics for social sciences. Upon successful completion of the course, the participants are expected to be able to understand the idea of statistics and introduces useful methods for the collection, presentation, analysis, and interpretation of data. Some major topics are descriptive statistics, graphical description of data, measures of central tendency, dispersion and shape, probability, discrete and continuous random variables, discrete and continuous probability distributions. | ||||
| Course Description in Turkish | Bu ders ekonomi ve işletme öğrencileri için hazırlanmış olan temel istatistik dersinin ilk kısmıdır. Ders başarı ile tamamlandığında, öğrenci, tanımlayıcı istatistikler, verinin grafiksel tanımı, merkezi eğilim, dağılım ve şekil ölçütleri, kesitli ve sürekli rassal değişkenler, kesitli ve sürekli olasılık dağılımları konularını öğrenmiş olmalı ve öğrencinin bu konularda yorum yapabilme becerisini geliştirmiş olması beklenmektedir. |
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 (counting, combinatorial methods, conditional probability, and Bayes’ theorem). 2) Recognize and distinguish the properties of important discrete probability distributions and solve real life problems using appropriate distributions. Explain the use of (and solve problems related with) expectation and variances. 3) Recognize and distinguish the properties of important continuous probability distributions and solve real life problems using appropriate distributions. Explain the use of (and solve problems related with) expectation and variance. 4) Understand the properties of joint probability distributions of multivariate random variables including marginal/conditional distribution/expectation. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | ||||
| 2) Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | ||||
| 3) Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | ||||
| 4) Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | ||||
| 5) Demonstrates individual and professional ethical behavior and social responsibility | ||||
| 6) Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | ||||
| 7) Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | ||||
| 8) Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | ||||
| 9) Displays computer proficiency to support problem solving and decision-making | ||||
| 10) Demonstrates teamwork, leadership, and entrepreneurial skills | ||||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | H | |
| 2) | Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | S | |
| 3) | Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | N | |
| 4) | Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | N | |
| 5) | Demonstrates individual and professional ethical behavior and social responsibility | N | |
| 6) | Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | N | |
| 7) | Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | H | |
| 8) | Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | S | |
| 9) | Displays computer proficiency to support problem solving and decision-making | S | |
| 10) | Demonstrates teamwork, leadership, and entrepreneurial skills | S | |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | S |
| Prepared by and Date | NAROD ERKOL , December 2023 |
| Course Coordinator | NAROD ERKOL |
| Semester | Spring |
| Name of Instructor | Asst. Prof. Dr. NAROD ERKOL |
Course Contents
| Week | Subject |
| 1) | Syllabus, Introduction |
| 2) | Using graphs to describe data |
| 3) | Using graphs to describe data |
| 4) | Using numerical measures to describe data |
| 5) | Using numerical measures to describe data |
| 6) | Probability methods |
| 7) | Probability methods |
| 8) | Midterm exam |
| 9) | Discrete probability distributions |
| 10) | Discrete probability distributions |
| 11) | Discrete probability distributions |
| 12) | Continuous probability distributions |
| 13) | Continuous probability distributions |
| 14) | Joint, marginal, conditional distributions |
| 15) | Final examination period |
| 16) | Final examination period |
| Required/Recommended Readings | Newbold, P., Carlson, W.L., Thorne, B.M. (2013) Statistics for Business and Economics, ninth edition. Pearson. | |||||||||||||||||||||
| Teaching Methods | Active Learning Flipped Learning | |||||||||||||||||||||
| Homework and Projects | Post-class assignments | |||||||||||||||||||||
| Laboratory Work | NA | |||||||||||||||||||||
| Computer Use | NA | |||||||||||||||||||||
| Other Activities | Scheduled and unscheduled quizzes | |||||||||||||||||||||
| Assessment Methods |
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| Course Administration |
erkoln@mef.edu.tr 02123953670 Course Instructor: Asst. Prof. Narod Erkol (erkoln@mef.edu.tr) Attendance/participation: Students are expected to prepare for the lecture via pre-class assignments, videos and reading materials. Students are responsible to follow the announcements, course materials available on Blackboard system. Formal use of e-mails: Students are expected to use their @mef accounts for email traffic. The instructor is only responsible for the information sent/received through Blackboard 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. It is strongly recommended to complete all the work in a timely fashion. Late submissions will not be accepted. Missing projects: No make up unless a legitimate proof of absence is presented. Missing final exam: Faculty regulations. 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. Improper behavior, academic dishonesty and plagiarism: Law on Higher Education Article 54. Important: If the learner cannot collect at least 30 points from the activities other than the final exam, they can not take the final exam and will get an FZ grade. |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Calculation | |||
| No/Weeks per Semester | |||||
| Course Hours | 28 | 140 | |||
| Homework Assignments | 6 | 72 | |||
| Midterm(s) | 2 | 34 | |||
| Final Examination | 2 | 54 | |||
| Total Workload | 300 | ||||
| Total Workload/25 | 12.0 | ||||
| ECTS | 6 | ||||