Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 2 / Spring Semester | ||||
| Level of Course | 1st Cycle Degree Programme | ||||
| Type of Course | Compulsory | ||||
| Department | ELECTRICAL AND ELECTRONICS ENGINEERING | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | SUAD BAŞBUĞ (suad@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| This course aims to teach the concepts of probability and statistics, which have an important place in engineering education and its applications also aims to teach Variation, Regression, Correlation, Statistical Estimation, Curve Fitting and Data Analysis. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | can explain the concepts and theories of probability and statistics |
PO-1 Mathematics, science and engineering information to gain the practical skills. PO-2 Ability to identify engineering problems, modelling, formulate and improve the ability to solve. |
Examination |
| LO-2 | will be able to variance calculation will be able to perform a chi-square test. Students will be able to learn curve fitting and sampling, and will be able to analyze data |
PO-1 Mathematics, science and engineering information to gain the practical skills. PO-2 Ability to identify engineering problems, modelling, formulate and improve the ability to solve. PO-4 Individual and/or in groups to gain the ability to work. PO-9 To adapt to innovation and emerging technologies, continuous self-renewal, and improve the ability of researchers. PO-11 The techniques required for engineering applications, methods and improve the ability to use modern tools. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Probability Models and Axioms, Conditioning and Bayes’ Rule, Independence Counting, Random Variables, Discrete Random Variables, Bernoulli, Uniform, Binomial and Geometric, Discrete Random Variables Expectation and its properties, Continuous Random Variables, Expectation and Variance, Gaussian Distribution, Gaussian Distribution Applications, Correlation and Covariance, Markov Chains, T-Test and applications. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Probability Models and Axioms | Lecture, question and answer, discussion |
| 2 | Conditioning and Bayes’ Rule | Lecture, question and answer, discussion |
| 3 | Independence | Lecture, question and answer, discussion |
| 4 | Counting | Lecture, question and answer, discussion |
| 5 | Random Variables | Lecture, question and answer, discussion |
| 6 | Discrete Random Variables (Bernoulli, Uniform, Binomial and Geometric) | Lecture, question and answer, discussion |
| 7 | Discrete Random Variables (Expectation and its properties) | Lecture, question and answer, discussion |
| 8 | mid-term exam | |
| 9 | Continuous Random Variables | Lecture, question and answer, discussion |
| 10 | Continuous Random Variables (Expectation and Variance) | Lecture, question and answer, discussion |
| 11 | Gaussian Distribution | Lecture, question and answer, discussion |
| 12 | Gaussian Distribution Applications | Lecture, question and answer, discussion |
| 13 | Correlation and Covariance | Lecture, question and answer, discussion |
| 14 | Markov Chains | Lecture, question and answer, discussion |
| 15 | T-Test | Lecture, question and answer, discussion |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Mühendisler İçin İstatistik, Mehmetçik BAYAZIT, Beyhan OĞUZ, Birsen Yayınevi, 2005. | |
| 2 | Introduction To Probability, Prof. John Tsitsiklis, 2018, Open Courseware, Massachusetts Institute of Technology | |
| Required Course instruments and materials | ||
| Course book, laptop computer, projector | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 2 | 5 | 10 |
| b) Search in internet/Library | 2 | 5 | 10 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 2 | 7 | 14 |
| Oral Examination | 0 | ||
| Quiz | 1 | 2 | 2 |
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 5 | 1 | 5 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 5 | 1 | 5 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 90 | ||