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•           Information on Degree Programmes

COURSE INFORMATON
Course Title Code Semester L+P Hour Credits ECTS
Statistics SUF   201 3 2 3 4

 Prerequisites and co-requisites Yok Recommended Optional Programme Components None

Language of Instruction Turkish
Course Level First Cycle Programmes (Bachelor's Degree)
Course Type
Course Coordinator Asst.Prof.Dr. Makbule BAYLAN
Instructors
 Dr. Öğr. Üyesi MAKBULE BAYLAN 1. Öğretim Grup:A

Assistants
Goals
Teaching Data, Measures of Location, Distribution Measures, Probability, discrete and continuous probability functions, distributions, hypothesis testing, confidence intervals, such as regression and correlation with the basic statistical concepts and methods.
Content
Introduction to statistics, basic concepts and symbols, frequency distributions, measures of location (mean, weighted mean, median, mod and geometric mean), measures of dispersion (range, variance, standard error of mean, coefficient of variation), probability, discrete distributions (Binomial, Poisson), normal distribution, hypothesis testing (z- and t- tests), chi-square test, analysis of regression and correlation.

Learning Outcomes
1) Understanding the importance of statistical engineering
2) being able to solve problems in profession and other subjects using statistical methods and techniques
3) learning the basic concepts of statistics, the formation and analysis of data
4) Developing the ability to identify, interpret and draw appropriate statistical methods for the situation
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Course's Contribution To Program
NoProgram Learning OutcomesContribution
12345
1
Having knowledge on “natural and applied sciences” and “basic engineering”; combination of their theoretical and practical knowledge on fisheries engineering applications.
X
2
2. Assessment of data scientifically on fisheries engineering, determining and solving the problems
X
3
3. Uses theoretical and practical knowledge in the field of fisheries to design; investigates and interprets events and phenomena usig scientific methods and techniques.
X
4
4. Collecting data in fisheries science, making the basic experimental studies, evaluating the results, identifying the problems and developing methods of solution
X
5
5. Having plan any study related to fisheries science as an individually, managing and consulting.
X
6
6.Learning the knowledge by the determining learning needs; developing positive attitude towards lifelong learning
X
7
7. Communicating oral and written in expertise field, monitoring the seminars and meeting in expertise field, following the foreign language publication.
X
8
8. Improving life-long learning attitude and using the information to the public interest.
9
9. Creating public awareness about fisheries and having the ability to ensure sustainable use of aquatic resources.
X
10
10. Communicating oral and written effectively, participating the seminars and meetings in expertise field, following the foreign language publications.
X
11
11. Using the informatics and communicating technology
12
12. Improves constantly itself , as well as professional development scientific, social, cultural and artistic fields according to his/her interests and abilities identifying needs of learning.
13
13. Gaining competence to determine the current status of aquatic resources and its sustainable use, water pollution and control, and biotechnology areas.
X
14
14. Having ability to promote the study about aquaculture techniques by saving the natural environment, fishery diseases, fishing and processing technology, structure of fishery sector, problems and solution of their expertise field
X
15
15. Ability to act in accordance with the regulation, social, scientific, cultural, and ethical values on fisheries field

Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Introduction Reading related sources Lecture
Drilland Practice
2 The basic concepts and symbols Reading related sources Lecture
Drilland Practice
3 Data classification and graph screening Reading related sources Lecture
Drilland Practice
4 Location measurements Reading related sources Lecture
Drilland Practice
5 Dispersion measures Reading related sources Lecture
Drilland Practice
6 Probability and counting rules Reading related sources Lecture
Drilland Practice
7 Chance variables and Probability functions Reading related sources
8 Mid-term exam Preparing for exam Testing
9 Binomial and Poisson distributions Reading related sources Lecture
Drilland Practice
10 Normal distribution Reading related sources Lecture
Drilland Practice
11 Hypothesis testing (Z- and T- Tests) Reading related sources Lecture
Drilland Practice
12 Confidence Interval Reading related sources Lecture
Drilland Practice
13 Chi-square distribution and Chi-square test Reading related sources Lecture
Drilland Practice
14 Regression analyses Reading related sources Lecture
Drilland Practice
15 Correlation analyses Reading related sources Lecture
Drilland Practice
16-17 FINAL Preparing for exam Testing