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| BTB5150 | Data Processing in Fisheries | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of FISHERIES TECHNOLOGY ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | Associated Prof. Dr. Hacer SAĞLAM | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | Enable post graduate students whom never take statistics and having insufficient statistical knowledge on experiment design, data collection and evaluation |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | describe population-sample, parameter-statistic concepts, | 1,4,5,7,8,9,10,11,12 | 1 | | PO - 2 : | design experiments and surveys, execise methods to reduce experimentel bias, | 3,5,6,10,14,15 | 1 | | PO - 3 : | collect, summarise and present data, | 3,5,6,10,14,15 | 1 | | PO - 4 : | use descriptive statistical methods, | 3,5,6,10,14,15 | 1 | | PO - 5 : | understand normal, binomial and poisson distributions and test distributions, and use in their field of profession, | 3,5,6,10,14,15 | 1 | | PO - 6 : | develop their skills to construct hypothesis and control by Z, X2 (Chi-square) tests and variance analysis. | 3,5,6,10,14,15 | 1 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Collection and compilation of fishery statistics, methods for fisheries resource survey and appraisal, objectives and basic methods, experimental design, sampling, summarization and presentation, measures of central tendency and dispersion, probabilities and application in fisheries, theoretical distributions, testing hypothesis, statistical (parametric and non-parametric) tests, regression and correlation, time series, computer based case studies. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to statistics, terms and definitions, population and sample, sample size and sampling methods, dicrete and continuous variables, tables and graphs.
| | | Week 2 | Raw data,arrays, frequency distributions, class intervals and class limits, class boundaries, the size of class intervals, class mark, general rules for forming frquency distributions, histograms and frequency polygons, relative and cumulative frequency distributions.
| | | Week 3 | Measures of central tendency, arithmetic mean, weighed arithmetic mean, median, mode, empirical relation between mean median and mode, geometric mean, harmonic mean, properties of different measures. | | | Week 4 | Measures of dispersion, range, mean deviation, variation, standart deviation, coefficient of variation, properties of variance, Sheppard's correction for variance.
| | | Week 5 | Mid-term exam. | | | Week 6 | Elemantary probability theory, classical and statistical definition, probability theorems, independent and dependent ivents, conditional probability, probability distributions, mathematical expectation, factoriel n, permutations, combinations.
Classical populations, normal distributions, binomial distributions, poisson distributions.
| | | Week 7 | Relationship between variables, definitions, regresion lines and coefficients, estimation, correaltion coefficient, lineer and non-lineer relationships, computation, least square method.
| | | Week 8 | Mid-term exam | | | Week 9 | Sampling distributions, definitions, distribution of means, distribution of difference of means, distribution of proportions.
| | | Week 10 | Standart error, standart eror of mean, difference of means, correlation and regression coefficients.
| | | Week 11 | Test distributions, Z, t, Chi-square, F distributions. | | | Week 12 | estimation of parameters, confidence intervals.
| | | Week 13 | Hypothesis testing, Type I and Type II errors, significance levels, Z, Student's t, chi-square tests and tables.
| | | Week 14 | Analysis of variance, mathematical model and analyse, means of squares, F test, computations evaluation of analyses. | | | Week 15 | Determination of different groups, least significance, Duncan method.
| | | Week 16 | End-of-term exam | | | |
| 1 | Düzgüneş, O., Kesici, T., Gürbüz, F. 1993. İstatistik Metodları (Statistical Methods). A.Ü. Ziraat Fak. Yay. No:1291, 218 pp. | | | 2 | Spiegel, M.R.1972. Theory and Problems of Statistics. Schaum's Outline Series. McGraw-Hill Book Company, 359 pp. | | | |
| 1 | Yıldız, N., Bircan, H. 1994. Araştırma ve Deneme Metodları. Atatürk. Ün. Zir. Fak. No 305. Erzurum. 266 s. | | | 2 | Düzgüneş, O. 1963. Bilimsel Araştırmalarda İstatistik Prensipleri ve Metodları. EÜ. Matbaası.375 s. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 5 | 15/03/2017 | 2 | 20 | | In-term studies (second mid-term exam) | 8 | 12/04/2017 | 2 | 30 | | End-of-term exam | 16 | 05/06/2017 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 5 | 4 | 20 | | Arasınav | 2 | 1 | 2 | | Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 10 | 3 | 30 | | Dönem sonu sınavı için hazırlık | 10 | 4 | 40 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 206 |
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