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IST3017 | Statistical Computation and Data Analysis | 4+0+0 | ECTS:6 | Year / Semester | Fall Semester | Level of Course | First Cycle | Status | Elective | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | Prerequisites and co-requisites | None | Mode of Delivery | Face to face | Contact Hours | 14 weeks - 4 hours of lectures per week | Lecturer | Prof. Dr. Orhan KESEMEN | Co-Lecturer | PROF. DR. Zafer KÜÇÜK | Language of instruction | Turkish | Professional practise ( internship ) | None | | The aim of the course: | Providing descriptive information about the datasets that can be encountered in almost every basic science. Determining the appropriate analysis methods for the structures of the datasets and giving them how to apply these methods. |
Learning Outcomes | CTPO | TOA | Upon successful completion of the course, the students will be able to : | | | LO - 1 : | classify the data set | 1,2,3,4 | 1 | LO - 2 : | determine the analysis method ragarding the data set | 1,2,3,4 | 1 | LO - 3 : | make a decision for about the hypothesis applying the analysis method on the data set | 1,2,3,4 | 1 | LO - 4 : | draw various graphics regarding the data sets | 1,2,3,4 | 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), LO : Learning Outcome | |
Stages of statistical data analysis, exploratory and generalizing data analysis, odd number statistics, data representation, graphical representations, branch-leaf, box and root representation, data transformation, data editing, durable lines, tables, analysis techniques. |
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Course Syllabus | Week | Subject | Related Notes / Files | Week 1 | Basic definitions and concepts | | Week 2 | Cases of non-parametric tests were used. Normality analysis. | | Week 3 | Hypothesis testing related to the average | | Week 4 | Statistical tests for the data collected from a single sample. | | Week 5 | Statistical tests for two independent samples | | Week 6 | Statistical tests used in the data collected from the two-dependent samples . | | Week 7 | Variance analysis. | | Week 8 | Doubly comparison after variance analysis. | | Week 9 | Mid-term exam
| | Week 10 | Chi-Square test for independence. | | Week 11 | Regression analysis. | | Week 12 | Simple linear regression model. | | Week 13 | Explained and unexplained changes. | | Week 14 | Determination of correlation coefficient. | | Week 15 | Control of the importance of variance. | | Week 16 | End-of-term exam | | |
1 | Özdamar, K., 1999; Paket programlar ile istatistiksel veri analizi, Kan Kitabevi, Eskişehir. | | |
1 | Ünver, Ö., 2006; SPSS Uygulamalı Temel İstatistik Yöntemler, Seçkin Yayıncılık, Ankara. | | |
Method of Assessment | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | Mid-term exam | 9 | 24/11/2021 | 1,5 | 50 | End-of-term exam | 16 | 13/01/2022 | 1,5 | 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 | 4 | 14 | 56 | Sınıf dışı çalışma | 3 | 14 | 42 | Arasınav için hazırlık | 11 | 1 | 11 | Arasınav | 1.5 | 1 | 1.5 | Proje | 10 | 1 | 10 | Dönem sonu sınavı için hazırlık | 18 | 1 | 18 | Dönem sonu sınavı | 1.5 | 1 | 1.5 | Total work load | | | 140 |
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