|
|
| SEC 319 | Statistical Computation and Data Analysis | 4+0+0 | ECTS:5 | | 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. Türkan ERBAY DALKILIÇ | | Co-Lecturer | Assoc. Prof. Dr. Zafer KÜÇÜK | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course aims to teach the students the descriptive knowledge for data sets, to enable them to understand what kind of analysis method is suitable for what kind of data set, and to teach them how to apply this method on data set. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | classify the data set | 1,2,3,4,5,6,7,8,11 | 1 | | LO - 2 : | determine the analysis method ragarding the data set | 1,2,3,4,5,7,8,10,11 | 1 | | LO - 3 : | make a decision for about the hypothesis applying the analysis method on the data set | 1,2,3,4,5,6,8,10,11 | 1 | | LO - 4 : | draw various graphics regarding the data sets | 1,2,3,4,6,8,10,11 | 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 | | |
| Rules of statistical data analysis, Robust and Exploratory data analysis, odd number statistics, data representation, graphical tools, stem-and-leaf, box and root representation, data transformation, data smoothing, robust lines, tables, analysis techniques |
| |
| 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 | 12/11/2015 | 1,5 | 50 | | End-of-term exam | 16 | 29/12/2015 | 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 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Proje | 10 | 1 | 10 | | Total work load | | | 52 |
|