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| EKO4002 | Computer Applications in Statistics - II | 3+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of ECONOMETRICS | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Sinem KOÇAK | | Co-Lecturer | Asst. Prof. Dr. Uğur Şevik | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | By using the SPSS package program, the aim is to provide students with the ability to choose and use methods such as regression analysis, variance analysis, correlation analysis, factor analysis and cluster analysis that are suitable for the problem, analyze, interpret the results and write a report on the results. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | perform operations on factor analysis and cluster analysis | 2,4 | 1, | | LO - 2 : | apply variance analysis | 2,4 | 1, | | LO - 3 : | interpret relationships between variables using correlation analysis | 2,4 | 1, | | LO - 4 : | make predictions using regression analysis | 2,4 | 1, | | LO - 5 : | select appropriate statistical analysis method for a research | 2,4 | 1, | | LO - 6 : | perform statistical analysis appropriate to the problem using the SPSS package program | 2,4 | 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 | | |
| This course covers theoretical knowledge and SPSS applications of methods such as prediction with regression analysis, analysis of variance, correlation analysis, factor analysis and cluster analysis. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Two-way analysis of variance | | | Week 2 | Friedman S-Test for more than two dependent samples | | | Week 3 | Chi-square tests for independence and homogeneity | | | Week 4 | Correlation analysis, Pearson correlation coefficient, Spearman correlation coefficient | | | Week 5 | Correlation analysis, Kendall's tau-b coefficient | | | Week 6 | Simple linear regression analysis | | | Week 7 | Statistical inferences (confidence intervals and hypothesis tests) | | | Week 8 | Curve fitting to data | | | Week 9 | Midterm Exam | | | Week 10 | Multiple linear regression | | | Week 11 | Multiple correlation and partial correlation coefficient | | | Week 12 | Examination of residues | | | Week 13 | Factor analysis | | | Week 14 | Distance and similarity measures | | | Week 15 | k-Means cluster analysis | | | Week 16 | Final exam | | | |
| 1 | Balcı, S., Ahi, B. 2016; SPSS Kullanma Kılavuzu: SPSS ile Adım Adım Veri Analizi, Anı Yayıncılık, İstanbul | | | |
| 1 | Durmuş, B., Çinko, M., Yurtkoru, E.S. 2012; Sosyal Bilimlerde SPSS'le Veri Analizi, Beta Yayınları, Ankara | | | 2 | Karagöz, Y. 2018; SPSS ve AMOS Uygulamalı Bilimsel Araştırma Yöntemleri, Nobel Akademik Yayıncılık, Sivas | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 04/2024 | 1 | 50 | | End-of-term exam | 16 | 06/2024 | 1 | 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 | 4 | 14 | 56 | | Arasınav için hazırlık | 6 | 3 | 18 | | Arasınav | 1 | 1 | 1 | | Uygulama | 3 | 14 | 42 | | Dönem sonu sınavı için hazırlık | 5 | 4 | 20 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 180 |
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