Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ADVANCED STATISTICAL METHODS | - | Spring Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Pakize YİĞİT |
| Name of Lecturer(s) | Prof.Dr. Hanefi ÖZBEK |
| Assistant(s) | |
| Aim | To show advanced statistical methods that can be used in scientific research. |
| Course Content | This course contains; 1. Introduction to multivariate statistical methods,2. r*c table chi-square tests,3. Multiple regression analysis methods,4. Logistic regression analysis 1,5. Logistic regression analysis 2,6. Probit analysis,7. ROC analysis MIDTERM,8. One-way repeated measures ANOVA,9. Two-way repeated measures ANOVA,10. One-way ANOVA for independent groups,11. Two-way ANOVA for independent groups,12. MANOVA,,13. Survival analysis methods 1,14. Survival analysis methods 2 FINAL EXAM. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Summarize the multivariate statistical methods. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 2. Apply multivariate statistical methods on a computer. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 3. Interpret the results of statistics. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 4: Inquiry-Based Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | 1. Introduction to multivariate statistical methods | Preparation for the lecture notes previously issued. |
| 2 | 2. r*c table chi-square tests | Preparation for the lecture notes previously issued. |
| 3 | 3. Multiple regression analysis methods | Preparation for the lecture notes previously issued. |
| 4 | 4. Logistic regression analysis 1 | Preparation for the lecture notes previously issued. |
| 5 | 5. Logistic regression analysis 2 | Preparation for the lecture notes previously issued. |
| 6 | 6. Probit analysis | Preparation for the lecture notes previously issued. |
| 7 | 7. ROC analysis MIDTERM | Preparation for the lecture notes previously issued. |
| 8 | 8. One-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 9 | 9. Two-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 10 | 10. One-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 11 | 11. Two-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 12 | 12. MANOVA, | Preparation for the lecture notes previously issued. |
| 13 | 13. Survival analysis methods 1 | Preparation for the lecture notes previously issued. |
| 14 | 14. Survival analysis methods 2 FINAL EXAM | Preparation for the lecture notes previously issued. |
| Resources |
| Lecture notes wil be given to the students. |
| Advanced Statistics, Larry Stephens, McGraw Hill, 2004. Bilgisayar istatistik ve tıp Dr. Murat Hayran, Dr. Oktay Özdemir. Bilimsel araştırmalarda biyoistatistik prensip ve yöntemlerinin bilinçli kullanımı Kadir Sümbüloğlu, Vildan Sümbüloğlu. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-1. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-2. Sağlık alanına özel istatistiksel yöntemler Kadir Sümbüloğlu. Sağlık Araştırmaları İçin Temel İstatistik, Murat Hayran, Mutlu Hayran. Tıbbi araştırmalarda istatistiksel analiz teknikleri “SPSS uygulamaları” Aziz Akgül. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | He/she knows the basic functions and working mechanisms of organs and systems and can explain each system as a course subject. | ||||||
| 2 | He/she can dissect different parts of cadavers alone, identify organs and other structures on them, and lecture on them | ||||||
| 3 | He/she can design a project on a subject individually, take an active role in that project, compose a scientific article, and publish. | ||||||
| 4 | She/he can take an active role in multidisciplinary studies related to her/his branch. | ||||||
| 5 | He/she can identify normal anatomical structures on radiogram, MR and CT images and provide anatomical explanations for pathological conditions | ||||||
| 6 | He/she understands the relationship of anatomy with other basic medicine and clinical branches and takes part in projects. | ||||||
| 7 | He/she can speak at least one foreign language effectively, he/she can follow international publications in his/her branch or other branchs. | ||||||
| 8 | He/she can understand the topographic anatomy, surface projections and courses of organs and structures. | ||||||
| 9 | He/she can can make a presentation about his/her branch or other researchers branchs. | ||||||
| 10 | He/she can analyzes herself/himself by following current research topics. | ||||||
| 11 | He/she effectively fulfills the responsibility given within the team in multidisciplinary studies. | ||||||
| 12 | He/she can solve clinical problems and explain anatomically. | ||||||
| 13 | He/she can define the basic microanatomical structures in the human body, tissues, organs and systems. | ||||||
| 14 | He/she can make trust-based relationships with students and get them interest in the lesson. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 0 | 0 | 0 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 0 | 0 | 0 | |||
| General Exam | 0 | 0 | 0 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 0 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30) | 0 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ADVANCED STATISTICAL METHODS | - | Spring Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Pakize YİĞİT |
| Name of Lecturer(s) | Prof.Dr. Hanefi ÖZBEK |
| Assistant(s) | |
| Aim | To show advanced statistical methods that can be used in scientific research. |
| Course Content | This course contains; 1. Introduction to multivariate statistical methods,2. r*c table chi-square tests,3. Multiple regression analysis methods,4. Logistic regression analysis 1,5. Logistic regression analysis 2,6. Probit analysis,7. ROC analysis MIDTERM,8. One-way repeated measures ANOVA,9. Two-way repeated measures ANOVA,10. One-way ANOVA for independent groups,11. Two-way ANOVA for independent groups,12. MANOVA,,13. Survival analysis methods 1,14. Survival analysis methods 2 FINAL EXAM. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Summarize the multivariate statistical methods. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 2. Apply multivariate statistical methods on a computer. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| 3. Interpret the results of statistics. | 10, 12, 13, 14, 16, 19, 4, 6, 8, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 4: Inquiry-Based Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | 1. Introduction to multivariate statistical methods | Preparation for the lecture notes previously issued. |
| 2 | 2. r*c table chi-square tests | Preparation for the lecture notes previously issued. |
| 3 | 3. Multiple regression analysis methods | Preparation for the lecture notes previously issued. |
| 4 | 4. Logistic regression analysis 1 | Preparation for the lecture notes previously issued. |
| 5 | 5. Logistic regression analysis 2 | Preparation for the lecture notes previously issued. |
| 6 | 6. Probit analysis | Preparation for the lecture notes previously issued. |
| 7 | 7. ROC analysis MIDTERM | Preparation for the lecture notes previously issued. |
| 8 | 8. One-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 9 | 9. Two-way repeated measures ANOVA | Preparation for the lecture notes previously issued. |
| 10 | 10. One-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 11 | 11. Two-way ANOVA for independent groups | Preparation for the lecture notes previously issued. |
| 12 | 12. MANOVA, | Preparation for the lecture notes previously issued. |
| 13 | 13. Survival analysis methods 1 | Preparation for the lecture notes previously issued. |
| 14 | 14. Survival analysis methods 2 FINAL EXAM | Preparation for the lecture notes previously issued. |
| Resources |
| Lecture notes wil be given to the students. |
| Advanced Statistics, Larry Stephens, McGraw Hill, 2004. Bilgisayar istatistik ve tıp Dr. Murat Hayran, Dr. Oktay Özdemir. Bilimsel araştırmalarda biyoistatistik prensip ve yöntemlerinin bilinçli kullanımı Kadir Sümbüloğlu, Vildan Sümbüloğlu. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-1. Paket programlar ile istatistiksel veri analizi Kazım Özdamar 1999-2. Sağlık alanına özel istatistiksel yöntemler Kadir Sümbüloğlu. Sağlık Araştırmaları İçin Temel İstatistik, Murat Hayran, Mutlu Hayran. Tıbbi araştırmalarda istatistiksel analiz teknikleri “SPSS uygulamaları” Aziz Akgül. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | He/she knows the basic functions and working mechanisms of organs and systems and can explain each system as a course subject. | ||||||
| 2 | He/she can dissect different parts of cadavers alone, identify organs and other structures on them, and lecture on them | ||||||
| 3 | He/she can design a project on a subject individually, take an active role in that project, compose a scientific article, and publish. | ||||||
| 4 | She/he can take an active role in multidisciplinary studies related to her/his branch. | ||||||
| 5 | He/she can identify normal anatomical structures on radiogram, MR and CT images and provide anatomical explanations for pathological conditions | ||||||
| 6 | He/she understands the relationship of anatomy with other basic medicine and clinical branches and takes part in projects. | ||||||
| 7 | He/she can speak at least one foreign language effectively, he/she can follow international publications in his/her branch or other branchs. | ||||||
| 8 | He/she can understand the topographic anatomy, surface projections and courses of organs and structures. | ||||||
| 9 | He/she can can make a presentation about his/her branch or other researchers branchs. | ||||||
| 10 | He/she can analyzes herself/himself by following current research topics. | ||||||
| 11 | He/she effectively fulfills the responsibility given within the team in multidisciplinary studies. | ||||||
| 12 | He/she can solve clinical problems and explain anatomically. | ||||||
| 13 | He/she can define the basic microanatomical structures in the human body, tissues, organs and systems. | ||||||
| 14 | He/she can make trust-based relationships with students and get them interest in the lesson. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |