Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ONTRODUCTION to LINEAR MODELS | - | Spring Semester | 3+0 | 3 | 10 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Required |
| Course Coordinator | Prof.Dr. Abdulbari BENER |
| Name of Lecturer(s) | Prof.Dr. Abdulbari BENER |
| Assistant(s) | |
| Aim | |
| Course Content | This course contains; Introduction, Linear algebra; Multivariate Gaussian distribution,Linear Regression Models,Linear Regression Models Assumptions,Nonlinear Regression Models,Poisson Regression Analysis ,Zero-Inflated Regression Models,Negative Binomial Regression Model,Logistic Regression,Linear Mixed Models,Generalized Linear Mixed Models ,Mixed Effect Nonlinear Models ,Linear Mixed Models Covariance Structure,Applications ,Applications. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Able to interpret and write reports of Linear model analysis | 10, 16, 6 | E |
| able to know the theory of linear models, their formulation, their estimation and inference. | 12, 14, 2 | A, E |
| Able to applicate Linear models in a statistical package. | 10, 14, 6, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction, Linear algebra; Multivariate Gaussian distribution | Lecture Notes |
| 2 | Linear Regression Models | Lecture Notes |
| 3 | Linear Regression Models Assumptions | Lecture Notes |
| 4 | Nonlinear Regression Models | Lecture Notes |
| 5 | Poisson Regression Analysis | Lecture Notes |
| 6 | Zero-Inflated Regression Models | Lecture Notes |
| 7 | Negative Binomial Regression Model | Lecture Notes |
| 8 | Logistic Regression | Lecture Notes |
| 9 | Linear Mixed Models | Lecture Notes |
| 10 | Generalized Linear Mixed Models | Lecture Notes |
| 11 | Mixed Effect Nonlinear Models | Lecture Notes |
| 12 | Linear Mixed Models Covariance Structure | Lecture Notes |
| 13 | Applications | Lecture Notes |
| 14 | Applications | Lecture Notes |
| Resources |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Can use advanced theoretical and applied knowledge gained in the fields of theoretical and applied biostatistics. | X | |||||
| 2 | Can use the knowledge of basic probability and statistics theories and applications at the level of expertise. | X | |||||
| 3 | They have knowledge of all kinds of research design in the field of health | X | |||||
| 4 | Can design, construct and propose solutions for research in the field of health. | ||||||
| 5 | Can identify and analyze problems in health research and produce solutions based on scientific methods | X | |||||
| 6 | Conducts scientific clinical descriptive or analytical research on priority issues related to the field. | ||||||
| 7 | Evaluate and explain the information about the field of biostatistics with a critical approach. | ||||||
| 8 | Observes and teaches social, scientific, and ethical values in the stages of data collection, recording, interpretation, and reporting related to the field of biostatistics. | X | |||||
| 9 | To be familiar with the software commonly used in the fields of biostatistics and to be able to use at least one effectively | X | |||||
| 10 | Conducts studies in the field of biostatistics independently or as a team. | ||||||
| 11 | Maintains work in the field of biostatistics individually or as a team, can participate in the decision-making process, and make and finalize the necessary planning by using time effectively. | ||||||
| 12 | Ensure the continuity of her professional development by using the biostatistics field and lifelong learning principles. | X | |||||
| 13 | Publishes a scientific article in a national and international journal or presents it at a scientific meeting. | X | |||||
| 14 | Take part in research, projects and activities in collaboration with other disciplines in the field of health. | ||||||
| 15 | A sensitive individual, they can use their knowledge for the benefit of society and have sufficient awareness about quality management, occupational safety, and environment in all processes. | ||||||
| 16 | Can use the knowledge and problem-solving skills synthesized in the field of biostatistics by considering ethical principles in health research. | X | |||||
| 17 | It can be found in national and international policy studies in the field of biostatistics and education. | ||||||
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 |
|---|---|---|---|---|---|
| ONTRODUCTION to LINEAR MODELS | - | Spring Semester | 3+0 | 3 | 10 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Required |
| Course Coordinator | Prof.Dr. Abdulbari BENER |
| Name of Lecturer(s) | Prof.Dr. Abdulbari BENER |
| Assistant(s) | |
| Aim | |
| Course Content | This course contains; Introduction, Linear algebra; Multivariate Gaussian distribution,Linear Regression Models,Linear Regression Models Assumptions,Nonlinear Regression Models,Poisson Regression Analysis ,Zero-Inflated Regression Models,Negative Binomial Regression Model,Logistic Regression,Linear Mixed Models,Generalized Linear Mixed Models ,Mixed Effect Nonlinear Models ,Linear Mixed Models Covariance Structure,Applications ,Applications. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Able to interpret and write reports of Linear model analysis | 10, 16, 6 | E |
| able to know the theory of linear models, their formulation, their estimation and inference. | 12, 14, 2 | A, E |
| Able to applicate Linear models in a statistical package. | 10, 14, 6, 9 | E |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction, Linear algebra; Multivariate Gaussian distribution | Lecture Notes |
| 2 | Linear Regression Models | Lecture Notes |
| 3 | Linear Regression Models Assumptions | Lecture Notes |
| 4 | Nonlinear Regression Models | Lecture Notes |
| 5 | Poisson Regression Analysis | Lecture Notes |
| 6 | Zero-Inflated Regression Models | Lecture Notes |
| 7 | Negative Binomial Regression Model | Lecture Notes |
| 8 | Logistic Regression | Lecture Notes |
| 9 | Linear Mixed Models | Lecture Notes |
| 10 | Generalized Linear Mixed Models | Lecture Notes |
| 11 | Mixed Effect Nonlinear Models | Lecture Notes |
| 12 | Linear Mixed Models Covariance Structure | Lecture Notes |
| 13 | Applications | Lecture Notes |
| 14 | Applications | Lecture Notes |
| Resources |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Can use advanced theoretical and applied knowledge gained in the fields of theoretical and applied biostatistics. | X | |||||
| 2 | Can use the knowledge of basic probability and statistics theories and applications at the level of expertise. | X | |||||
| 3 | They have knowledge of all kinds of research design in the field of health | X | |||||
| 4 | Can design, construct and propose solutions for research in the field of health. | ||||||
| 5 | Can identify and analyze problems in health research and produce solutions based on scientific methods | X | |||||
| 6 | Conducts scientific clinical descriptive or analytical research on priority issues related to the field. | ||||||
| 7 | Evaluate and explain the information about the field of biostatistics with a critical approach. | ||||||
| 8 | Observes and teaches social, scientific, and ethical values in the stages of data collection, recording, interpretation, and reporting related to the field of biostatistics. | X | |||||
| 9 | To be familiar with the software commonly used in the fields of biostatistics and to be able to use at least one effectively | X | |||||
| 10 | Conducts studies in the field of biostatistics independently or as a team. | ||||||
| 11 | Maintains work in the field of biostatistics individually or as a team, can participate in the decision-making process, and make and finalize the necessary planning by using time effectively. | ||||||
| 12 | Ensure the continuity of her professional development by using the biostatistics field and lifelong learning principles. | X | |||||
| 13 | Publishes a scientific article in a national and international journal or presents it at a scientific meeting. | X | |||||
| 14 | Take part in research, projects and activities in collaboration with other disciplines in the field of health. | ||||||
| 15 | A sensitive individual, they can use their knowledge for the benefit of society and have sufficient awareness about quality management, occupational safety, and environment in all processes. | ||||||
| 16 | Can use the knowledge and problem-solving skills synthesized in the field of biostatistics by considering ethical principles in health research. | X | |||||
| 17 | It can be found in national and international policy studies in the field of biostatistics and education. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |