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Information
| Code | ISB005 |
| Name | Mathematical Growth Models |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
Understanding and applying the mathematical tools used to model growth
Course Content
Types and uses of mathematical growth models
Course Precondition
none
Resources
Random growth models, Damron Mathematical models, Timbergen
Notes
Random growth models, Damron Mathematical models, Timbergen
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Make scientific research on Mathematics, Probability and Statistics. |
| LO02 | They have the knowledge to make doctoral plans in the field of statistics. |
| LO03 | Has comprehensive knowledge about analysis methods used in statistics |
| LO04 | Has comprehensive knowledge aboutmodeling methods used in statistics |
| LO05 | Comprehensive knowledge of methods used in statistics. |
| LO06 | Indicates statistical problems, develops methods to solve. |
| LO07 | Apply innovative methods to analyze statistical problems. |
| LO08 | Designs and applies the problems faced in the field of analytical modeling and experimental researches. |
| LO09 | Access to information and do research about the source. |
| LO10 | Develops solution approaches in complex situations and takes responsibility. |
| LO11 | It has the confidence to take responsibility. |
| LO12 | He/She demonstrates that he is aware of his / her new and developing practices. |
| LO13 | Transmits the processes and results of their studies clearly in written and oral form in national and international environments. |
| LO14 | It considers the social, scientific and ethical values in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops new methods and strategies in modeling statistical problems and generating problem-specific solutions. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Can do detailed research on a specific subject in the field of statistics. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Have a good command of statistical theory to contribute to the statistical literature. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Can use the knowledge gained in the field of statistics in interdisciplinary studies. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Can organize projects and events in the field of statistics. | |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Can perform the stages of creating a project, executing it and reporting the results. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Have the ability of scientific analysis. | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Can produce scientific publications in the field of statistics. | 2 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Have analytical thinking skills. | 3 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Can follow professional innovations and developments both at national and international level. | 3 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Can follow statistical literature. | 4 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Can improve his/her foreign language knowledge at the level of making publications and presentations in a foreign language. | |
| PLO13 | Bilgi - Kuramsal, Olgusal | Can use information technologies at an advanced level. | |
| PLO14 | Bilgi - Kuramsal, Olgusal | Have the ability to work individually and make independent decisions. | 4 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have the qualities necessary for teamwork. | 3 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Have a sense of professional and ethical responsibility. | 2 |
| PLO17 | Bilgi - Kuramsal, Olgusal | Acts in accordance with scientific ethical rules. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | What is growth | reading related articles | |
| 2 | What does growth modeling mean? | reading related articles | |
| 3 | Linear growth models? | reading related articles | |
| 4 | Non-linear growth models | reading the related article | |
| 5 | Lojistic growth model | reading the related articles | |
| 6 | Weibull growth model | reading the related articles | |
| 7 | Richards | reading the related articles | |
| 8 | Mid-term | non | |
| 9 | Gompertz | reading the related articles | |
| 10 | Modified models | reading the related articles | |
| 11 | Heligman-pollard model | reading the related articles | |
| 12 | Age pattern of mortality | reading the related articles | |
| 13 | Comperative analysis | reading the related articles | |
| 14 | Application of models | reading the related articles | |
| 15 | Application of models 2 | reading the related articles | |
| 16 | Final exam | exam | |
| 17 | Final exam | exam |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||