Rapor Tarihi: 13.04.2026 03:06
| Course Title | Code | Language | Type | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| Biometry | BYL305 | Turkish | Compulsory | 5. Semester | 2 + 2 | 3.0 | 5.0 |
| Prerequisite Courses | |
| Course Level | Undergraduate |
| Mode of delivery | Face to face |
| Course Coordinator | Prof. Dr. MERAL KEKEÇOĞLU |
| Instructor(s) | Prof. Dr. MERAL KEKEÇOĞLU (Güz) |
| Goals | To teach the statistical evaluation methods of biological work results and upskill statistical data interpretation |
| Course Content | Descriptive statistics , correlation, regression and analysis of variance . Nonparametric Methods Numerical Taxonomy of demography index number . Descriptive statistics , odds and probabilities of the normal distribution, parameter estimates, errors and probability normal distribution , Significance test the hypothesis control group comparison tests, spouse correlation test , analysis of variance input, completely aleatory trial plan, aleatory blocks ( Randomized Complete Block ) trial plan , Latin square experimental plan, factorial experiments, binomial and chi-square distributions, confidence intervals. Statistical inferences basis ; Hypothesis tests, one-way classification , variance analysis and basic assumptions, correlation and linear regression analysis , regression coefficients , regression of linearity testing, non-linear relationships, correlation coefficient , groups inside ( intra- class ) correlation coefficient Katogorical data analysis; some non-parametric statistical methods |
| # | Öğrenme Kazanımı |
| 1 | 1. Learn the basic concepts of biometrics. |
| 2 | 2. 2. Learn to prepare frequency distributions and statistical graphs. |
| 3 | 3. Learns to calculate descriptive statistics |
| 4 | 4. Learns to solve problems related to Normal and Binomial distributions. |
| 5 | 5. Learns to perform hypothesis testing using certain statistical distributions. |
| 6 | 6. Learns to use biometric activities in conducting operations and solving problems. |
| 7 | 7. Learns to apply biometric activities to practical applications. |
| Week | Topics/Applications | Method |
|---|---|---|
| 1. Week | The descriptive statistics, correlation, regression and analysis of variance | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer |
| 2. Week | Non parametric methods, demography, index numbers, numeric taxonomy | Other Activities, Presentation (Preparation), Practice, Lecture, Question and Answer |
| 3. Week | The descriptive statistics, probability distribution, parameter estimations, normal distribution of error and probabilities | Preparation, After Class Study, Research, Presentation (Preparation), Practice, Lecture |
| 4. Week | Importance test, hypothesis check, group comparison tests | Preparation, After Class Study, Research, Presentation (Preparation), Practice, Lecture, Problem Solving |
| 5. Week | Multiple comparison test, variance analysis, test plan and linked blocks | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer, Problem Solving |
| 6. Week | Test plan, latin square test plan, factorial tests | Preparation, After Class Study, Practice, Lecture, Demonstration |
| 7. Week | Binomial and chi-square distributions, confidence intervals | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer |
| 8. Week | Binomial and chi-square distributions, confidence intervals | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer |
| 9. Week | Review, practice sessions, and data analysis examples (SPSS applications) | Preparation, After Class Study, Research, Presentation (Preparation), Practice, Lecture, Demonstration, Group Work |
| 10. Week | Basis of statistical inference, hypothesis tests, one way classification | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer, Group Work |
| 11. Week | Variance analysis and basic assumption | Preparation, After Class Study, Research, Presentation (Preparation), Practice, Lecture |
| 12. Week | Correlation and linear regression analysis, regression coefficient, linearity test | Research, Presentation (Preparation), Practice, Lecture, Demonstration, Group Work |
| 13. Week | Correlation coefficient, intragroup correlation coefficient | Preparation, After Class Study, Presentation (Preparation), Practice, Lecture, Question and Answer |
| 14. Week | Categorical data analysis, non parametric statistic methods | Preparation, After Class Study, Research, Presentation (Preparation), Practice, Lecture, Question and Answer |
| No | Program Requirements | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Obtains theoretical and applied information in the field of biological sciences, perceives and recognizes living things in the context of structure, function - functioning, organization and mutual interactions, comprehends and interprets their importance | ✔ | |||||
| 2 | Defines problems related to biology subjects, formulates hypothesis for the solution of problems by making synthesis, tests the hypothesis with various observational and experimental methods, analyzes the results and gains the ability to interpret. | ✔ | |||||
| 3 | Learns the processes and phenomena in the field of life sciences in relation to current knowledge and methods. Using this information, proposes solutions to local and global problems related to field. | ✔ | |||||
| 4 | Selects, applies and develops contemporary techniques and tools in the field of life sciences and effectively uses information technologies as a part of her/his own life. | ✔ | |||||
| 5 | In order to meet the needs of the individual or society, works effectively and takes responsibility in activities and projects for her professional development, both individually and in multidisciplinary groups. | ✔ | |||||
| 6 | Follows scientific publications published/to be published in the field, conveys the information obtained verbally and in writing to colleagues and different segments of the society. Communicates with colleagues in Turkish/English. | ✔ | |||||
| 7 | By following the current scientific and technological developments in biological sciences, gains the competence to develop lifelong learning, knowledge and skills. | ✔ | |||||
| 8 | Have universal and social ethical values and professional responsibility awareness with a scientific point of view. | ✔ | |||||
| 9 | Adheres to the principle of sustainability in theoretical and applied studies related to biological sciences, gains awareness of its effects and legal consequences on a local/global scale. | ✔ | |||||
| Program Requirements | DK1 | DK2 | DK3 | DK4 | DK5 | DK6 | DK7 |
|---|---|---|---|---|---|---|---|
| PY1 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| PY2 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| PY3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
| PY5 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY6 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY7 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| PY8 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| PY9 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| Ders Kitabı veya Notu | Ders Kitabı veya Ders Notu bulunmamaktadır. |
|---|---|
| Diğer Kaynaklar |
|
| Güz Dönemi | |||
| Responsible Personnel | Grup | Evaluation Method | Percentage |
|---|---|---|---|
| Prof. Dr. MERAL KEKEÇOĞLU | Vize | 40.00 | |
| Prof. Dr. MERAL KEKEÇOĞLU | Final | 60.00 | |
| Toplam | 100.00 | ||
| ECTS credits and course workload | Quantity | Duration (Hour) | Total Workload (Hour) | |
|---|---|---|---|---|
|
Ders İçi |
Class Hours | 14 | 4 | 56 |
|
Ders Dışı |
Presentation (Preparation) | 14 | 2 | 28 |
| Practice | 1 | 6.5 | 6.5 | |
|
Sınavlar |
Midterm | 1 | 2 | 2 |
| Final | 1 | 2 | 2 | |
| Practice | 14 | 2 | 28 | |
| Practice End-Of-Term | 5 | 1 | 5 | |
| Total Workload | 127.5 | |||
| *AKTS = (Total Workload) / 25,5 | ECTS Credit of the Course | 5.0 | ||
