BÄ°S605 - BIOSTATISTICS
Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
---|---|---|---|---|---|---|
BIOSTATISTICS | BÄ°S605 | 1st Semester | 3 | 0 | 3 | 7 |
Prequisites | None | |||||
Course language | Turkish | |||||
Course type | Elective | |||||
Mode of Delivery | Face-to-Face | |||||
Learning and teaching strategies | Lecture Discussion | |||||
Instructor (s) | Prof. Erdem Karabulut, PhD - Prof. Pınar Özdemir, PhD - Assoc. Prof. Osman DAĞ, PhD | |||||
Course objective | To teach basic statistical concepts and methods to students by examples and applications in health sciences, to provide understanding and evaluating the statistical analysis used in the literature of their own study fields. | |||||
Learning outcomes |
| |||||
Course Content | 1. Basic statistical concepts 2. Descriptive statistics 3. Theoretical distributions, normal distribution 4. Sampling distributions 5. Basic research designs 6. Basic sampling methods 7. Hypothesis tests 8. Measures of association 9. Linear regression analysis 10. Measures of risk, evaluation of diagnostic tests | |||||
References | 1. Sümbüloglu K ve Sümbüloğlu V. Biyoistatistik. Seçkin Yayıncılık, Ankara, 2010. 2. Özdamar K. Pasw ile Biyoistatistik. Kaan Kitabevi, Eskişehir, 2013. 3. Alpar R. Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle UYGULAMALI İSTATİSTİK ve GEÇERLİK-GÜVENİRLİK. Detay Yayıncılık, Ankara, 2018. 4. Daniel, Wayne W. Biostatistics 9th Edition, New York: John Wiley&Sons, 2013. |
Course outline weekly
Weeks | Topics |
---|---|
Week 1 | Basic statistical concepts such as: statistics and biostatistics, population, sample, statistic, parameter, data, variable, types of data, and etc. |
Week 2 | Descriptive statistics: grouping the data, measures of central tendency, measures of location, histogram, bar graph, stem & leaf graph, box-plot, etc. |
Week 3 | Descriptive statistics: measures of dispersion, error bar graph, etc. |
Week 4 | Examining the association among the variables by tables and graphics: Cross tables, tables regarding descriptive statistics (mean, s. deviation, etc.), multivariate applications of basic graphics, scatter plots, etc. |
Week 5 | Standardization (z and T scores). Theoretical distributions: Normal distribution, and normality transformations. Tests and graphs for normality. Sampling distributions and confidence intervals |
Week 6 | 1st Midterm Examination |
Week 7 | Research methods. Introduction to hypothesis tests: Aims and stages of hypothesis tests, possible types of errors, p and alpha values, power, effect size, decision process |
Week 8 | Hypothesis tests: Parametric and nonparametric one sample tests. Parametric and nonmparametric independent two sample tests. |
Week 9 | Hypothesis tests: Parametric and nonparametric k independent sample tests. |
Week 10 | Hypothesis tests: : Parametric and nonparametric dependent two-sample tests). Parametric and nonparametric k dependent sample tests . |
Week 11 | 2nd Midterm Examination |
Week 12 | Measures of association: Pearson correlatin coefficient, Spearman correlation coefficient, Phi, Cramer V, Eta, etc. |
Week 13 | Simple and multiple linear regression analysis. |
Week 14 | Simple and multiple linear regression analysis and examining an article. |
Week 15 | Preparation to final exam |
Week 16 | Final Exam |
Assesment methods
Course activities | Number | Percentage |
---|---|---|
Attendance | 0 | 0 |
Laboratory | 0 | 0 |
Application | 0 | 0 |
Field activities | 0 | 0 |
Specific practical training | 0 | 0 |
Assignments | 12 | 30 |
Presentation | 0 | 0 |
Project | 0 | 0 |
Seminar | 0 | 0 |
Midterms | 2 | 20 |
Final exam | 1 | 50 |
Total | 100 | |
Percentage of semester activities contributing grade succes | 0 | 50 |
Percentage of final exam contributing grade succes | 0 | 50 |
Total | 100 |
WORKLOAD AND ECTS CALCULATION
Activities | Number | Duration (hour) | Total Work Load |
---|---|---|---|
Course Duration (x14) | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Specific practical training | 0 | 0 | 0 |
Field activities | 0 | 0 | 0 |
Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 6 | 84 |
Presentation / Seminar Preparation | 0 | 0 | 0 |
Project | 0 | 0 | 0 |
Homework assignment | 12 | 4 | 48 |
Midterms (Study duration) | 2 | 10 | 20 |
Final Exam (Study duration) | 1 | 16 | 16 |
Total Workload | 43 | 39 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
D.9. Key Learning Outcomes | Contrubition level* | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
1. Have knowledge about radiotherapy machines and their properties at such a level that they are able to perform their calibration and quality control. | X | ||||
2. Comprehend treatment planning and applications of radiotherapy. | X | ||||
3. Have adequate information on clinical and basic oncology. | X | ||||
4. Be able to improve their knowledge about radiotherapy physics and go deep in their subject. | X | ||||
5. Be able to prepare complex treatment plans, i.e. stereotactic radiosurgery, IMRT and 3DCRT | X | ||||
6. Be able to perform calibration and quality control of radiotherapy machines. | X | ||||
7. Be able to prepare scientific reports, posters and articles. | X | ||||
8. Be able to use informatics technology both in clinics and research. | X | ||||
9. Perform dosimetric measurements in the field of radiation oncology. | X | ||||
10. Be able to find alternative solutions to the subjects in radiotherapy by critical approach. | X | ||||
11. Be able to handle problems together with physicians and other medical staff and thus find solutions. | X | ||||
12. Be able to work independently as well as in a team in clinics and research studies. | X | ||||
13. Be able to follow the advances in radiotherapy and develop written and verbal communication with colleagues. | X | ||||
14. Be able to use their knowledge and skills effectively in interdisciplinary studies. | X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest