School of Engineering \ Biomedical Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
The course involves data collection, analysis and presentation of results through statistical plots. In general, the syllabus contains principles of biostastiscal design, hypothesis testing, parametric and non-parametric sample analysis and comparison using ANOVA, Student's t-test, correlation and regression.
Textbook and / or References
• McKillup, S. (2011). Statistics Explained: An Introductory Guide for Life Scientists (2nd ed.). Cambridge: Cambridge University Press.
• Hartvigsen, G. (2021) A Primer in Biological Data Analysis and Visualization Using R (2nd ed.) New York: Columbia University Press
• Ralfh, T., (2019) Data Visualisation with R: 111 Examples (2nd ed.) Cham: Springer Nature
BMM 422 dersinin amacı Biyomedikal Mühendisliği dördüncü sınıf öğrencilerin biyomedikal ve tıbbi araştırmalarda biyoistatistiğin rolünü anlaması, halk sağlığı veya tıbbi araştırmalardan elde edilen verileri özetlemesi ve görüntülemesi, çeşitli çalışma tasarımlarının esaslarını anlaması, avantaj ve limitlerini açıklaması, hipotez testi yapmak için uygun testleri belirlemesi ve çıktıların uygun bir şekilde yorumlaması, tıbbi araştırmalarda ele alınan problemleri istatistiksel araçlar ile ayırt etmesi ve uygun istatistiksel prosedürleri seçmesi ve biyoistatistik alanında kullanılan istatistiksel yazılım olan R ve standart paketlerini öğrenmesidir.
1. Learning statistical methods, collecting and analysing data to address problems arising in medicine, public health and molecular biology
2. Being able to use hypothesis testing, defining statistical models, using tests such as ANOVA, Student's test, regression and alike
Week 1 What is and is not statistics?
Week 2 Introduction to R statistical software
Week 3 General concepts in statistics (varience, SEM, etc.)
Week 4 Connection between statistics and probability
Week 5 p-value and statistical significance
Week 6 Z and T tests
Week 7 One way ANOVA
Week 8 Two way ANOVA and Tukey test
Week 9 More complex ANOVA
Week 10 Regression and correlation
Week 11 ANCOVA
Week 12 Non-parametric tests
| Tentative Assesment Methods |
| Activities |
Number |
Weight (%) |
| Course Attendance/Participation |
- |
- |
| Laboratory |
- |
- |
| Application |
- |
- |
| Homework |
- |
- |
| Project |
1 |
20% |
| Presentation |
- |
- |
| Field Work |
- |
- |
| Internship |
- |
- |
| Course Boards |
- |
- |
| Quiz |
- |
- |
| Midterm Exam |
1 |
30% |
| Final Exam |
1 |
50% |
|
Total |
100% |
| Tentative ECTS-Workload Table |
| Activities |
Number/Weeks |
Duration (Hours) |
Workload |
| Course Hours (first 6 weeks) |
6 |
4 |
24 |
| Course Hours (last 6 weeks) |
6 |
3 |
18 |
| Laboratory |
- |
- |
- |
| Application |
- |
- |
- |
| Homework |
- |
- |
- |
| Project |
1 |
24 |
24 |
| Presentation |
- |
- |
- |
| Field Work |
- |
- |
- |
| Internship |
- |
- |
- |
| Course Boards |
- |
- |
- |
| Preparation for Quiz |
- |
- |
- |
| Preparation for Midterm Exam |
1 |
38 |
38 |
| Final Exam |
1 |
2 |
2 |
| Preparation for Final Exam |
1 |
36 |
36 |
| Study Hours Out of Class (preliminary work, reinforcement, etc.) |
12 |
2 |
24 |
| Total Workload | | |
166 |
| Total Workload / 30 | | |
166 / 30 |
| | |
|
| ECTS Credits of the Course | | |
6 |
|
Program Outcome
**
|
| 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
|
Course Outcome
|
|---|
| 1 |
B, A
|
|
|
|
C
|
A
|
|
|
A, B
|
|
|
| 2 |
B, A
|
|
|
|
C
|
A
|
|
|
A, B
|
|
|