Syllabus ( BENG 432 )
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Basic information
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| Course title: |
Advanced Biostatistics |
| Course code: |
BENG 432 |
| Lecturer: |
Assist. Prof. Pınar PİR
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| ECTS credits: |
5 |
| GTU credits: |
3 () |
| Year, Semester: |
4, Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Departmental Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
BENG331 - Biostatistics (at least CC) |
| Professional practice: |
No |
| Purpose of the course: |
This course aims to introduce the advanced statistical methods for analysis of biological and medical data |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Analyse biological data using advanced statistical methods
Contribution to Program Outcomes
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Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
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Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
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Work effectively in multi-disciplinary research teams
Method of assessment
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Written exam
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Homework assignment
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Model the biological systems based on biological and medical data
Contribution to Program Outcomes
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Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
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Apply mathematical analysis and modeling methods for bioengineering design and production processes.
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Work effectively in multi-disciplinary research teams
Method of assessment
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Written exam
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Homework assignment
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Interpret the data and models towards better understanding and design of new biological systems.
Contribution to Program Outcomes
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Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
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Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
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Work effectively in multi-disciplinary research teams
Method of assessment
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Written exam
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Homework assignment
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Contents
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| Week 1: |
Analysis of categorical data - analysis of contingency tables with two categories
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| Week 2: |
Analysis of categorical data - analysis of contingency tables with multiple categories
Homework1 |
| Week 3: |
Regression methods
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| Week 4: |
Correlation methods
Homework2
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| Week 5: |
Analysis of Variance (ANOVA) - parametric methods
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| Week 6: |
Analysis of Variance (ANOVA) - non-parametric methods
Homework3 |
| Week 7: |
Midterm Exam -1
ANOVA on multi-factor datasets
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| Week 8: |
Analysis of epidemiological data - categorical data data |
| Week 9: |
Analysis of epidemiological data - time course data
Homework4 |
| Week 10: |
Analysis of person-time data
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| Week 11: |
Survival Analysis Methods
Homework5 |
| Week 12: |
Midterm Exam - 2
Non-parametric survival analysis |
| Week 13: |
Experimental design techniques |
| Week 14: |
Interpretation of p-values |
| Week 15*: |
- |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
Rosner, Introduction to Biostatistics (8th ed, 2015) - Chapters 9 -13 |
| Recommended readings: |
PennState Eberly College of Science - online statistics courses |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
7, 12 |
40 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
2,4,6,9,11 |
30 |
| Quiz: |
- |
0 |
| Final exam: |
16 |
30 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
0 |
0 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
4 |
5 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
20 |
2 |
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| Mid-term: |
2 |
2 |
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| Personal studies for final exam: |
20 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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