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Syllabus ( BENG 432 )


   Basic information
Course title: Advanced Biostatistics
Course code: BENG 432
Lecturer: Dr. Öğr. Üyesi Pınar PİR
ECTS credits: 5
GTU credits: 3 ()
Year, Semester: 4, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Departmental Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: BENG331 - Biostatistics (at least CC)
Professional practice: No
Purpose of the course: This course focuses on advanced statistical methods for analysis of biological data
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Analyse biological data using advanced statistical methods

    Contribution to Program Outcomes

    1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
    2. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
    3. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Model the biological systems based on biological and medical data

    Contribution to Program Outcomes

    1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
    2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
    3. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Interpret the data and models towards better understanding and design of new biological systems.

    Contribution to Program Outcomes

    1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
    2. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
    3. Work effectively in multi-disciplinary research teams

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
Week 1: Analysis of categorical data - analysis of contingency tables with two categories
Week 2: Analysis of categorical data - analysis of contingency tables with multiple categories
Homework1
Week 3: Regression methods
Week 4: Correlation methods
Homework2
Week 5: Analysis of Variance (ANOVA) - parametric methods
Week 6: Analysis of Variance (ANOVA) - non-parametric methods
Homework3
Week 7: Midterm Exam -1
ANOVA on multi-factor datasets

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
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
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 7, 12 40
Other in-term studies: - 0
Project: - 0
Homework: 2,4,6,9,11 30
Quiz: - 0
Final exam: 16 30
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 0 0
Practice, Recitation: 0 0
Homework: 4 5
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 20 2
Mid-term: 2 2
Personal studies for final exam: 20 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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