BIOENGINEERING (ENGLISH, THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
BME3005 | Biostatistics | Fall Spring |
2 | 2 | 3 | 6 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL |
Course Lecturer(s): |
Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL |
Course Objectives: | - The course provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types; regression analysis, confidence intervals, correlations |
The students who have succeeded in this course; - The students who have succeeded in this course; I. Interpret statistical results correctly, effectively, and in context. II. Select an appropriate test for comparing two or more populations, and interpret and explain a p-value III. Understand the concept of the power of data. IV. Calculate and interpret confidence intervals for population means and proportions V. Understand regression analysis and correlation of variables. |
Design of Experiments, Statistical programming: , Exploratory Data Analysis and Descriptive Statistics, Probability Theory, Sampling Distributions and the Central Limit Theorem, Estimation, Statistical Inference, Contingency tables, Nonparametric Tests, Power and sample size, ANOVA, Correlation and Regression, Logistic regression, Survival Analysis, applications on biological datasets. |
Week | Subject | Related Preparation | |
1) | Introduction to biostatistics | ||
2) | Descriptive Statistics | ||
3) | Probability Theory | ||
4) | Sampling Distributions and the Central Limit Theorem | ||
5) | ANOVA | ||
6) | The Special Case of Two Groups: the t test | ||
7) | Contingency tables, Chi Square Test, z-test | ||
8) | Fisher Exact Test, Relative Risk, Odds Ratio | ||
9) | Power and Sample size | ||
10) | Paired t-test, Repeated Measures of Analysis of Variance, McNemar's Test | ||
11) | Nonparametric Tests: Mann-Whitney Rank-Sum Test, Wilcoxon Signed-Rank Test | ||
12) | Nonparametric Tests: Kruskal-Wallis Test, Friedman Test | ||
13) | Confidence Intervals | ||
14) | Correlation and Regression |
Course Notes: | Primer of Biostatistics, Stanton A. Glantz, McGraw-Hill, 7th Edition Fundamental of Biostatistics, Bernard Rosner, Cengage Learning, 8th Edition |
References: |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | 5 | % 30 |
Homework Assignments | 0 | % 0 |
Presentation | 0 | % 0 |
Project | 0 | % 0 |
Seminar | % 0 | |
Midterms | 1 | % 30 |
Preliminary Jury | % 0 | |
Final | 1 | % 40 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 14 | 7 | 98 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 0 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Quizzes | 5 | 1 | 5 |
Preliminary Jury | 0 | 0 | 0 |
Midterms | 1 | 3 | 3 |
Paper Submission | 0 | 0 | 0 |
Jury | 0 | 0 | 0 |
Final | 1 | 3 | 3 |
Total Workload | 151 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | An understanding of the advanced concepts of Mathematics (calculus, analysis, linear algebra, differential equations, statistics), Natural Sciences (physics, chemistry, biology), and Engineering Sciences (electronics, material science, mechanics, thermal and fluid systems, control, signal and image processing, microcontrollers) relevant to Biomedical Engineering. | |
2) | An ability to use at an advanced level the techniques, skills, and modern engineering tools (including software) necessary for engineering practice. | |
3) | The capability of designing and conducting advanced experiments and of analyzing and evaluating data. | |
4) | An ability to design the components of complex systems and processes under realistic constraints. | |
5) | Acquisition of the skills needed to develop products (device, system, process) which are used in diagnosis, prevention, treatment and cure of diseases. | |
6) | An ability to communicate knowledge and opinion efectively, both oral and in writing. | |
7) | An ability to assume initiative and individual resposibility, and to cooperate with team-mates from other disciplines. | |
8) | A kowledge of the current needs and problems of society, and an awareness of the social and global impact of engineering solutions. | |
9) | Assimilation of the ethics and responsibilities of the profession. | |
10) | Recognition of the importance of life-long learning, and participation therein. |