MANAGEMENT ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
BME3005 | Biostatistics | Fall | 2 | 2 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL |
Course Lecturer(s): |
Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL |
Recommended Optional Program Components: | None |
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 / Textbooks: | 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 |
Quizzes | 5 | % 30 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
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 |
Study Hours Out of Class | 14 | 7 | 98 |
Quizzes | 5 | 1 | 5 |
Midterms | 1 | 3 | 3 |
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) | Build up a body of knowledge in mathematics, science and engineering subjects; use theoretical and applied information in these areas to model and solve engineering problems. | |
2) | identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose. | |
3) | Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.) | |
4) | Devise, select, and use modern techniques and tools needed for engineering management practice; employ information technologies effectively. | |
5) | Design and conduct experiments, collect data, analyze and interpret results for investigating engineering management problems. | |
6) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently. | |
7) | Demonstrate effective communication skills in both oral and written English and Turkish. | |
8) | Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself. | |
9) | Develop an awareness of professional and ethical responsibility. | |
10) | Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development. | |
11) | Know contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions. | |
12) | Develop effective and efficient managerial skills. |