BME3005 BiostatisticsBahçeşehir UniversityDegree Programs ENERGY SYSTEMS ENGINEERINGGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
ENERGY SYSTEMS ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

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.

Basic information

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

Learning Outcomes

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.

Course Content

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.

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks: Primer of Biostatistics, Stanton A. Glantz, McGraw-Hill, 7th Edition
Fundamental of Biostatistics, Bernard Rosner, Cengage Learning, 8th Edition
References:

Evaluation System

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

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

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 Energy Systems Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems.
2) Ability to identify, formulate, and solve complex Energy Systems Engineering problems; select and apply proper modeling and analysis methods for this purpose.
3) Ability to design complex Energy systems, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose.
4) Ability to devise, select, and use modern techniques and tools needed for solving complex problems in Energy Systems Engineering practice; employ information technologies effectively.
5) Ability to design and conduct numerical or pysical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Energy Systems Engineering.
6) Ability to cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Energy Systems-related problems
7) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions.
8) Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself.
9) Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Energy Systems Engineering applications.
10) Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11) Acquire knowledge about the effects of practices of Energys Systems Engineering on health, environment, security in universal and social scope, and the contemporary problems of Energys Systems engineering; is aware of the legal consequences of Energys Systems engineering solutions.