Course Name | Applied Statistics |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
MATH 462 | Fall/Spring | 3 | 0 | 3 | 7 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | First Cycle | |||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | DiscussionQ&ALecturing / Presentation | |||||
Course Coordinator | - | |||||
Course Lecturer(s) | ||||||
Assistant(s) |
Course Objectives | This course provides essential materials for analyzing statistical data appear in various fields of social and phsical sciences. |
Learning Outcomes | The students who succeeded in this course;
|
Course Description | This course provides several basic methods for analyzing statistical data appear in various fields of science. |
Related Sustainable Development Goals | |
| Core Courses | X |
Major Area Courses | ||
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Importance of describing data and summarizing descriptive relationships | You need to follow the lecture notes. |
2 | Obtaining meaningful data, presenting data. Data presentation errors | You need to follow the lecture notes. |
3 | Descriptive Measures: Measures of central tendency, measures of variability | You need to follow the lecture notes. |
4 | Measures of relative location | You need to follow the lecture notes. |
5 | Methods for detecting Outliers, obtaining bivariate linear relationships | You need to follow the lecture notes. |
6 | General principles for analyzing data: Concept of sampling, unbiasedness and minimum variance, the sampling distribution of the sample mean and the Central Limit Theorem | You need to follow the lecture notes. |
7 | General principles for analyzing data: Single sample estimation with confidence intervals and tests of hypothesis | You need to follow the lecture notes. |
8 | General principles for analyzing data: Two samples estimation with confidence intervals and tests of hypothesis | You need to follow the lecture notes. |
9 | Design of experiments | You need to follow the lecture notes. |
10 | Analysis of variance | You need to follow the lecture notes. |
11 | Simple linear regression | You need to follow the lecture notes. |
12 | Multiple regression and model building | You need to follow the lecture notes. |
13 | Categorical data analysis | You need to follow the lecture notes. |
14 | Some selected topics and applications | You need to follow the lecture notes. |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | The extracts above and exercises will be given |
Suggested Readings/Materials | “Statistical Techniques for Data Analysis” by J.K. Taylor and C. Cihon, Chapman and Hall/CRC, 2nd Edition, 2004. ISBN: 9781584883852 |
Semester Activities | Number | Weigthing |
Participation | 1 | 10 |
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | 1 | 10 |
Project | 1 | 20 |
Seminar / Workshop | ||
Oral Exam | ||
Midterm | 1 | 30 |
Final Exam | 1 | 30 |
Total |
Weighting of Semester Activities on the Final Grade | 4 | 70 |
Weighting of End-of-Semester Activities on the Final Grade | 1 | 30 |
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 14 | 4 | 56 |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | |||
Presentation / Jury | 1 | 15 | |
Project | 1 | 20 | |
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 32 | |
Final Exams | 1 | 39 | |
Total | 210 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have adequate knowledge in Mathematics, Science and Biomedical Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. | X | ||||
2 | To be able to identify, define, formulate, and solve complex Biomedical Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
3 | To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose. | X | ||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Biomedical Engineering applications. | X | ||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Biomedical Engineering research topics. | X | ||||
6 | To be able to work efficiently in Biomedical Engineering disciplinary and multi-disciplinary teams; to be able to work individually. | |||||
7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. | |||||
8 | To have knowledge about global and social impact of Biomedical Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions. | |||||
9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. | |||||
10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
11 | To be able to collect data in the area of Biomedical Engineering, and to be able to communicate with colleagues in a foreign language. | |||||
12 | To be able to speak a second foreign language at a medium level of fluency efficiently. | |||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Biomedical Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest