FACULTY OF ENGINEERING

Department of Biomedical Engineering

MATH 462 | Course Introduction and Application Information

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 Discussion
Q&A
Lecture / 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;
  • will be able to analyze statistical data.
  • will be able to decribe relationships between data.
  • will be able to measure central tendency and relative location of data.
  • will be able to extract knowledge from data.
  • will be able to test hypothesis about statistical data.
  • will be able to analyze linear relationships between variables.
Course Description This course provides several basic methods for analyzing statistical data appear in various fields of science.

 



Course Category

Core Courses
X
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
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

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
1
10
Project
1
20
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
3
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
4
56
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
1
15
15
Project
1
20
20
Seminar / Workshop
0
Oral Exam
0
Midterms
1
32
32
Final Exam
1
39
39
    Total
210

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
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

 


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