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

 



Course Category

Core Courses
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 Some selected topics and applications You need to follow the lecture notes.
16 Review of the Semester  

 

Course Textbooks The extracts above and exercises will be given
References “Statistical Techniques for Data Analysis” by J.K. Taylor and C. Cihon

 

EVALUATION SYSTEM

Semester Requirements Number Percentage
Participation
14
10
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
10
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
1
30
Total

Contribution of Semester Work to Final Grade
18
70
Contribution of Final Work to Final Grade
1
30
Total

ECTS / WORKLOAD TABLE

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
15
3
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
10
Project
1
7
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
25
Final / Oral Exam
1
35
    Total
170

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Qualifications / Outcomes
* Level of Contribution
1
2
3
4
5
1

To have sufficient background in Mathematics, Basic sciences and Biomedical Engineering areas and the skill to use this theoretical and practical background in the problems of the Biomedical Engineering.

2

To identify, formulate and solve Biomedical Engineering-related problems by using state-of-the-art methods, techniques and equipment; to select and apply appropriate analysis and modeling methods for this purpose.

3

To analyze a complex system, system components or process, and to design with realistic limitations to meet the requirements using modern design techniques; to apply modern design techniques for this purpose.

4

To choose and use the required modern techniques and tools for analysis and solution of complex problems in Biomedical Engineering applications; to skillfully use information technologies.

5

To design and do simulation and/or experiment, collect and analyze data and interpret results for studying complex engineering problems or research topics of the discipline. 

6

To efficiently participate in intradisciplinary and multidisciplinary teams; to work independently.

7

To communicate both in oral and written form in Turkish; to have knowledge of at least one foreign language; to have the skill to write and understand reports, prepare design and production reports, present, give and receive clear instructions.

8

To recognize the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.

9

To behave ethically, to be aware of professional and ethical responsibilities; to have knowledge about the standards in Biomedical Engineering applications.

10

To have information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.

11

To have knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Biomedical Engineering solutions.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest