| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 3 | EEM 209 | Probability Theory & Statistical Analysis | 4+0+0 | 4 | 7 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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Electrical and Electronics Engineering
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Mode of Delivery
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Face to Face
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Type of Course Unit
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Compulsory
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Objectives of the Course
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Introducing Fundementals of Probability and Statistics and the Necessary Background
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Course Content
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Definition, history, advancement and basic principles of statistics and probability. Statistical methods. Students will be able to analyze the data obtained in their own fields and to obtain correct and meaningful results.
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Course Methods and Techniques
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Prerequisites and co-requisities
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None
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Course Coordinator
|
Associate Prof.Dr. Ümit Deniz ULUŞAR
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Name of Lecturers
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Prof.Dr. ÜMİT DENİZ ULUŞAR
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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Matlab Help Files
Engineering Statistics
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Course Category
|
Mathematics and Basic Sciences
|
%50
|
|
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Engineering
|
%50
|
|
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Engineering Design
|
%0
|
|
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Social Sciences
|
%0
|
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Education
|
%0
|
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Science
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%0
|
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Health
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%0
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Field
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%0
|
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
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In-Term Studies
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Mid-terms
|
1
|
%
35
|
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Assignment
|
4
|
%
2,50
|
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Project
|
1
|
%
10
|
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Final examination
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1
|
%
45
|
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Total
|
7
|
%
92,5
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ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
|
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Course Duration
|
15
|
1
|
15
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Hours for off-the-c.r.stud
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15
|
5
|
75
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Assignments
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4
|
2
|
8
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Mid-terms
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1
|
3
|
3
|
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Project
|
1
|
3
|
3
|
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Final examination
|
1
|
3
|
3
|
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Total Work Load
| |
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Number of ECTS Credits 4
107
|
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
|
1
| Recognize the basic concepts of statistics and usage methods |
|
2
| Will be able to perform statistical analysis using Matlab and other 3rd party tools. |
|
3
| Will gain research notion |
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4
| Improve analystical problem solving capabilities |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction
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| 2 |
Introduction to Statistics, Basics and Definitions
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| 3 |
Measurement Data Variable and Relationship, Classification of Data (Quantitative, Qualitative)
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| 4 |
Descriptive Statistics, Measuring Central Tendency
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| 5 |
Graphical Techniques
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| 6 |
Variability
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| 7 |
Measuring Correlation Between Two Variables
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| 8 |
Probability
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| 9 |
Distributions
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| 10 |
Central Limit Theory
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| 11 |
Hyphotesis Testing and Confidence Interval
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| 12 |
Regression Analysis, Linear Regression
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| 13 |
Classification
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Contribution of Learning Outcomes to Programme Outcomes
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https://obs.akdeniz.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2429214&lang=en