FACULTY OF ARTS AND SCIENCES

Department of Mathematics

MATH 204 | Course Introduction and Application Information

Course Name
Introduction to Probability Theory II
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 204
Spring
2
2
3
6

Prerequisites
  MATH 203 To attend the classes (To enrol for the course and get a grade other than NA or W)
Course Language
English
Course Type
Required
Course Level
First Cycle
Mode of Delivery face to face
Teaching Methods and Techniques of the Course -
Course Coordinator -
Course Lecturer(s)
Assistant(s)
Course Objectives This course aims to consider basic theory and applications of probability theory including jointly distributed and multivariate random variables, independent random variables and their sums, some special discrete and continuous distributions and copulas, covariance and correlation coefficient, order statistics, some special inequalities, the weak and strong law of large numbers, and central limit theorem.
Learning Outcomes The students who succeeded in this course;
  • will be able to examine jointly distributed and multivariate random variables.
  • will be able to evaluate independent random variables and their sums.
  • will be able to examine some special discrete and continuous distributions and copulas.
  • will be able to calculate covariance and correlation coefficient.
  • will be able to calculate order statistics and some special inequalities.
  • will be able to analyze the weak and strong law of large numbers and central limit theorem.
Course Description This course aims to provide basic theory and applications of Probability Theory.

 



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 Multivariate random variables and their joint distribution functions ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 6
2 Sum of two random variables and convolution formula ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 6
3 Independent random variables and sums of two random variables ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 6
4 Joint probability distribution of functions of random variables ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 6
5 Exchangeable random variables ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 6
6 Some special discrete distributions ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 6
7 Some special continuous distributions, Some copulas ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 6
8 Midterm Exam
9 Expectation of sums of random variables ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 7
10 Covariance and correlation coefficient ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 7
11 Conditional expectation ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 7
12 Order statistics ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 6
13 Chebyshev, Markov, and Pearson inequalities ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.Chapter 8
14 Weak law of large numbers, strong law of large numbers, central limit theorem ''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772. Chapter 8
15 Semester review
16 Final exam

 

Course Notes/Textbooks

''A First Course in Probability'' by Sheldon Ross, 9th edition, Pearson,2012. ISBN-13: 978-0321794772.

Suggested Readings/Materials

“Probability and Statistics for Engineers and Scientists” by Ronald Walpole, Raymond Myers, Sharon Myer.Publisher:Pearson,2010, ISBN-13:978-0321629111

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
40
Final Exam
1
60
Total

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

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
2
32
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
2
32
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
35
35
Final Exam
1
39
39
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

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

To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.

X
2

To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.

3

To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.

4

To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.

5

To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.

6

To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.

X
7

To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.

X
8

To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.

9

To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.

10

To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.

11

To be able to collect data in the areas of Mathematics or Statistics and 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 be able to relate the knowledge accumulated throughout the human history to their field of expertise.

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

 


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