FACULTY OF ARTS AND SCIENCES

Department of Mathematics

MATH 336 | Course Introduction and Application Information

Course Name
Engineering Statistics II
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 336
Fall/Spring
3
0
3
5

Prerequisites
  MATH 236 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
Field work/Application
Course Coordinator -
Course Lecturer(s)
Assistant(s)
Course Objectives The main aim of the course is to introduce advanced statistical methods and usage areas to students.
Learning Outcomes The students who succeeded in this course;
  • will be able to determine appropriate distribution for the observed data.
  • will be able to analyze independence and homogeneity relationships between criteria.
  • will be able to model linear and nonlinear relationships between variables.
  • will be able to make complex models of multivariate.
  • will be able to observe the effect of one or more factors and the use of different models.
  • will be able to apply statistical methods in different fields.
Course Description Chi-square distribution and applications, goodness of fit test, simple linear regression and correlation analysis, multiple regression analysis, non-linear regression analysis, defining model in multiple regression, single and multi-factor analysis of variance.

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Tests of chi-square "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 9
2 Independence and Homogeneity Tests "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 9
3 Testing for goodness of fit "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 9
4 Introduction to regression "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 11
5 Analysis of simple regression and correlation "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 11
6 Analysis of multiple linear regression "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 12
7 Analysis of multiple linear regression "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 12
8 Methods used for defining multiple regression "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 12
9 Midterm
10 Analysis of non-linear regression "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 11
11 Analysis of variance "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 13
12 Multi-factor analysis of variance "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 13
13 The models of two-factor analysis of variance "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 14
14 Two- factor analysis of variance "Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712 Chapter 14
15 Semester Review
16 Final Exam

 

Course Notes/Textbooks

"Applied Statistics and Probability for Engineers" by D.C. Montgomery, G.C. Runger, Wiley, 6th Edition, 2013. ISBN-13: 978-1118539712

Suggested Readings/Materials

"Statistics for Engineers and Scientists" by William Navidi, McGraw-Hill Education, 4th Edition, 2014. ISBN-13: 978-0073401331

PowerPoint slides, Excel sheets supplied in lectures for example problems.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
5
25
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
35
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
6
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
3
42
Field Work
0
Quizzes / Studio Critiques
5
4
20
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
15
15
Final Exam
1
25
25
    Total
150

 

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.

X
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.

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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