### Master of Science in Industrial Engineering

Course Code Course Name Semester Theory Practice Lab Credit ECTS
IND 522 Advanced Statistical Modeling 1 3 0 0 3 6
 Language of Instruction English Course Type Compulsory Course Level Masters Degree Course Instructor(s) H. ZİYA ULUKAN zulukan@gsu.edu.tr (Email) Assistant Objective This advanced course in inferential statistics emphasizes the practical application of statistical analysis. Instruction includes an examination of the role of statistics in research; understanding statistical terminology; use of appropriate statistical techniques; and interpretation of findings in the fields of engineering. Content Topics include graphing and tabulation of data, hypothesis testing for small and large samples, chi-squared, statistical quality control, analysis of variance (ANOVA), regression, correlation, and decision making under uncertainty.Topics combines intermediate and advanced statistical methods with practical research applications Course Learning Outcomes At the conclusion of this course, the student should possess the ability to perform required statistical analyses for virtually any univariate application in a business / industrial setting. Teaching and Learning Methods Direct References Understanding and Using Advanced Statistics: A Practical Guide for Students, Jeremy J Foster, Emma Barkus, Christian Yavorsky, Sage pub., 2006Advanced Statistics, Jain, T.R. ,, Aggarval, S.C., Statistics: Methods and Applications : a Comprehensive Reference for Science, Industry, and Data Mining, Statsoft, 2006.
###### Theory Topics
Week Weekly Contents
1 Introduction to Advanced Statistical Modeling
2 Sampling and Survey Techniques
3 Estimation and inferences
4 Hypothesis testing
5 The Design & Analysis of Factorial Experiments for 2 Factors - Model I Applications
6 The Design & Analysis of Factorial Experiments for 2 Factors - Model II Applications
7 The Design & Analysis of Factorial Experiments for 2 Factors - Model III Applications
8 Midterm
9 Linear Regression Analysis I
10 Linear Regression Analysis II
11 Linear Regression Analysis III
12 Linear Regression Analysis IV
13 Multiple Regression Analysis I
14 Multiple Regression Analysis II
###### Practice Topics
Week Weekly Contents
Number Contribution
Contribution of in-term studies to overall grade 4 60
Contribution of final exam to overall grade 1 40
Toplam 5 100
###### In-Term Studies
Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 1 30
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 3 30
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 4 60
No Program Learning Outcomes Contribution
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X
9 X
10 X
11 X
12 X
Class Hours 14 3 42
Working Hours out of Class 13 5 65
Assignments 0 0 0
Presentation 0 0 0
Midterm Examinations (including preparation) 1 10 10
Project 0 0 0
Laboratory 0 0 0
Other Applications 0 0 0
Final Examinations (including preparation) 1 15 15
Quiz 3 14 42
Term Paper/ Project 0 0 0
Portfolio Study 0 0 0
Reports 0 0 0
Learning Diary 0 0 0
Thesis/ Project 0 0 0
Seminar 0 0 0
Other 0 0 0