Master of Science in Industrial Engineering

Advanced Statistical Modeling(IND 522)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
IND 522 Advanced Statistical Modeling 1 3 0 0 3 6
Prerequisites
Admission Requirements
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., 2006
Advanced Statistics, Jain, T.R. ,, Aggarval, S.C.,
Statistics: Methods and Applications : a Comprehensive Reference for Science, Industry, and Data Mining, Statsoft, 2006.
Print the course contents
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
Contribution to Overall Grade
  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
Activities Number Period Total Workload
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
Total Workload 174
Total Workload / 25 6,96
Credits ECTS 7
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