GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
ANALYZE OF EFFICIENCY AND PRODUCTIVITY/7700048
Course Title: ANALYZE OF EFFICIENCY AND PRODUCTIVITY
Credits 3 ECTS 7.5
Semester 1 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Prof. Dr. Murat ATAN
 -- WEB SITE(S) OF LECTURER(S)
  www.gazi.edu.tr/~atan
 -- EMAIL(S) OF LECTURER(S)
  atan@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Productivity and Efficiency Know Basic Concepts. Productivity and Efficiency Measurement Methods, Productivity and Efficiency Analysis of the can.
Know the practice of business in efficiency and productivity analysis.
Two-Stage Linear Regression Analysis, Nonparametric Methods, Data envelopment analysis (DEA), Know the Model Input Oriented CCR.
Will explore how enterprises can obtain the actual data activity indicator, eliminating the drawbacks of existing approaches.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  What is Efficiently and Productivity? Definitions and Basic Concepts, Methods of Measuring Productivity and Efficiently, Analyze of Productivity and E
2. Week  Methods of Parametrics Solution, Simple Linear Regression Analysis, Two Stage Linear Regression Analysis
3. Week  Methods of Non-Parametrics Solution, Data Envelopment Analysis(DEA)
4. Week  Input Oriented CCR Models, Input Oriented BCC Models
5. Week  Output Oriented CCR Models, Output Oriented BCC Models
6. Week  Necessary Steps with Application on DEA Models, Selection on Observation Clusters, Selection on Input and Output Clusters
7. Week  Measuring on Relative Efficiently with DEA, Detail Analysis For Each A Decision Unit, Malmquist Total Factor Productivity Index (TFP) and Application.
8. Week  Midterm
9. Week  Stochastic Limit Approximation Models
10. Week  Laboratory application related with DEA
11. Week  Laboratory application related with DEA
12. Week  Laboratory application related with DEA
13. Week  Stochastic Limit approximation to the laboratory practice
14. Week  Project Slides
15. Week  Project Slides
16. Week  Final Exam
 -- RECOMMENDED OR REQUIRED READING
  Armağan, TARIM., (2001), “Veri Zarflama Analizi : Matematiksel Programlama Tabanlı Göreli Etkinlik Ölçüm Yaklaşımı”, Sayıştay Yayın İşleri Müdürlüğü, araştırma / İnceleme / Çeviri Dizisi : 15, Ankara, A., CHARNES, W. COOPER, W., LEWIN, A.Y., SEIFORD, L. M., (1994), “Data Envelopment Analysis”, Kluwer Academic İlknur, YAVUZ, (2003), “Verimlilik ve Etkinlik Ölçümüne Yeni Yaklaşımlar ve İllere Göre İmalat Sanayiinde Etkinlik Karşılaştırmaları”, Milli Prodüktivite Merkezi Yayınları No: 667, Ankara. Reha, YOLALAN, (1993), “İşletmeler arası Göreli Etkinlik Ölçümü”, Milli Prodüktivite Merkezi Yayınları: 483, Ankara.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  No
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
0
0
 Assignment
1
50
 Exercises
1
50
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
100
 Contribution of Final Examination to Overall Grade  
0
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
2
28
 Practising Hours of Course Per Week
14
1
14
 Reading
14
2
28
 Searching in Internet and Library
6
3
18
 Designing and Applying Materials
0
 Preparing Reports
9
4
36
 Preparing Presentation
9
4
36
 Presentation
9
2
18
 Mid-Term and Studying for Mid-Term
0
 Final and Studying for Final
0
 Other
0
 TOTAL WORKLOAD: 
178
 TOTAL WORKLOAD / 25: 
7.12
 ECTS: 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Having the professional expertise and skills on theoretical and practical aspect of econometrics.X
2Having appropriate behavior of econometricsin the field of social, scientific, cultural and ethical values, applicability of gained knowledge and skills in accordance with the principle of responsibility to social and working life.X
3Having skills about selecting, using and evaluating of appropriate econometrics and statistical programming methods, based on theoretical and practical knowledge acquired in the field related to the current problems faced by.X
4Having skills to work with the team for reach fruitful results in interdisciplinary and interdisciplinary studies.X
5Having knowledge of research methods in the field of institutions and organizations planning projects, managing in order to conduct independent research.X
6Using some of the advanced level econometrics and statistical software programs.X
7Having skills about information system related to the fields in working life.X
8Having qualities for analytical thinking, prospecting and bring scientific solutions to problems as a researcher.X
9Having skills to comprehension, application and evaluation about terms and basic theory of operational research.X
10Having the professional expertise and skills on theoretical and practical aspects of operational research.X