GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
REGRESSION ANALYSIS/İST-302
Course Title: REGRESSION ANALYSIS
Credits 4 ECTS 6
Semester 6 Compulsory/Elective Compulsory
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Prof. Dr. Hamza Gamgam
 -- WEB SITE(S) OF LECTURER(S)
  http://websitem.gazi.edu.tr/site/gamgam
 -- EMAIL(S) OF LECTURER(S)
  gamgam@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
To fit model for data.
To teach the estimations of model parameters by the Least Square Method.
To teach the concepts of statistical inferences.
To examine the model assumptions.
To examine the multicolinearity.
To teach the variable selection methods.
 -- MODE OF DELIVERY
  The modes of delivery are face to face and Lab. application.
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional program component for this course.
 --COURSE CONTENT
1. Week  Main concepts and scatter diagram
2. Week  Simple linear regression model and parameter estimation with the least squares method
3. Week  Model assumptions, sum of squares, coefficient of determination and confidence intervals
4. Week  Testing of hypothesis and application in LAB.
5. Week  Testing of hypothesis and application in LAB.
6. Week  Finding the parameter estimation and variances with matrix calculation
7. Week  Multiple regression model and parameter estimation
8. Week  Multiple correlation coefficient and statistical inferences for multiple regression models
9. Week  MIDTERM EXAM
10. Week   Statistical inferences for multiple regression models and application in LAB.
11. Week  Part and partial correlation coefficient , use of dummy variables
12. Week  Weighted least squares method , examination of residual terms
13. Week  Multicolinearity, correlation matrix
14. Week  Methods of variables selection and application in LAB.
15. Week  Autocorrelation and application in LAB.
16. Week  FINAL EXAM
 -- RECOMMENDED OR REQUIRED READING
  Draper, N. R. and Smith, H., 1996, Applied Regression Analysis, New York. Ünver, Ö. ve Gamgam, H. (2008) Uygulamalı Temel İstatistik Yöntemler, Seçkin Yayınevi.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture and Lab. application.
 -- WORK PLACEMENT(S)
  No
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
30
 Assignment
1
4
 Exercises
4
6
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
4
56
 Practising Hours of Course Per Week
8
1
8
 Reading
4
3
12
 Searching in Internet and Library
6
4
24
 Designing and Applying Materials
0
 Preparing Reports
6
2
12
 Preparing Presentation
3
2
6
 Presentation
3
2
6
 Mid-Term and Studying for Mid-Term
4
2
8
 Final and Studying for Final
4
2
8
 Other
0
 TOTAL WORKLOAD: 
140
 TOTAL WORKLOAD / 25: 
5.6
 ECTS: 
6
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. The statistical textbooks which include latest information about statistics, equipment and other resources supported by scientific approach on undergraduate level have theoretical and practical knowledge.X
22. Statisticians by using knowledge and skills acquired at bachelor degree level model, analyze, and interpret datasets.X
33. Statisticians identify and analyze the problems with current developments in statistic and also develop solutions based upon researches and proofs.X
44. Statisticians apply theoretical and practical knowledge acquired in Statistics at bachelor degree level to the current problems.X
55. Statisticians have the ability to use computer software and computing technology at the certain level required by statistics field.X
66. Statisticians take responsibility at disciplinary and interdisciplinary studies as an individual or a team member.X
77. Statisticians must have knowledge and ability to follow development in the field of Statistics, and must develop life long-learning attitudes.X
88. By using a foreign language, statistician can keep track of every statistical information, and communicate with colleagues.X
99. Applying the statistical knowledge in the professional sense, statistician has social, scientific, and ethical values.X
1010. A statistician must have the ability to social sensitivity and socialization.X
1111. During the process of inference, a statistician uses time efficiently with the analytical thinking ability.X