GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
LINEAR MODELS/İST-428
Course Title: LINEAR MODELS
Credits 3 ECTS 5
Semester 8 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Assist. Prof. Ufuk EKİZ
 -- WEB SITE(S) OF LECTURER(S)
  http://websitem.gazi.edu.tr/site/ufukekiz
 -- EMAIL(S) OF LECTURER(S)
  ufukekiz@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
A student learns estimation in the full rank model
A student learns estimation in the less than full rank model
A student learns hypothesis testing in the less than full rank model.
 -- 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  Matrix Operations, Transpose, Inverses of Matrices and Orthogonality, Eigenvalues and Rank, Idempotent Matrices and Traces.
2. Week  Moore-Penrose Conditions.
3. Week  Multivariate Normal Distribution
4. Week  Quadratic Forms and Their Distributions, Independence of Quadratic Forms
5. Week  Noncentral Chi-Squared Distribution
6. Week   Full Rank Model
7. Week  Estimation in the Full Rank Model
8. Week  Hypothesis Testing in the Full Rank Model
9. Week  Midterm Exam
10. Week  Less than Full Rank Model
11. Week  Estimable Function
12. Week  Estimation in the Less than Full Rank Model.
13. Week  Hypothesis Testing in the Less than Full Rank Model
14. Week  One-Way Classification Design with Fixed Effects
15. Week  Two-Factor Design without Interaction: Fixed Effects
16. Week  Final Exam
 -- RECOMMENDED OR REQUIRED READING
  1. An introduction to linear statistical models Graybill, Franklin A. New York : McGraw-Hill, c1961. Linear models Searle, Shayle R. New York : Wiley, 1971. 2. Linear models Searle, Shayle R. New York : Wiley, 1971. 3. Koch, Karl-Rudolph. Parameter estimation and hypothesis testing in linear models / Karl-Rudolf Koch. Berlin : Springer, c1999.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
   Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  No
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
40
 Assignment
0
0
 Exercises
0
0
 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
2
1
2
 Practising Hours of Course Per Week
1
1
1
 Reading
6
4
24
 Searching in Internet and Library
1
4
4
 Designing and Applying Materials
2
5
10
 Preparing Reports
6
5
30
 Preparing Presentation
1
6
6
 Presentation
1
5
5
 Mid-Term and Studying for Mid-Term
1
20
20
 Final and Studying for Final
1
20
20
 Other
0
0
0
 TOTAL WORKLOAD: 
122
 TOTAL WORKLOAD / 25: 
4.88
 ECTS: 
5
 -- 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