Course Name Code Semester T+U Hours Credit ECTS
Regression Analysis II MAT 525 0 3 + 0 3 6
Precondition Courses Students are assumed to be familiar with Analysis I-II, Linear Algebra I-II, Probability, Statistics, and Regression Analysis I.
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Doç.Dr. NESRİN GÜLER
Course Lecturers
Course Assistants Res.Assist. Emre Kişi
Course Category
Course Objective Applied studies are constructed on the theory. Regression analysis is an often used tool in the statisticians toolbox. The purpose of this course is to give the theory of regression analysis.
Course Content Straight-line regression. Polynomial regression. Analysis of variance. Analysis of covariance. Missing observations. Computational techniques for fitting a specified regression. Choosing the best regression.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 He/she consolidates the concept of linear regression. Lecture, Question-Answer, Drilland Practice, Self Study, Problem Solving, Testing, Homework, Project / Design,
2 He/she understands polynomial regression . Lecture, Question-Answer, Drilland Practice, Self Study, Problem Solving, Testing, Homework, Project / Design,
3 He/she understands that how variance analysis is doing and it is using. Lecture, Question-Answer, Drilland Practice, Motivations to Show, Self Study, Problem Solving, Testing, Homework, Project / Design,
4 He/she understands that how covariance analysis is doing and it is using. Lecture, Question-Answer, Drilland Practice, Motivations to Show, Case Study, Self Study, Problem Solving, Testing, Homework, Project / Design,
5 He/she understands fitting a specified regression. Lecture, Question-Answer, Drilland Practice, Motivations to Show, Self Study, Problem Solving, Testing, Homework, Project / Design,
6 He/she understands choosing the best regression Lecture, Question-Answer, Drilland Practice, Motivations to Show, Self Study, Problem Solving, Testing, Homework, Project / Design,
Week Course Topics Preliminary Preparation
1 Straight-line regression [1] pages 139-150
2 Straight-line regression (continuation) [1] pages 150-163
3 Polynomial regression: Polynomials in one variable and orthogonal polynomials [1] pages 165-172
4 Piecewise Polynomial Fitting [1] pages 172-185
5 Analysis of variance [1] pages 187-222
6 One-way, two-way classification [1] pages 187-222
7 Analysis of covariance [1] pages 222-225
8 Missing observations [1] pages 220-221
9 Computational techniques for fitting a specified regression [1] pages 329-353
10 Computational techniques for fitting a specified regression (continuation) [1] pages 353-376
11 Computational techniques for fitting a specified regression (continuation) [1] pages 376-388
12 Choosing the best regression: Generating all possible regressions [1] pages 399-413
13 Choosing the best regression: Generating the better regressions only [1] pages 439-447
14 Other methods [1] pages 447-456
Resources
Course Notes [1] Seber, G. A. F., Linear Regression Analysis, John Wiley, New York, 1977.
Course Resources [2] Johnson, R. A. and Wichern, D. W., Applied Multivariate Statistical Analysis, Englewood Cliffs, New Jersey, 1982.
[3] Searle, S. R., Matrix Algebra Useful For Statistics, Canada, 1982.
[4] Searle, S. R., Linear Models, John Wiley and Sons, Inc., New York, 1971.
[5] Graybill, F. A., Introduction to Matrices with Applications in Statistics, United States, 1969.
[6] Graybill, F. A., An Introduction to Linear Statistical Models, Volume 1, Mc Graw-Hill Book Co., New York, 1961.
Order Program Outcomes Level of Contribution
1 2 3 4 5
2 Student follows the current journals in his/her field and puts forward problems. X
2 Student follows the current journals in his/her field and puts forward problems. X
3 Student understands the relations between the disciplines pertaining to the undergraduate programs of Mathematics X
3 Student understands the relations between the disciplines pertaining to the undergraduate programs of Mathematics X
4 Student gets new knowledge by relating the already acquired experience and knowledge with the subject-matters out of his/her field. X
4 Student gets new knowledge by relating the already acquired experience and knowledge with the subject-matters out of his/her field. X
5 Student uses different proof methods to come to a solution by analyzing the problems encountered. X
5 Student uses different proof methods to come to a solution by analyzing the problems encountered. X
6 Student determines the problems to be solved within his/her field and if necessary takes the lead. X
6 Student determines the problems to be solved within his/her field and if necessary takes the lead. X
7 Student conveys, in team work, his/her knowledge in the studies done in different disciplines by applying the dynamics pertaining to his/her own field. X
7 Student conveys, in team work, his/her knowledge in the studies done in different disciplines by applying the dynamics pertaining to his/her own field. X
8 Student critically evaluates the knowledge got at the bachelor´s degree level, makes up the missing knowledge and focuses on the current subject-matters X
8 Student critically evaluates the knowledge got at the bachelor´s degree level, makes up the missing knowledge and focuses on the current subject-matters X
9 Student knows a foreign language to communicate orally and in writing and uses the foreign language in a way that he/she can have a command of the Maths terminology and can do a source research. X
9 Student knows a foreign language to communicate orally and in writing and uses the foreign language in a way that he/she can have a command of the Maths terminology and can do a source research. X
10 Student improves himself/herself at a level of expertness in Mathematics or in the fields of application by improving the knowledge got at the bachelor´s degree level. X
10 Student improves himself/herself at a level of expertness in Mathematics or in the fields of application by improving the knowledge got at the bachelor´s degree level. X
10 Student improves himself/herself at a level of expertness in Mathematics or in the fields of application by improving the knowledge got at the bachelor´s degree level. X
11 Student considers the scientific and cultural ethical values in the phases of gathering and conveying data or writing articles. X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 40
1. Ödev 20
1. Performans Görevi (Seminer) 40
Total 100
1. Yıl İçinin Başarıya 60
1. Final 40
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 15 15
Assignment 1 8 8
Performance Task (Seminar) 1 20 20
Final examination 1 20 20
Total Workload 159
Total Workload / 25 (Hours) 6.36
dersAKTSKredisi 6