Course Name Code Semester T+U Hours Credit ECTS
Applied Multivariate Statistical Analysis II MAT 521 0 3 + 0 3 6
Precondition Courses Students are assumed to be familiar with Analysis I-II and Linear Algebra I-II, Probability, Statistics, and Applied Multivariate Statistical Analysis I
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Prof.Dr. HALİM ÖZDEMİR
Course Lecturers
Course Assistants Res.Assist. Emre Kişi and Res.Assist. Tuğba Petik
Course Category Field Proper Education
Course Objective Researchers in biological, physical, and social sciences frequently collect measurements on several variables. Applied Multivariate Statistical Analysis is concerned with statistical methods for describing and analyzing these multivariate data. Data analysis, while interesting with one variable, becomes truly fascinating and challenging when several variables are involved. The purpose of this course is to present the concepts and methods of multivariate analysis at a level that is easily understandable by students who have taken two or more mathematics and statistics courses.
Course Content Inferences about a mean vector. Comparisons of several multivariate means. Multivariate linear regression models.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 He/she uses matrix algebra. Lecture, Question-Answer, Discussion, Drilland Practice, Self Study, Testing, Homework, Project / Design,
2 He/she understands the concept of confidential region about the mean vector. Lecture, Question-Answer, Discussion, Self Study, Testing, Homework, Project / Design,
3 He/she interprets about the mean vector for large sample. Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Self Study, Testing, Homework, Project / Design,
4 He/she interprets about the mean vector when some observations are missing. Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Self Study, Testing, Homework, Project / Design,
5 He/she understands multivariate linear regression models and multivariate multiple linear regression models Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Self Study, Problem Solving, Testing, Homework, Project / Design,
6 Inferences about a mean vector. Comparisons of several multivariate means. Multivariate linear regression models. Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Self Study, Problem Solving, Testing, Homework, Project / Design,
Week Course Topics Preliminary Preparation
1 Inferences about a mean vector [1] pages 177-199
2 Inferences about a mean vector (continuation) [1] pages 199-214
3 Inferences about a mean vector (continuation) [1] pages 214-225
4 Comparisons of several multivariate means [1] pages 226-237
5 Comparisons of several multivariate means (continuation) [1] pages 237-247
6 Comparisons of several multivariate means (continuation) [1] pages 247-259
7 Comparisons of several multivariate means (continuation) [1] pages 259-263
8 Comparisons of several multivariate means (continuation) [1] pages 263-288
9 Multivariate linear regression models [1] pages 291-302
10 Multivariate linear regression models (continuation) [1] pages 302-310
11 Multivariate linear regression models (continuation) [1] pages 310-318
12 Multivariate linear regression models (continuation) [1] pages 318-333
13 Multivariate linear regression models (continuation) [1] pages 333-342
14 Multivariate linear regression models (continuation) [1] pages 342-357
Resources
Course Notes [1] Johnson, R. A. and Wichern, D. W., Applied Multivariate Statistical Analysis, Englewood Cliffs, New Jersey, 1982.
Course Resources [2] Seber, G. A. F., Linear Regression Analysis, John Wiley, New York, 1977.


[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.
Order Program Outcomes Level of Contribution
1 2 3 4 5
0 Develop strategic, political and practice plans and evaluate the results by taking into account the quality process in his/her area of expertise X
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
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. Performans Görevi (Seminer) 40
1. Ödev 20
1. Ara Sınav 40
Total 100
1. Final 40
1. Yıl İçinin Başarıya 60
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