Türkçe | English
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING
Master of Science in Engineering (With Thesis)
Course Catalog
http://eee.ktu.edu.tr/eng/default_eng.aspx
Phone: +90 0462 3253154 , 3772906
FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING / Master of Science in Engineering (With Thesis)
Katalog Ana Sayfa
  Katalog Ana Sayfa  KTÜ Ana Sayfa   Katalog Ana Sayfa
 
 

FBE5006Advanced Statistics3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
Department
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerProf. Dr. Necati TÜYSÜZ
Co-Lecturer
Language of instruction
Professional practise ( internship ) None
 
The aim of the course:
to aware the students about the statistical concepts and parameters, to have the students gain abilities to adapt the univariate and multivariate statistical methods to any kind of data set, to have the students gain abilities to solve the statistical problems using SPSS program
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Students will be able to be aware of collecting accurate, precise and represantative statistical data, and then organize, summarize and and represent it on a proper way.
PO - 2 : Students will be able to describe characteristics of a sample and make predictions about its population.
PO - 3 : Students will be able to set up a research question as a hypothesis and test it using relevant statistical method(s).
PO - 4 : Students will be able to effectively use SPSS program in thier studies.
PO - 5 : Students will be able to check the differences or smilarities between experimental (empirical) and control data
PO - 6 : Students will be able to test if there is a relationship between dependent and independent(s) factors.
PO - 7 : Students will be able to classify objects according their measurable characteristics and also with respect to several underlying factors.
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Basic statistical concepts and parameters, sampling and statistical estimation theory, statistical decision theory (t-test, F test etc.), ANOVA, corelation and simple linear regression, non parametric tests, multivariate normal distribution and hypothesis testing, Multi ANOVA, multivariate linear regression, Logistic regression, trend surface analysisi, cluster techniques, discriminant anaylsis, PCA, Factor analysis, multivariate scaling, correspondance analysis, canonical corelation.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Basic statistic parameters
 Week 2Statistical decision theory T-test (Student test) F-test
 Week 3Analysis of variance One-way ANOVA
 Week 4Correlation and regression analysis Simple correlation Simple linear regression
 Week 5Non-parametric test Mann-Whitney U test Friedman test
 Week 6 Introduction to eigenvector methods including factor analysis -Q-Mode factor analysis -R-Mode factor analysis
 Week 7multivariate normal distribution and hypothesis testing
 Week 8Mid-term exam
 Week 9Principle component analysis
 Week 10Discriminant Functions
 Week 112. Mid-term exam
 Week 12Multi ANOVA, Multivariate linear regression, Logistic regression
 Week 13 Cluster Analysis
 Week 14Correspondence analysis
 Week 15Canonical correlations
 Week 16Final exam
 
Textbook / Material
1Tüysüz, N., Yaylalı-Abanuz, G., 2012; Jeoistatistik-Kavramlar ve Bilgisayarlı Uygulamalar, KTÜ Yayınları, Yayın No: 220, 382 s.
 
Recommended Reading
1Özdamar, K., 2002; Paket programlar ile istatistiksel veri analizi, Kaan Kitabevi, 2 cilt.
2Hinton, P.R., Brownlow, C., Mac Murray, I., Cozens, B., 2004; SPSS Explained, Routledge Yayınevi, 377 s.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 8 2 30
In-term studies (second mid-term exam) 11 2 20
End-of-term exam 16 3 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 6 10 60
Laboratuar çalışması 0 0 0
Arasınav için hazırlık 3 14 42
Arasınav 3 1 3
Uygulama 2 14 28
Klinik Uygulama 0 0 0
Ödev 0 0 0
Proje 0 0 0
Kısa sınav 0 0 0
Dönem sonu sınavı için hazırlık 10 2 20
Dönem sonu sınavı 3 2 6
Diğer 1 0 0 0
Diğer 2 0 0 0
Total work load201