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FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES

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FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES /
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IST3017Statistical Computation and Data Analysis4+0+0ECTS:6
Year / SemesterFall Semester
Level of CourseFirst Cycle
Status Elective
DepartmentDEPARTMENT of STATISTICS and COMPUTER SCIENCES
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 4 hours of lectures per week
LecturerDoç. Dr. Orhan KESEMEN
Co-LecturerPROF. DR. Zafer KÜÇÜK
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
Providing descriptive information about the datasets that can be encountered in almost every basic science. Determining the appropriate analysis methods for the structures of the datasets and giving them how to apply these methods.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : classify the data set1,2,3,41
LO - 2 : determine the analysis method ragarding the data set1,2,3,41
LO - 3 : make a decision for about the hypothesis applying the analysis method on the data set1,2,3,41
LO - 4 : draw various graphics regarding the data sets1,2,3,41
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), LO : Learning Outcome

 
Contents of the Course
Stages of statistical data analysis, exploratory and generalizing data analysis, odd number statistics, data representation, graphical representations, branch-leaf, box and root representation, data transformation, data editing, durable lines, tables, analysis techniques.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Basic definitions and concepts
 Week 2Cases of non-parametric tests were used. Normality analysis.
 Week 3Hypothesis testing related to the average
 Week 4Statistical tests for the data collected from a single sample.
 Week 5Statistical tests for two independent samples
 Week 6Statistical tests used in the data collected from the two-dependent samples .
 Week 7Variance analysis.
 Week 8Doubly comparison after variance analysis.
 Week 9Mid-term exam
 Week 10Chi-Square test for independence.
 Week 11Regression analysis.
 Week 12Simple linear regression model.
 Week 13 Explained and unexplained changes.
 Week 14Determination of correlation coefficient.
 Week 15Control of the importance of variance.
 Week 16End-of-term exam
 
Textbook / Material
1Özdamar, K., 1999; Paket programlar ile istatistiksel veri analizi, Kan Kitabevi, Eskişehir.
 
Recommended Reading
1Ünver, Ö., 2006; SPSS Uygulamalı Temel İstatistik Yöntemler, Seçkin Yayıncılık, Ankara.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 24/11/2021 1,5 50
End-of-term exam 16 13/01/2022 1,5 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 4 14 56
Sınıf dışı çalışma 3 14 42
Arasınav için hazırlık 11 1 11
Arasınav 1.5 1 1.5
Proje 10 1 10
Dönem sonu sınavı için hazırlık 18 1 18
Dönem sonu sınavı 1.5 1 1.5
Total work load140