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 FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES Course Catalog http://www.ktu.edu.tr/isbb Phone: +90 0462 3773112 FENF
FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES /

 IST3017 Statistical Computation and Data Analysis 4+0+0 ECTS:6 Year / Semester Fall Semester Level of Course First Cycle Status Elective Department DEPARTMENT of STATISTICS and COMPUTER SCIENCES Prerequisites and co-requisites None Mode of Delivery Face to face Contact Hours 14 weeks - 4 hours of lectures per week Lecturer Doç. Dr. Orhan KESEMEN Co-Lecturer PROF. DR. Zafer KÜÇÜK Language of instruction Turkish 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 Outcomes CTPO TOA Upon successful completion of the course, the students will be able to : LO - 1 : classify the data set 1,2,3,4 1 LO - 2 : determine the analysis method ragarding the data set 1,2,3,4 1 LO - 3 : make a decision for about the hypothesis applying the analysis method on the data set 1,2,3,4 1 LO - 4 : draw various graphics regarding the data sets 1,2,3,4 1 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 Week Subject Related Notes / Files Week 1 Basic definitions and concepts Week 2 Cases of non-parametric tests were used. Normality analysis. Week 3 Hypothesis testing related to the average Week 4 Statistical tests for the data collected from a single sample. Week 5 Statistical tests for two independent samples Week 6 Statistical tests used in the data collected from the two-dependent samples . Week 7 Variance analysis. Week 8 Doubly comparison after variance analysis. Week 9 Mid-term exam Week 10 Chi-Square test for independence. Week 11 Regression analysis. Week 12 Simple linear regression model. Week 13 Explained and unexplained changes. Week 14 Determination of correlation coefficient. Week 15 Control of the importance of variance. Week 16 End-of-term exam
 Textbook / Material
 1 Özdamar, K., 1999; Paket programlar ile istatistiksel veri analizi, Kan Kitabevi, Eskişehir.
 Recommended Reading
 1 Ünver, Ö., 2006; SPSS Uygulamalı Temel İstatistik Yöntemler, Seçkin Yayıncılık, Ankara.
 Method of Assessment Type of assessment Week No Date 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 work Duration (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 load 140