Course Name Code Semester T+U Hours Credit ECTS
Statistics For Information Technologies ISE 509 0 3 + 0 3 6
 Precondition Courses Recommended Optional Courses Course Language Turkish Course Level yuksek_lisans Course Type Optional Course Coordinator Dr.Öğr.Üyesi ALPER GÖKSU Course Lecturers Course Assistants Course Category Course Objective Modeling complex multivariable problems, using statistical techniques that can be used in the analysis of multivariate data, interpreting the results of multivariate analyzes and testing their validity. Course Content This course includes advanced statistical techniques used in the analysis and interpretation of data in the areas of information systems: Basic Concepts, Linear Regression Analysis, Multiple Linear Regression Analysis, Dummy Variable Regression Analysis, Nonlinear Regression Analysis, Regulatory and Intermediary Variables, Repeated Sampling, Maximum Likelihood and EM Algorithm, Time Series Analysis, Variance Analysis and Experimental Design, Multivariate Analysis of Variance, Principal Component Analysis, Hierarchical Clustering Methods, Non-hierarchical Clustering Methods and Self-Regulating Maps.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 To be able to design quantitative research including hypothesis, determining appropriate sample and validation Lecture, Drilland Practice, Demonstration, Testing, Homework,
2 To be able to explain the statistical theory and operational procedures necessary for univariate and multivariate analyzes Lecture, Question-Answer, Drilland Practice, Testing, Homework,
3 To be able to model the change in the dependent variable (s) corresponding to the change in the independent variable (s) and evaluate the assumptions underlying the analysis Lecture, Question-Answer, Drilland Practice, Testing, Homework,
4 To be able to model complex multivariate problems Lecture, Question-Answer, Drilland Practice, Demonstration, Testing, Homework,
5 Ability to analyze samples with small volumes and / or missing data Lecture, Question-Answer, Drilland Practice, Testing, Homework,
6 To be able to design experiments related to group averages and test them with significance tests Lecture, Question-Answer, Drilland Practice, Testing, Homework,
7 Reduce complex high-dimensional data sets to independent low-dimensional spaces Lecture, Question-Answer, Drilland Practice, Demonstration, Testing, Homework,
8 Have the knowledge and ability to divide multivariate data into common subgroups. Lecture, Question-Answer, Demonstration, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Sampling and Principles, Measurement, Techniques Introduction
2 Linear Regression Analysis
3 Multiple Linear Regression Analysis
4 Dummy Variable and Nonlinear Regression
5 Regulatory and Intermediary Variables, Repetitive Sampling
6 Maximum Likelihood and EM Algorithm
7 Time Series Analysis
8 Analysis of Variance and Experimental Design
9 Multivariate Analysis of Variance
10 Size Reduction
11 Example applications
12 Clustering Analysis I
13 Clustering Analysis II
14 Clustering Analysis III
Resources
Course Notes <p>www.sabis.sakarya.edu.tr course notes will be shared.</p>
Course Resources

1. Johnson R.A. and Wichern D.W., (2007), Applied Multivariate Statistical Analysis, 6th edition, Pearson, New Jersey.

2. Hair J.F., Anderson R.E., Tatham R.L. and Black W.C., (2009), Multivariate Data Analysis, 7th edition, Prentice Hall, New Jersey.

3. Ramachandran K.M. and Tsokos C.P., (2009), Mathematical Statistics with Applications, Elsevier Academic Press, Burlington.

Order Program Outcomes Level of Contribution
1 2 3 4 5
1
2
3
4
5
6
7
8
9
10
11
12
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 50
1. Kısa Sınav 20
1. Ödev 30
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100