Ders Bilgileri

#### Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
STATISTICS FOR INFORMATION TECHNOLOGIES ISE 509 0 3 + 0 3 6
 Dersin Dili Türkçe Dersin Seviyesi Yüksek Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi ALPER GÖKSU Dersi Verenler Dersin Yardımcıları Dersin Kategorisi Dersin Amacı 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. Dersin İçeriği 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.
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - To be able to design quantitative research including hypothesis, determining appropriate sample and validation 1 - 4 - 5 - A - C - 2 - To be able to explain the statistical theory and operational procedures necessary for univariate and multivariate analyzes 1 - 2 - 4 - A - C - 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 1 - 2 - 4 - A - C - 4 - To be able to model complex multivariate problems 1 - 2 - 4 - 5 - A - C - 5 - Ability to analyze samples with small volumes and / or missing data 1 - 2 - 4 - A - C - 6 - To be able to design experiments related to group averages and test them with significance tests 1 - 2 - 4 - A - C - 7 - Reduce complex high-dimensional data sets to independent low-dimensional spaces 1 - 2 - 4 - 5 - A - C - 8 - Have the knowledge and ability to divide multivariate data into common subgroups. 1 - 2 - 5 - A - C -
 Öğretim Yöntemleri: 1:Lecture 4:Drilland Practice 5:Demonstration 2:Question-Answer Ölçme Yöntemleri: A:Testing C:Homework

#### Ders Akışı

Hafta Konular ÖnHazırlık
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

#### Kaynaklar

Ders Notu

www.sabis.sakarya.edu.tr course notes will be shared.

Ders Kaynakları

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.

#### Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5

#### Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 50
KisaSinav 1 20
Odev 1 30
Toplam 100
Yıliçinin Başarıya Oranı 50
Finalin Başarıya Oranı 50
Toplam 100

#### AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
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 10 10
Quiz 1 10 10
Assignment 1 10 10
Oral Examination 1 5 5
Performance Task (Application) 1 5 5
Final examination 1 5 5
Toplam İş Yükü 141
Toplam İş Yükü /25(s) 5.64
Dersin AKTS Kredisi 5.64
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