ISB539 Computer Aided Statistical Methods -I

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code ISB539
Name Computer Aided Statistical Methods -I
Term 2022-2023 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. GÜZİN YÜKSEL


Course Goal / Objective

The purpose of this course is to supply the students with the ability to analyze and interpret the problems both theoretically and practically by using the data analysis program used in different fields and basic statistical methods in the SPSS.

Course Content

Introduction to basic computer skills, Preparation of data, Descriptive Statistics, Correlation, Statistical Tests, Tests for means, ANOVA Analysis, Regression Analysis, Coding survey data, Reliability Analysis

Course Precondition

None

Resources

SPSS Paket Programı ile İstatistiksel Veri Analizi Prof Dr. Hamza Erol. Paket Programlar ile İstatistiksel Veri Analizi I, Prof. Dr. Kazım Özdamar

Notes

Course Notes


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Have the ability to analyze data by using SPSS.
LO02 Have the ability to apply statistics structures using SPSS in a business environment.
LO03 Develop the skills in problem analysis and problem solving
LO04 Develop the skills in data handling and manipulation
LO05 Have the ability to interpret the data.
LO06 Have the ability to find solutions to the problems of operational work.
LO07 Learn how to use SPSS in different areas.
LO08 Uses the knowledge by using SPSS documentation and other sources in the internet.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Have in-depth theoretical and practical knowledge about Probability and Statistics
PLO02 Bilgi - Kuramsal, Olgusal They have the knowledge to make doctoral plans in the field of statistics.
PLO03 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge about analysis and modeling methods used in statistics. 5
PLO04 Bilgi - Kuramsal, Olgusal Has comprehensive knowledge of methods used in statistics. 4
PLO05 Bilgi - Kuramsal, Olgusal Make scientific research on Mathematics, Probability and Statistics. 5
PLO06 Bilgi - Kuramsal, Olgusal Indicates statistical problems, develops methods to solve. 4
PLO07 Bilgi - Kuramsal, Olgusal Apply innovative methods to analyze statistical problems. 3
PLO08 Bilgi - Kuramsal, Olgusal Designs and applies the problems faced in the field of analytical modeling and experimental researches. 5
PLO09 Bilgi - Kuramsal, Olgusal Access to information and do research about the source.
PLO10 Bilgi - Kuramsal, Olgusal Develops solution approaches in complex situations and takes responsibility. 3
PLO11 Bilgi - Kuramsal, Olgusal Has the confidence to take responsibility. 3
PLO12 Beceriler - Bilişsel, Uygulamalı They demonstrate being aware of the new and developing practices. 3
PLO13 Beceriler - Bilişsel, Uygulamalı He/She constantly renews himself/herself in statistics and related fields. 4
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Communicate in Turkish and English verbally and in writing.
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Transmits the processes and results of their studies clearly in written and oral form in national and international environments. 3
PLO16 Yetkinlikler - Öğrenme Yetkinliği It considers the social, scientific and ethical values ​​in the collection, processing, use, interpretation and announcement stages of data and in all professional activities. 5
PLO17 Yetkinlikler - Öğrenme Yetkinliği Uses the hardware and software required for statistical applications. 5


Week Plan

Week Topic Preparation Methods
1 Data Entry and File Operations. Reading the source ,preparing the project Öğretim Yöntemleri:
Beyin Fırtınası
2 Creating Tables and Charts. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay, Grup Çalışması
3 Descriptive Statistics. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay
4 Hypothesis Testing. Reading the source ,preparing the project Öğretim Yöntemleri:
Soru-Cevap, Gösterip Yaptırma
5 Cross Tables and Correlation Coefficients. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay
6 Tests for means. Reading the source ,preparing the project Öğretim Yöntemleri:
Beyin Fırtınası, Proje Temelli Öğrenme
7 Tests for means II Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay, Proje Temelli Öğrenme
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Ödev
9 Analysis of Variance and Multiple Comparisons. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay
10 Correlation Analysis and Curve Fitting. Reading the source ,preparing the project Öğretim Yöntemleri:
Tartışma
11 Simple Linear Regression. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay
12 Multiple Regression Analysis. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay, Problem Çözme
13 Reliability Analysis Reading the source ,preparing the project Öğretim Yöntemleri:
Soru-Cevap, Alıştırma ve Uygulama
14 Factor Analysis. Reading the source ,preparing the project Öğretim Yöntemleri:
Örnek Olay
15 Examples Reading the source ,preparing the project Öğretim Yöntemleri:
Benzetim, Örnek Olay
16 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 20.11.2022 01:02