Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
ÇMB232 Data analysis in environmental engineering (TE 2) 927006 2 4 2
Level of Course Unit
First Cycle
Objectives of the Course
Environmental Engineering at the evaluation of the data obtained using statistical methods.
Name of Lecturer(s)
Yrd.doç.dr.andaç Akdemir
Learning Outcomes
  1. Çevre Mühendisliğinde kullanılan verilerin uygunluğunu istatistiksel olarak açıklayabilir
  2. Elde edilen verileri uygun istatistiksel yöntem kullanarak açıklayabilir
  3. Çevre Mühendisliğinde elde edilen veriler ışığında uygun istatistiksel yöntemlerle gelecekteki yaklaşımları hesaplayabilir
  4. Verilerin değerlendirmesi farklı yöntemlerle kullanarak yönetmler arası karşılaştırma yapabilir
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
Spiegel, M.R.; Stephens, L.J. 1999. Temel Problemlerle İstatistik. Novel Yayın Yayıncılık.
Özdamar, K.,2002. Paket Programlar İle İstatistiksel Veri Analizi-I, Kaan Kitabevi
Özdamar, K.,2004. Paket Programlar İle İstatistiksel Veri Analizi-I, Kaan Kitabevi
Kocakoç, İ. 2007. Matlab ve istatistiksel Veri Analizi, Nobel yayıncılık
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
The use of statistical tests in Environmental Engineering, Cluster Analysis, Factor Analysis, ANOVA, MANOVA, Multi regression, Autocorrelation
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Introduction of Basic Statistics
2.Normal Distribution and Distribution Tests
3.Confidence Intervals and Hypothesis Testing
4.Time series analysis
5.Curve Fitting and Method of Least Squares
6.Single and Multiple Correlation
7.Variance Analysis-ANOVA
8.Variance Analysis-MANOVA
9.Non-Parametric Tests and Interpretations
10.Midterms Exam
11.Factor Analysis
12.Cluster Analysis
13.Correspondence Analysis
14.Artificial Neural Networks
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination160
Quiz140
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination122
Final Examination122
Attending Lectures14228
Laboratory122
Tutorial818
Homework188
SUM50