COURSE SYLLABUS
STATISTICS FOR ENVIRONMENTAL ENGINEERS
| 1 |
Course Title: |
STATISTICS FOR ENVIRONMENTAL ENGINEERS |
| 2 |
Course Code: |
CEV5302 |
| 3 |
Type of Course: |
Compulsory |
| 4 |
Level of Course: |
Second Cycle |
| 5 |
Year of Study: |
1 |
| 6 |
Semester: |
2 |
| 7 |
ECTS Credits Allocated: |
6 |
| 8 |
Theoretical (hour/week): |
3 |
| 9 |
Practice (hour/week) : |
0 |
| 10 |
Laboratory (hour/week) : |
0 |
| 11 |
Prerequisites: |
None |
| 12 |
Recommended optional programme components: |
None |
| 13 |
Language: |
Turkish |
| 14 |
Mode of Delivery: |
Face to face |
| 15 |
Course Coordinator: |
Prof. Dr. ERKAN IŞIGIÇOK |
| 16 |
Course Lecturers: |
|
| 17 |
Contactinformation of the Course Coordinator: |
E-mail: eris@uludag.edu.tr
Telefon: 224 29 41101
Adres: Bursa Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi,Ekonometri Bölümü,Görükle,Bursa |
| 18 |
Website: |
|
| 19 |
Objective of the Course: |
Teaching the statistical methods for using in engineering practices. |
| 20 |
Contribution of the Course to Professional Development |
The course provides knowledge to analyze and interpret the students descriptive and inferential statistics related to the engineering practices. |
| 21 |
Learning Outcomes: |
| 1 |
After the completion of the course, the student will be able to use statistical methods for organizing data obtained from engineering applications.;
|
| 2 |
After the completion of the course, the student will evaluate the data using statistical methods and reach scientific conclusions.;
|
| 3 |
After the completion of the course, the student will be able to use statistical methods for designing scientific studies.;
|
|
| Week |
Theoretical |
Practical |
| 1 |
Definition and importance of statistics. Collection and organization of data. Classifying and grouping of data. |
|
| 2 |
Graphical methods. The mean. Distributions. Variation. Standard deviation. |
|
| 3 |
Tests of hypotheses. Large-sample tests. |
|
| 4 |
Tests of hypotheses. Small-sample tests. Confidence interval. |
|
| 5 |
Correlation. |
|
| 6 |
Correlation. |
|
| 7 |
Regression. Coefficient of determination. The method of least squares. |
|
| 8 |
Regression. Checking model adequacy. |
|
| 9 |
Analysis of variance. |
|
| 10 |
Analysis of variance. |
|
| 11 |
Analysis of variance. |
|
| 12 |
Basic principles of experimental design. |
|
| 13 |
Methodology for experimental design. |
|
| 14 |
Experimental design. Randomized complete block design. Factorial design. |
|
| 23 |
Textbooks, References and/or Other Materials: |
Freund J.E. and Simon G.A. (1992) Modern Elementary Statistics, Prentice-Hall Inc., Englewood Cliffs, N.J., ISBN:0-13-587825-X.
|
| 24 |
Assesment |
|
| TERM LEARNING ACTIVITIES |
NUMBER |
PERCENT |
| Midterm Exam |
2 |
40 |
| Quiz |
0 |
0 |
| Homeworks, Performances |
0 |
0 |
| Final Exam |
1 |
60 |
| Total |
3 |
100 |
| Contribution of Term (Year) Learning Activities to Success Grade |
40 |
| Contribution of Final Exam to Success Grade |
60 |
| Total |
100 |
| Measurement and Evaluation Techniques Used in the Course |
multiple choice test questions and written questions |
| Information |
This course is evaluated with an absolute evaluation system. |
| 25 |
|
ECTS / WORK LOAD TABLE |
| Activites |
NUMBER |
TIME [Hour] |
Total WorkLoad [Hour] |
| Theoretical |
14 |
3 |
42 |
| Practicals/Labs |
0 |
0 |
0 |
| Self Study and Preparation |
14 |
4 |
56 |
| Homeworks, Performances |
0 |
0 |
0 |
| Projects |
0 |
0 |
0 |
| Field Studies |
0 |
0 |
0 |
| Midtermexams |
2 |
20 |
40 |
| Others |
0 |
0 |
0 |
| Final Exams |
1 |
40 |
40 |
| Total WorkLoad |
|
|
218 |
| Total workload/ 30 hr |
|
|
5,93 |
| ECTS Credit of the Course |
|
|
6 |
| 26 |
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS |
|
PQ1
|
PQ2
|
PQ3
|
PQ4
|
PQ5
|
PQ6
|
PQ7
|
PQ8
|
PQ9
|
PQ10
|
PQ11
|
PQ12
|
PQ13
|
PQ14
|
PQ15
|
PQ16
|
|
LO1
|
1
|
5
|
3
|
5
|
3
|
2
|
2
|
2
|
2
|
3
|
2
|
2
|
3
|
3
|
0
|
0
|
|
LO2
|
3
|
1
|
2
|
5
|
2
|
3
|
3
|
1
|
2
|
2
|
3
|
3
|
3
|
2
|
0
|
0
|
|
LO3
|
3
|
2
|
1
|
5
|
2
|
2
|
2
|
2
|
2
|
3
|
2
|
3
|
2
|
3
|
0
|
0
|
|
| LO: Learning Objectives |
PQ: Program Qualifications |
| Contribution Level: |
1 Very Low |
2 Low |
3 Medium |
4 High |
5 Very High |
