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| Course Description |
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| Course Name |
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Applied Statistics for Engineering |
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| Course Code |
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MTY-501 |
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| Course Type |
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Compulsory |
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| Level of Course |
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Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. RIZVAN EROL
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| Learning Outcomes of the Course |
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Understands and applies statistical design principles in engineering studies.
Selects an appropriate statistical analysis method for the objective and scope of an engineering study.
Performs basic statistical analysis using a computer software.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
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The overall objective of this course is to study statistical methods used to analyze the data collected to solve engineering problems and apply these methods on statistical software. |
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| Course Contents |
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Basic statistical concepts for engineers, normal distribution, hypothesis tests, sampling methods, statistical analysis of performance data of firms, usage of statistical software, experimental design for product and process design, Taguchi methods for quality engineerin |
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| Language of Instruction |
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Turkish |
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| Work Place |
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IE classrooms |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Statistics and probability in engineering, descriptive statistics |
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lecturing, discussion |
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2 |
Basic concepts of probability |
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lecturing, discussion |
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3 |
Basic concepts of statistics |
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lecturing, discussion |
|
4 |
Basic sampling distributions |
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lecturing, discussion |
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5 |
Graphical analysis of data |
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lecturing, discussion |
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6 |
Estimators, random sampling and point estimators |
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lecturing, discussion |
|
7 |
Interval estimators, population mean and variance estimation |
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lecturing, discussion |
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8 |
Hypothesis tests, basic concepts |
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lecturing, discussion |
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9 |
Well-known hypothesis tests |
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lecturing, discussion |
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10 |
Midterm exam |
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written exam |
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11 |
Regession models, model adequacy, multiple regression |
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lecturing, discussion |
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12 |
Regression applications in engineering |
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lecturing, discussion |
|
13 |
Experimental designs, analysis of variance |
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lecturing, discussion |
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14 |
Process optimization via staistical models |
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lecturing, discussion |
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15 |
Project presentations |
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presentations |
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16/17 |
Final Exam |
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written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 MONTGOMERY, D. C., 2003, Applied Statistics And Probability For Engineers, John Wiley&Sons Inc.
WALPOLE, R. E., MYERS, R. H., MYERS, S. L. & YEE, K., 2002, Probability and Statistics for Engineers and Scientists (7th edition), Prentice-Hall Inc.
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
75 |
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Homeworks/Projects/Others |
5 |
25 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Understands, evaluates, interprets and applies knowledge in depth in the field of engineering and technology management, doing scientific research. |
4 |
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2 |
Has comprehensive knowledge about current methods and techniques of engineering and technology management and its limitations. |
4 |
|
3 |
Has the ability of describing and applying knowledge despite limited or missing data; integrates knowledge from different disciplines into the present knowledge.
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3 |
|
4 |
Designs engineering problems, develops techniques to solve them, using innovative ways. |
4 |
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5 |
Has the ability of designing and applying research based on analytical, modelling and experimental approaches and has the ability to solve problems encountered while conducting such research. |
4 |
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6 |
Works in multi-disciplinary teams, takes a leading role and responsibility and develops approaches for compicated solutions. |
3 |
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7 |
Describes, gathers and uses necessary information and data. |
4 |
|
8 |
Has the ability of developing new and/or original ideas or techniques to come up with innovative solutions for designing systems, components or processes. |
3 |
|
9 |
Presents research findings systematically and clearly in oral or written forms in national or international meetings. |
3 |
|
10 |
Understands social and environmental implications of engineering practice. |
3 |
|
11 |
Considers social, scientific and ethical values in all professional activities and while collecting and analysing data and discussing the findings. |
5 |
|
12 |
Keeps up with the latest developments in the field. |
2 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
5 |
7 |
35 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 139 |
| Total Workload / 25 (h): | 5.56 |
| ECTS Credit: | 6 |
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