| Course Description |
|
| Course Name |
: |
Computer Aided Statistical Methods -II |
|
| Course Code |
: |
ISB-540 |
|
| Course Type |
: |
Optional |
|
| Level of Course |
: |
Second Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Spring (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. GÜZİN YÜKSEL
|
|
| Learning Outcomes of the Course |
: |
Have the ability to analyze data and information skills.
Have the ability to find solutions to the problems of operational work.
Learn how to use SPSS.
Have the ability to analyze data with SPSS.
Have the ability to apply statistics structures using SPSS in a business environment.
Develop the skills in problem analysis and problem solving
Develop the skills in data handling and manipulation
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
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 Contents |
: |
Data Entry, Repeated Measure ANOVA, Nested Factors ANOVA, Nonparametric 1- Sample Tests, Nonparametric 2-Independent Sample Tests, Nonparametric 2-Related Sample Tests, Nonparametric K-Related Sample Tests, Loglinear Analysis, Logistic Regression Analysis, Factor Analysis, Discriminant Analysis, Cluster Analysis
|
|
| Language of Instruction |
: |
Turkish |
|
| Work Place |
: |
Laboratory |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Data Entry, and File Operations. (With computerized statistical software) |
Reading the related source, preparing the project |
Face to face and computer application |
|
2 |
Repeated Measure ANOVA |
Reading the related source, preparing the project |
Face to face and computer application |
|
3 |
Nested Factors ANOVA |
Reading the related source, preparing the project |
Face to face and computer application |
|
4 |
Nonparametric 1- Sample Tests |
Reading the related source, preparing the project |
Face to face and computer application |
|
5 |
Nonparametric Two Independent Sample Tests |
Reading the related source, preparing the project |
Face to face and computer application |
|
6 |
Nonparametric Two Independent Sample Tests |
Reading the related source, preparing the project |
Face to face and computer application |
|
7 |
Nonparametric 2-Related Sample Tests |
Reading the related source, preparing the project |
Face to face and computer application |
|
8 |
Midterm |
Review the topics discussed in the lecture notes and sources |
Written Exam |
|
9 |
Nonparametric K-Related Sample Tests |
Reading the related source, preparing the project |
Face to face and computer application |
|
10 |
Loglinear Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
11 |
Logistic Regression Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
12 |
Logistic Regression Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
13 |
Factor Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
14 |
Discriminant Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
15 |
Cluster Analysis |
Reading the related source, preparing the project |
Face to face and computer application |
|
16/17 |
Final Exam |
Review the topics discussed in the lecture notes and sources |
Written Exam |
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. |
4 |
|
2 |
Conduct scientific research on Mathematics, Probability and Statistics. |
5 |
|
3 |
Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. |
4 |
|
4 |
Possess comprehensive information on the analysis and modeling methods used in Statistics. |
4 |
|
5 |
Present the methods used in analysis and modeling in the field of Statistics. |
5 |
|
6 |
Discuss the problems in the field of Statistics. |
5 |
|
7 |
Implement innovative methods for resolving problems in the field of Statistics. |
4 |
|
8 |
Develop analytical modeling and experimental research designs to implement solutions. |
5 |
|
9 |
Gather data in order to complete a research. |
3 |
|
10 |
Develop approaches for solving complex problems by taking responsibility. |
4 |
|
11 |
Take responsibility with self-confidence. |
4 |
|
12 |
Have the awareness of new and emerging applications in the profession |
4 |
|
13 |
Present the results of their studies at national and international environments clearly in oral or written form. |
4 |
|
14 |
Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. |
4 |
|
15 |
Update his/her knowledge and skills in statistics and related fields continously |
3 |
|
16 |
Communicate effectively in oral and written form both in Turkish and English. |
3 |
|
17 |
Use hardware and software required for statistical applications. |
5 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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