GASTRONOMY AND CULINARY ARTS (TURKISH) | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
SEN4016 | Multivariate Data Analysis | Spring | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. MEHMET ALPER TUNGA |
Recommended Optional Program Components: | None. |
Course Objectives: | The students will have the ability of applying specific techniques included in multivariate analysis such as principle component analysis, factor analysis, linear regression to specific problems. |
The students who have succeeded in this course; 1. Describe multivariate data analysis concepts 2. Define the properties and limitations of PCA and compute PCA through different ways 3. Describe the types of factoring and factor computation 4. Define metric and non-metric scales 5. Describe simple and multiple correspondence analysis and chi squared distances 6. Define variations of MANOVA 7. Evaluate regression coefficients, parameter estimation, hypothesis testing 8. Describe deduction, induction, estimation, tests, correlation 9. Define univariate and multivariate filters |
The course content is composed of principle component analysis (pca), factor analysis, multidimensional scaling, correspondence analysis, multivariate analysis of variance (manova), multiple linear regression, statistical inference, feature subset selection. |
Week | Subject | Related Preparation |
1) | Introduction | |
2) | Principle Component Analysis (PCA) | |
3) | Principle Component Analysis (PCA) | |
4) | Factor Analysis | |
5) | Factor Analysis | |
6) | Multidimensional Scaling | |
7) | Correspondence Analysis | |
8) | Multivariate Analysis of Variance (MANOVA) | |
9) | Multiple Linear Regression | |
10) | Multiple Linear Regression | |
11) | Statistical Inference | |
12) | Statistical Inference | |
13) | Feature Subset Selection | |
14) | Feature Subset Selection |
Course Notes / Textbooks: | Multivariate Data Analysis, 7/E, Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson, 2010, 9780138132637 |
References: | Yok - None. |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 4 | % 20 |
Homework Assignments | 2 | % 10 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 4 | 5 | 20 |
Homework Assignments | 2 | 5 | 10 |
Quizzes | 4 | 3 | 12 |
Midterms | 1 | 15 | 15 |
Final | 1 | 17 | 17 |
Total Workload | 116 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | - Possess advanced level theoretical and practical knowledge supported by textbooks with updated information, practice equipments and other resources. | 2 |
2) | Use of advanced theoretical and practical knowledge within the field. -Interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the field. | 4 |
3) | Inform people and institutions, transfer ideas and solution proposals to problems in written and orally on issues in the field. - Share the ideas and solution proposals to problems on issues in the field with professionals and non-professionals by the support of qualitative and quantitative data. -Organize and implement project and activities for social environment with a sense of social responsibility. -Monitor the developments in the field and communicate with peers by using a foreign language at least at a level of European Language Portfolio B1 General Level. -Use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. | 5 |
4) | Evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach. -Determine learning needs and direct the learning. -Develop positive attitude towards lifelong learning. | 3 |
5) | Act in accordance with social, scientific, cultural and ethic values on the stages of gathering, implementation and release of the results of data related to the field. - Possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also, environmental protection, worker's health and security. | 3 |
6) | Conduct studies at an advanced level in the field independently. - Take responsibility both as a team member and individually in order to solve unexpected complex problems faced within the implementations in the field. - Planning and managing activities towards the development of subordinates in the framework of a project | 3 |