STAT466 MULTIVARIATE ANALYSIS II
| Course Code: | 2460466 |
| METU Credit (Theoretical-Laboratory hours/week): | 4 (3.00 - 2.00) |
| ECTS Credit: | 9.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Undergraduate |
| Course Coordinator: | Prof.Dr. BARIŞ SÜRÜCÜ |
| Offered Semester: | Spring Semesters. |
Course Objectives
The proposed course will introduce the fundamental methodology to analyze, model and make inference of multivariate data under parametric approaches. Moreover it presents different techniques in order to decrease the dimension of these data if the computation is untractable.
Course Content
MANOVA. Principal components, factor analysis. Multivariate classification and clustering. Canonical correlation.
Prerequisite: STAT 465
Course Learning Outcomes
- Model high dimensional data via linear regression models
- Inference and model checking in this type of regression models
- Present the methods to summary the data if the dimension of the data is high. This part includes a number of approaches from the principal component analyses to discrimination and classification methods.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions. | ✔ | |||
| 2 | Defining, modeling and solving real life problems that involve uncertainty, and interpreting results. | ✔ | |||
| 3 | To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation. | ✔ | |||
| 4 | Analysing small and big volumes of data and interpreting results. | ✔ | |||
| 5 | Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary. | ✔ | |||
| 6 | Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial. | ✔ | |||
| 7 | Taking responsibility in individual work and offering authentic solutions. | ✔ | |||
| 8 | Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically. | ✔ | |||
| 9 | Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results. | ✔ | |||
| 10 | Having a professional and ethical sense of responsibility. | ✔ | |||
| 11 | Developing computational solutions to statistical problems that cannot be solved analytically. | ✔ | |||
| 12 | Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge. | ✔ | |||
| 13 | Serving the society with the expertise in the field. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution