STAT518 STAT. ANALY. OF DESIGNED EXPERIMENTS
| Course Code: | 2460518 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. VİLDA PURUTÇUOĞLU |
| Offered Semester: | Fall Semesters. |
Course Objectives
This course equips master’s students with a comprehensive understanding of the design and statistical analysis of experiments, integrating both theoretical foundations and practical applications. Students will learn to plan and analyze experiments using classical and modern designs—including completely randomized, randomized block, Latin square, factorial, and response surface designs—while addressing randomization, blocking, repeated measures, and mixed-effects modeling. Emphasis is placed on evaluating power and sample-size requirements, applying advanced methods such as generalized linear models and smoothing techniques, and using statistical software (e.g., R, Python, C++, MATLAB) to analyze real datasets. By the end of the course, students will be able to critically design efficient experiments, conduct rigorous statistical analyses, and interpret results to support evidence-based decision-making across diverse applied domains.
Course Content
Randomization theory of experimental design. Principles of blocking. General analysis of experimental design models. Construction and analysis of balanced and partially balanced complete and incomplete block designs. Factorial design: confounding, aliasing, fractional replication. Designs for special situations.
Course Learning Outcomes
By the end of this course, students will be able to design experiments effectively using principles of randomization, blocking, factorial arrangements, Latin squares, and response surface methodology; apply appropriate statistical models including ANOVA, regression, mixed-effects models, and generalized linear models to analyze experimental data; evaluate experimental efficiency through power analysis, sample-size determination, and assessment of design assumptions; implement advanced analysis techniques such as repeated measures analysis, interaction modeling, and smoothing approaches for complex data; use statistical software to perform analyses, visualize results, and ensure reproducibility; interpret and communicate results clearly, providing rigorous conclusions that support decision-making in applied research contexts; and lastly, critically assess and compare experimental designs, identifying strengths, limitations, and suitability for various practical scenarios.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Ability for converting theoretical, methodological, and computational statistical knowledge into analytical solutions in researches requiring statistical analyses. | ✔ | |||
| 2 | Ability for specifiying problems in real life situations bearing uncertainty, forming hypotheses, modeling, application, and interpreting the results. | ✔ | |||
| 3 | Ability for using current technology, computer softwares for statistical applications, computer programming for specific problems when necessary, writing computer codes for speeding up statistical calculations, organizing and cleaning databases, and preparing them for statistical analyses, and data mining. | ✔ | |||
| 4 | Ability for taking part in intra/inter disciplinary team work, efficient use of time, taking responsibility as a team leader, and entrepreneurship. | ✔ | |||
| 5 | Ability for taking responsibility in solitary work and producing creative solutions. | ✔ | |||
| 6 | Ability for keeping up-to-date with current advancements in statistical sciences, doing research, being open-minded, and adopting critical thinking. | ✔ | |||
| 7 | Ability for effective communication both in Turkish and English in specification of statistical problems, analyes, and interpretation of findings. | ✔ | |||
| 8 | Ability for using the knowledge in the field of expertise for the welfare of the society. | ✔ | |||
| 9 | Ability for suggesting the researchers in a comprehensible way the appropriate statistical methods for problems in fields that use statistics such as economics, finance, industrial engineering, genetics, and medicine and apply if needed. | ✔ | |||
| 10 | Ability for catalyzing discussions and presentations, public speaking, making presentations, communicating topics of expertise to the audiance in a comprehensible way. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution