Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
BİS607 Experiments Design And Analysis Of 927001 1 2 6
Level of Course Unit
Second Cycle
Objectives of the Course
Experimental scientists and technicians employed in laboratories, industry, medicine or agriculture throughout the world run experiments. The classical experimental approach is to study each experimental variable separately. This one-variable-at-a-time strategy is easy to handle and widely employed. But is it the most efficient way to approach an experimental problem? The first people to ask this question were English agronomists and statisticians working at the beginning of the century. Agronomy is somewhat different from mostexperimental sciences in that there are almost always a large number of variables and each experiment lasts a long time. As they could not run large numbers of trials, they worked to develop the best research strategy. They found that the classical method was not appropriateand developed a revolutionary approach which guaranteed experimenters an optimal research strategy.
Name of Lecturer(s)
Learning Outcomes
  1. To know principles of design of experiments
  2. Comparing fixed, random and mixed effect models
  3. To know differencies between cross and straight classifications
  4. Designing different plans and analysing them
Mode of Delivery
Formal Education
Prerequisites and co-requisities
Recommended Optional Programme Components
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
Course Contents
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Managing Data in SPSS, Creating the Data File
2.One-Way Between-Subjects ANOVA, Mathematical Model,
3.Expectation Under Fixed Effects, Expectation Under Mixed Effects Comparisons of Designs and Estimators, Expectation Under Random Effects
4.Two-Way Between-Subjects ANOVA, Two-Way Crossed Classification without Interaction
5.Two-Way Crossed Classification with Interaction,
6.Two-level factorial designs, the concept of interaction between factors. calculating interaction using the responses, measured at the experimental points.
7.Two-Way Nested Classification
8.Latin-square designs
9.One-Way Within-Subjects ANOVA
10.Two-Way Within-Subjects ANOVA
11.Mixed (Between/Within) Design
12.Three-Way and Higher-Order Crossed Classifications
13.Three-Way Nested Classification
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination130
Attending Lectures1430
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination133
Final Examination133
Attending Lectures14342