Introduce students with basic principles of planning and analyzing and implementation of experiments in quality management. Show the meaning of experiment planning in optimizing processes and quality insurance.

Student responsibilities:

Grading and evaluation of student work over the course of instruction and at a final exam:

Essay 50%, final exam 50%.

Methods of monitoring quality that ensure acquisition of exit competences:

Active participation in class.Continuous monitoring of participation in class and through homework. Checking understanding the subject matter through the partial exam and essay.

Upon successful completion of the course, students will be able to (learning outcomes):

After adopting the specific knowledge from the given course students will be able to: - identify the process and choose the appropriate DOE (design of experiment ) model - specify the shape of selected design using principles of DOE - carry out the experiment process by means that are derived from theory of DOE - analyse the data using modern statistical methods and software solutions - interpret the results - present the results in a way that is commonly used for conducted methodology

Lectures

1. Design of experiment: basic terms, history and development of DOE Basic theory of samples. Systematization of E.P.

2. Variance analysis: one and more changeable factors. Decomposition of sum of squares.

3. Variance analysis - completely random experiment plan, and random blocks.

4. Analysis completely factorial models of experiment, linkage with regression.

5. Meaning interactions in factorial experiment planning. Experiment of higher rank. Experiment in several levels.

6. Movable (rotatable) DOE.Rotation of the axes.

7. Design of experiments with ""parcels"" and ""nests"".

8. Partial DOE-experiment 2k-p type. Generating contrasts, alias.

9. Linear regression: meaning of regression coefficients. Comparison of coefficients for two ore more regression equations.

10. Multiple regression, linear, nonlinear.

11. Special types of regression models.

12. Mathematical models: building model, stepwise regression.

13. Placket-Burman experiments.

14. Taguchi experiments. Orthogonal rows.

15. Design of experiment and process optimizing.

Exercises

1. Arithmetic mean distribution, examples.

2. Analysis of variance, examples.

3. Analysis - random blocks models, examples.

4. Completely factorial DOE in two levels, examples.

5. Completely factorial DOE in several levels, examples.

6. Exam I.

7. Example of experiments with ""parcels"" and ""nests"".

8. Partial DOE, problems.

9. Linear regression, problems.

10. Exam II.

11. Examples of appliance of regression models, problems.

12. Examples of appliance of regression models, problems.

13. Placket-Burman DOE- problems.

14. Taguchi DOE- examples.

15. Exam III.

Compulsory literature:

Douglas C. Montgomery: "Design and Analysis of Experiments"

Robert F. Brewer: "Design of Experiments for Process Improvement and Quality Assurance"

Forrest W. Breyfogle III: "Statistical Methods for testing, Development, and Manufacturing"

Recommended literature: