• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Pile up information about types of experimental designs

   Contribution to Program Outcomes

   1. Apply knowledge in a specialized area of chemical engineering and food technologies disciplines,
   2. Acquire scientific knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Determine suitable experimental methods that fit their experiment

   Contribution to Program Outcomes

   1. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results,
   2. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Analyze and present results of their experiments statistically

   Contribution to Program Outcomes

   1. Find out new methods to improve his/her knowledge.
   2. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Basic concepts of experimental research: definition and terminology of experimental research like model and hypothesis
Week 2:	Completely randomized design: advantages and disadvantages, model and its analysis, ANOVA
Week 3:	Problems in Completely randomized design: solution of examples of Completely randomized design
Week 4:	Mean separation procedures: eror rates, several procedures like Tukey range procedure and Duncan procedure
Week 5:	Inferences about structured means: contrast of means, orthogonal contrast, confidence intervals
Week 6:	Factorial experiments: advantages and disadvantages, models, means, main effects, hypothesis
Week 7:	Problems in factorial experiments:solution of examples of Factorial experiments
Week 8:	I.Midterm
Week 9:	Randomized block design:advantages and disadvantages, models, means, fixed effects, random effects
Week 10:	Problems in randomized block design:solution of examples of Randomized block design
Week 11:	Nested factor experiments:Model and assumptions, sum of the squares, estimation of effect and variance components
Week 12:	Latin square design: advantages and disadvantages, model estimation, inference procedures, efficiency of latin square designs
Week 13:	Split plot design: model estimation split plot design, ANOVA, inferences of split plot design
Week 14:	Fractional factorial design: design considerations, fractional replications, analysis of fractional replicates
Week 15*:	General topic repeat
Week 16*:	Final exam
Textbooks and materials:	Experimental design and Analysis by Marvin Lentner and Thomas Bishop, Valley Book Comp. 2 th Edit.
Recommended readings:	1.Design and Analysis of experiments by Douglas C. Montgomery, Wiley, 6 th Edit. 2. Statistics: The exploration and analysis of data by Jay Devore and Roxy Peck, 2nd Edit. Duxbury press.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	40
Other in-term studies:		0
Project:		0
Homework:	2,3,4,5,6,7,9,10,11,12,13	20
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	2	14
Practice, Recitation:	2	14
Homework:	3	12
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	3	4
Mid-term:	3	4
Personal studies for final exam:	3	4
Final exam:	3	4
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)