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Course Information

Course Title	Code	Semester	Laboratory+Practice (Hour)	Pool	Type	ECTS
Mixed Effect Modeling in Forestry	ORM637	FALL	3+0		E	6

Learning Outcomes	1- the problem of serial correlation originating from the source of data 2- the negative effect of serial correlation in modeling prediction 3- the mixed-effect modeling that has been presented as a solution to serial correlation problem 4- the application areas of mixed effect modeling in forestry

ECTS / WORKLOAD

Activity	Percentage (100)	Number	Time (Hours)	Total Workload (hours)
Course Duration (Weeks x Course Hours)		14	3	42
Classroom study (Pre-study, practice)		14	4	56
Assignments	0	1	40	40
Short-Term Exams (exam + preparation)	0	0	0	0
Midterm exams (exam + preparation)	0	0	0	0
Project	50	0	0	0
Laboratory	0	0	0	0
Final exam (exam + preparation)	50	1	30	30
	0	0	0	0
Total Workload (hours)				168
Total Workload (hours) / 30 (s)				5,6 ---- (6)
ECTS Credit				6

Course Content

Week	Topics	Study Metarials
1	The basic assumptions in the regression models	R1, P2
2	The problem of serial correlation and its effect in the regression models	R2, P2
3	The methods presented as a solution to serial correlation	R3, P3
4	The mixed-effect modeling as a solution to the serial correlation	R2, P3
5	Linear and Nonlinear mixed-effect modeling	R3, P2
6	The determination of fixed effect- and random effect-parameters in mixed effect modeling	R4, P2
7	The comparisons of the technique used for fitting the mixed effect modeling	R3, P4
8	The BLUP techniques for the prediction of random-effect parameters in the mixed effect modeling	R4, P5
9	The determination of calibration response in mixed effect modeling	R3, P4
10	The information criteria for used in the comparing of mixed effect modeling	R4, P5
11	The comparing of statistical software used for fitting the mixed-effect modeling	R4, P3
12	The fitting of mixed effect modeling by a statistical package and writing source code-I	R5, P4
13	The fitting of mixed effect modeling by a statistical package and writing source code-II	R4, P5
14	The sample application of mixed effect modeling with actual data	R5, P6

Prerequisites	-
Language of Instruction	Turkish
Responsible	Prof. Dr. İlker ERCANLI
Instructors	-
Assistants	Res. Assist. Dr. Ferhat BOLAT
Resources	1. Galecki, A. & Burzykowski, A. (2013). Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics), Springer Texts in Statistics, ISBN: 1489996672, 576 p. 2. Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R, Springer; Softcover reprint of hardcover 1st ed. 2009 edition, ISBN: 1441927646, 600 p. 3. Fang, Z. (1999). A Simultaneous System of Linear and Nonlinear Mixed Effects Models for Forest Growth and Yield Prediction, Ph.D. Dissertation, University of Georgia. 177 pp. 4. Gregoire, T. G. (1987). Generalized Error Structure for Forestry Yield Models. Forest Science, 33: 423-444. 5. Hall, D. B. & Bailey, R. L. (2001). Modeling and Prediction of Forest Growth Variables Based on Multilevel Nonlinear Mixed Models. For. Sci., 47 (3): 311-321.
Supplementary Book	-
Goals	To teach the mixed-effect modeling that has been presented as a solution to serial correlation problem originating from data obtaining at the various level and that has been commonly used in recent years in forestry
Content	-

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