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  • Course Information
    • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Modeling of Temporal Data on Growth in Forestry ORM640 FALL-SPRING 3+0 Faculty E 6
    Learning Outcomes
    1-Knows the concept of temporal data
    2-Learns the temporal data types measured in trees and stands
    3-Learns error sources specific to temporal data
    4-Learns autoregressive modeling for modeling temporal data
  • ECTS / WORKLOAD
    • ActivityPercentage

    (100)

    		NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14456
    Assignments5014040
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)0000
    Project0000
    Laboratory 0000
    Final exam (exam + preparation) 5013030
    Other 0000
    Total Workload (hours)   168
    Total Workload (hours) / 30 (s)     5,6 ---- (6)
    ECTS Credit   6
  • Course Content
    • Week Topics Study Metarials
    1 Data types measured in trees and stands K6, K7- Introduction Section
    2 The concept of temporal data and its sources in forestry: permanent sample areas and tree stem analysis K6, K7, K8- Introduction Section, K3, K4, K5-Data Sources Section
    3 Sources of error in temporal data K1, K2-Introduction Section
    4 Effects of error sources on predictions in forestry K8- Introduction Section
    5 Modeling techniques for eliminating error sources K1, K2-Modeling Techniques Department, K3, K4- Method Section
    6 Autoregressive modeling-I K3, K4-Autoregressive Modeling Section, K5-Method Section
    7 Autoregressive modeling-II K3, K4-Autoregressive Modeling Section, K5-Method Section
    8 Autoregressive modeling and coding in SAS software-I K1, K2-Statistics Software Section, K3, K4-Autoregressive Modeling Section
    9 Autoregressive modeling and coding in SAS software-II K1, K2-Statistics Software Section, K3, K4-Autoregressive Modeling Section
    10 Covariance structures in autoregressive modeling: AR (1), AR (2), ARMA (1,1), Toeplitz K1, K2-Statistics Software Section, K7, K8-Method Section
    11 Success criteria used to compare different model structures K6, K7-Comparison Criteria Section
    12 Use of mixed-effect models in the modeling of temporal data K2-Introduction Section, K3, K5-Method Section
    13 Coding of mixed effect modeling in SAS software K7, K8-Method Section
    14 Integration of autoregressive modeling and mixed-effect modeling K8-Introduction ve Method Sections
    Prerequisites Yok
    Language of Instruction Turkish
    Responsible Prof.Dr. Zİya ŞİMŞEK
    Instructors -
    Assistants Assoc. Prof. Dr İlker ERCANLI Res. Assist. Ferhat BOLAT
    Resources 1. Searle, S.R., Casella, G., Mc Culloch, C.E., 1992. Variance components, John Wiley and Sons Inc., USA. 2. Littell, R. C., Miliken, G.A., Stroup, W.W., Wolfinger, R.D., 2005. SAS system for mixed models, SAS Institute Inc., Cary,, NC, USA 3. Paulo, J.A., Tomé, J., Tomé, M., 2011. Nonlinear fixed and random generalized height?diameter models for Portuguese cork oak stands. Annals of Forest Science, 68: 295-309 4. Vonesh, E.F., Chinchilli, V.M.,1997. Linear and nonlinear models fo the analysis of repeated measurements, Marcel Dekker, Inc., New York. 5. Ye, S., 2005. Covariance structure selection in linear mixed models for longitudinal data, M. Sc. Thesis, department of Bioinformatics and Biostatistics, University of Lousville, Kentucky, USA. 6. Mehtätalo, L, 2004. A longitudinal height?diameter model for Norway spruce in Finland, Canadian Journal of Forest Research, 34, 131?140 7. Lindstrom ML, Bates DM (1990). Nonlinear mixed effects models for repeated measure data. Biometrics, 46, 673?687 8. Gregoire, T.G., 1987. Generalized error structure for forestry yield models, Forest Science, 33, 423-444.
    Supplementary Book -
    Goals Learning the modeling of temporal data measured at certain time intervals in trees and stands
    Content -
  • Program Learning Outcomes
    • Program Learning Outcomes Level of Contribution
    1 Must learn the methods of both improving the basic sciences and engineering knowledge and obtaining new knowledges at a level of expertise 4
    2 Must be able to design, develop, and apply methods and experiments at advanced level to solve forestry problems, and analyses and interpret their results 5
    3 Must be able to provide solutions for the country?s forestry and environmental problems by considering global, public and ecosystem conditions 4
    4 Must be able to setup interdisciplinary approach to reach an advanced solution for forestry problems 5
    5 Must be able to act in an advanced level of professional ethics and responsibility during the identification and resolution of problems encountered in forestry 4
    6 Must be able to do the task in a single or multi-disciplinary working groups, and be able to show effective communication 4
    7 Must have the ability to effective use of both information technologies and a foreign language at an advanced level 5
    8 Must be able to describe, foresee and solve the current problems in the fields of forestry and other related problems at advanced level brought by current global developments 4
    9 Must be able to use the tools and techniques required for forestry applications at an advanced level 5
    10 Must be able to think, interpret, analyse and synthesize forestry practices at an advanced level by using a three dimensional perspective -
    11 Must be able to research and survey any kinds of natural resources and event, and write advanced reliable reports by using the achieved findings -
    12 Must be able to understand the necessity of life-long learning at an advanced level, and to be able to use the methods that keeps obtained knowledge up date -
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