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AM-28 - Semi-variogram modeling
Learning Objectives:
List the possible sources of error in a selected and fitted model of an experimental semi-variogram
Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
Explain the necessity of defining a semi-variogram model for geographic data
Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
Describe some commonly used semi-variogram models
Related Topics:
Spatial sampling for statistical analysis
Kriging Interpolation
Principles of semi-variogram construction
Keywords:
geostatistics
Keywords: