##### AM-28 - Semi-variogram modeling

- 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

## AM-29 - Kriging methods