1. Autocorrelation: variable correlated on itself. Observations that are “proximate” will have similar values (positive autocorrelation). “Proximate” can be defined in many ways: Closer together in time (1 dimension). Temporal autocorrelation. Closer together in space (2 dimensions). Spatial autocorrelation.

2. Degree of autocorrelation can be calculated: For dependent or independent variables. For regression residuals.

3. Autocorrelation of regression residuals creates problem. Usual view: estimated coefficients are unbiased, but standard errors are biased. Alternative view: autocorrelated residuals signal presence of omitted variables.  estimated coefficients are biased.

4. Residuals: Hedonic House Price Model (Blue paid too little; Red paid too much)

5. Red: Brick; Blue: No Brick

6. Soccer Scores

7. Protein per Capita 1997

8. TB rate 1997

9. HIV rate 1997

10. % children in LF 1995-2000

11. Fertility Rate 1995-2000

12. Per Capita GDP 1995-2000

13. 12 Proximity Matrices 1.Physical Distance 2.Language Phylogeny 3.Religion 4.Huntington Civlization 5.Colonial/Imperial 6.Level of Development 7.Ecology 8.Trade 9.Formal Treaty 10.Allies 11.Enemies 12.Event Frequency

14. The 12 selected languages are on the periphery of the digraph. Links point toward higher taxonomic levels, with all nodes ultimately connected to the node labeled Indo-European. The numbers indicate for selected nodes the maximum path length leading to that node. The taxonomy is from Grimes 2000

15. Language Macro-Families

16. GIS: Ecological Regions