Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Explores spatial regression models, addressing spatial autocorrelation challenges and the concept of spatial lag models to correct biases and improve inference accuracy.
Covers global and local deterministic interpolation methods in geographic information systems, discussing expert knowledge, method selection, and uncertainty estimation.
Introduces Geographically Weighted Regression, a spatially explicit approach to measure relationships between variables with location-specific outputs.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
