Introduces Geographically Weighted Regression, a spatially explicit approach to measure relationships between variables with location-specific outputs.
Explores the structured approach to exploratory spatial data analysis, emphasizing the importance of analytical frameworks and the Visual Seeking Mantra.
Introduces simple linear regression, properties of residuals, variance decomposition, and the coefficient of determination in the context of Okun's law.
Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
