Spatial regression or spatial lag model

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Related lectures (30)

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Spatial Dependence: Autocorrelation and Analysis

Introduces spatial dependence, the paradox of classical statistics, and the bias in linear regression.

Geographically Weighted Regression

Introduces Geographically Weighted Regression, a spatially explicit approach to measure relationships between variables with location-specific outputs.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Spatial Dependence: Concepts and Analysis

Explores spatial dependence, spatial autocorrelation, and bias in statistical analysis due to spatial relationships.

Autocorrelation and Spatial Dependence

Covers autocorrelation, spatial dependence, and modeling challenges in GIS, emphasizing the importance of historical data and statistical methods.

Structuring exploratory spatial data analysis

Explores the structured approach to exploratory spatial data analysis, emphasizing the importance of analytical frameworks and the Visual Seeking Mantra.