Spatial Dependence: Concepts and Analysis

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

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

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

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores the assumptions and checking methods for Gaussian linear regression models using residuals and graphical plots.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Introduces data science topics, evaluation methods, and course organization for the semester.

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