House Price Regressions: Exploring OLS Assumptions

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Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

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

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Explores autocorrelation, instrumental variables, and challenges in regression analysis.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

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

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.