Lectures related to Geographically Weighted Regression

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Regression: Linear Models

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

Linear Regression: Least Squares

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Ordinary Least Squares Regression Analysis

Covers Ordinary Least Squares Regression analysis and regression model restrictions.

Regression Analysis: Multivariate Data Modeling

Introduces regression analysis for multivariate data modeling, covering matrix algebra, interpretation of coefficients, and test intervals.

Linear Models: Ridge, OLS and LASSO

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

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Linear Regression: Estimation and Inference

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

Regression II

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.

Linear Least Squares: Minimizing Error Coefficients

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

Geographically Weighted Regression

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

Linear Regression Basics Log in to Mediaspace to watch this video

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

Linear Regression Basics Log in to Mediaspace to watch this video

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

Basics of linear regression model Log in to Mediaspace to watch this video

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Linear Regression Basics Log in to Mediaspace to watch this video

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

Basics of Linear Regression Log in to Mediaspace to watch this video

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

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

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

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

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