Linear Regression: Least Squares

Related lectures (32)

Page 3 of 4

Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.

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

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

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

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.