Least Squares Method Applications II

Related lectures (30)

Page 2 of 3

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

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

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers the basics of linear regression, focusing on estimation and prediction.

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

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

Covers linear regression, regularization, and probabilistic models in generating labels.

Explores linear regression with a focus on maximum likelihood estimation.

Explores linear regression and the least squares method to minimize the loss function for the best fit.

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