Linear Regression: Basics and Applications

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Related lectures (30)

Page 2 of 3

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

Least Squares Solutions

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

Nonparametric Statistics: Bayesian Approach

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

Linear Regression: Maximum Likelihood Approach

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

Linear Regression: Least Squares

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

Linear Regression: Concepts and Applications

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Linear Regression: Fundamentals and Applications

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

Linear Regression

Covers linear regression for estimating train speed using least squares and regularization.

Regression II

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

Linear Regression: Estimation and Prediction

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