Ordinary Least Squares Regression Analysis

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 2 of 4

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Regression: Maximum Likelihood Approach

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

Heteroskedasticity: Ch. 4a

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Linear Regression: Concepts and Applications

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

Autocorrelation and Instrumental Variables

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

Regression: Simple and Multiple Linear

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

Linear Regression: Least Squares

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

Linear and Weighted Regression: Optimal Parameters and Local Solutions

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

Least Squares Approximation

Explains least squares approximation for finding best fit lines or curves to data points.