This lecture covers the concept of Ordinary Least Squares Regression (OLS) analysis, focusing on the relationship between variables and the calculation of square errors. It also discusses the first stage restriction criterion and exclusion restrictions in regression models.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Sit do ut fugiat aliquip nostrud ut sunt adipisicing ullamco aute. Aliqua minim ad deserunt nisi laboris sint incididunt laboris nulla. Ea cupidatat cillum voluptate sunt amet et ex mollit laboris.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
