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.
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Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data th
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.
