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Learn the differences between OLS and GLS methods for linear regression, and how to test the assumptions of homoscedasticity, autocorrelation, and normality.
Learn the basics of regression analysis, the different types of regression models, and how to use them for forecasting and prediction.
Learn how to choose valid and efficient instruments for two stage least squares (2SLS) regression with tips and examples from famous studies.
Learn how to communicate and visualize the results of generalized linear models and generalized additive models for regression analysis in a clear and engaging way.
Learn how to perform common methods for validating and updating cox regression models over time, such as bootstrap, cross-validation, calibration, and more.
Learn how to use quasi-likelihood, GLMM, GAMM, and GAMLSS to extend GLMs and GAMs for non-normal distributions and complex data structures.
Learn why bandwidth matters in RDD, what are the common methods for selecting it, and what are their pros and cons.
Learn how to use residuals to check and improve your regression model fit and assumptions. Find out how to plot, test, and transform your residuals.
Learn about the latest trends and developments in regression analysis, and how they can boost your machine learning and statistical modeling skills.
Learn what adjusted r squared is, how it differs from r squared, and how to use it to compare regression models in a simple and intuitive way.
Learn how to choose, test, simplify, and report random effects for your generalized linear model (GLM) with these tips and tricks.
Learn why normality of residuals is important for regression analysis, how to check it with graphical and numerical methods, and how to deal with non-normality.
Learn about probit regression, decision trees, random forests, support vector machines, and Bayesian methods for modeling binary outcomes with different predictor…