Linear Regression: Beyond the Basics

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Related lectures (32)

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Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

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: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Nonparametric Statistics: Bayesian Approach

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

Regression: Simple and Multiple Linear

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

Applied Biostatistics: Bivariate Data and Regression Analysis

Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.

Applied Biostatistics: Bivariate Data Analysis

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.