Lecture
Model Selection Methods in Biostatistics
Related lectures (32)
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Regression Analysis: Model Selection and Diagnostic Tools
Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.
Applied Biostatistics: Bivariate Data and Regression Analysis
Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.
Bivariate Data Analysis: Correlation and Regression
Explores bivariate data analysis, correlation, and regression techniques for model assessment.
Model Selection: Least Squares
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
General Linear Model: Model Selection
Explores the General Linear Model, significance testing, model selection, and parameter inference.
Linear Regression: Statistical Inference and Regularization
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Applied Biostatistics: Bivariate Data Analysis
Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.
Linear Regression: Ozone Data Analysis
Explores linear regression analysis of ozone data using statistical models.