Learn how to fit wide variety of Linear Mixed-Effect (LME) models to make statistical inferences about your data and generate accurate predictions in this new webinar. Mixed-effect models are commonly used in econometrics (Panel Data), biostatistics and sociology (Longitudinal Data) where data is collected and summarized in groups. In these cases LME models with nested or crossed factors can fully incorporate group level contextual effects which cannot be accurately modeled by simple linear regression.

Topics covered in this webinar include:  

• Groups, hierarchy and advantages of LME models
• Preparing and organizing your data to fit LME models
• Specifying LME models using formula notation and design matrices
• Estimating model parameters using maximum likelihood (ML) and restricted maximum likelihood (REML)
• Generating confidence intervals on fixed effects, random effects, and covariance parameters
• Performing residual diagnostics and model comparison tests using theoretical or simulated likelihood ratio tests
• Making predictions on new data using the fitted LME model

About the Presenter: Shashank Prasanna is Product Marketing Manager at the MathWorks focused on MATLAB and add-on products for Statistics, Machine Learning and Data Analytics. His prior experience includes technical support at the MathWorks and software development at Oracle. Shashank holds an M.S. in electrical engineering from Arizona State University.