Instructors: Dr. Trevor Hefley and Claudio Dias da Silva, Jr.  
Capacity: 25 participants 

Non-linear models are widely used in plant sciences. Just like linear models, non-linear models can be fitted to data to enable statistical inference, which is required for scientific investigations and can aid when making management decisions. For example, logistic disease progression curves are a non-linear model used to understand plant disease epidemics, and statistical estimation and inference are needed to connect field data to management decisions (e.g., when to spray fungicide). “Non-Linear Models for the Plant Sciences” is designed to help you develop an understanding of non-linear models and how they fit data using common statistical techniques such as maximum likelihood estimation and Bayesian estimation. This workshop is co-hosted by the Departments of Statistics and Plant Pathology.  

Since model applications will be demonstrated using R software, prior experience will be helpful but not required. Attendees are encouraged to bring their laptops, but they can still follow the content regardless. 

Register here.

For questions, please contact Rebecca Dale at rebeccad@ksu.edu.