10 a.m. - noon
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.


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

10 a.m. - noon
Instructor: Dr. Josefina Lacasa
Capacity: 40 participants

The use of Bayesian models for applied data analysis in biological sciences is increasing due to their flexibility to tailor said data analysis to a given problem. Due to its widespread use, understanding Bayesian statistics has become as important as classic methods such as t-tests, regression, and ANOVA. Data generated from designed experiments can benefit from using Bayesian models, because they enable researchers (i) to build bespoke statistical models tailored to a specific research question or application, and (ii) to obtain fully probabilistic and statistically valid inference not only on model components (e.g., slope parameters) but also on other indirect quantities of interest (e.g., probability yield is below a certain threshold), and (iii) to adapt the experiment design based on intermediate results and maximize the information gained from an experiment. In this workshop, we aim to enable practitioners to understand the basics of Bayesian models, demystify standard Bayesian techniques such as Markov chain Monte Carlo, and provide real-world, hands-on agricultural data examples where Bayesian models enable new and essential insights from data generated by designed experiments. This workshop is co-hosted by the Department of Statistics.

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.

For questions, please contact Dr. Josefina Lacasa at lacasa@ksu.edu.