Linear and generalized linear models are useful tools for agronomic research, which have been widely used in the analysis of field trials and breeding studies. The aim of this course is to extend student's knowledge of linear and generalized linear models. It provides a solid basis to study other subjects of agronomy. The fundamental theory and general techniques for parameter estimation and statistical inference will be the primary focus. Some important issues in agricultural studies, for example, the estimable functions and testable hypotheses in comparative experiments, will be introduced and explained by using real-world examples. To consider different experimental scenarios, two classes of generalized linear models, including the logistic and Poisson regression models, will be introduced to characterize qualitative outcomes. Students will also learn how to use R to analyze real-world data in this course. After successfully completing this course, students will be able to tackle real-world problems by using linear and generalized linear models, and interpret the analysis results correctly.