Linear and generalized linear models are useful tools for agronomic research, which have been widely used in the analysis of field trails 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 for further studying other subjects of agronomy. Focus will be placed on introducing the fundamental theory and general techniques for parameter estimation and statistical inference. Some related issues in agricultural studies, for example, the estimable functions and testable hypotheses in comparative experiments, will be illustrated by practical examples. In order to consider different experimental scenarios, two classes of generalized linear models, including the logistic and Poisson regression models, will be introduced for characterizing qualitative outcomes. Students will also learn how to use R to analyze real-life data during lecture. After successfully completing this course, students will be able to tackle real-world problems using linear and generalized linear models, and interpret the analysis results correctly.