Linear and generalized linear models are useful tools in an agronomist's toolbox. They are often used to analyze field trials and breeding experiments in agricultural studies. They can also be used to explore input-output relationships in chemical, industrial and pharmaceutical studies. The objective of this course is to introduce fundamental theory of linear models. Several important topics, such as parameter estimation, hypothesis testing and prediction, will be detailed. Linear mixed models and generalized linear models will also be introduced to analyze more complicated data. In addition, R programs will be provided to implement these statistical methods. After successfully completing this course, students will be able to address real-world research questions using linear and generalized linear models.