Linear and generalized linear models have been widely used in agronomic research. Regression models, a subset of linear models, are the most important statistical analysis tool in an agronomist's toolkit. The primary focus of this course is to introduce fundamental theory and practical techniques of regression analysis. Several important topics, including parameter estimation, hypothesis testing, model selection and diagnostics, will be detailed. In addition, two generalized linear models, including logistic and Poisson regression models, will be introduced for analyzing different types of data. R scripts will be provided to implement the analysis procedures. After successfully completing this course, students will be able to address real-world research issues using regression analysis and interpret the analysis results correctly.