Causation is one of the most critical types of relationship among experimental variables and is often the primary focus of scientific experiments. In this chapter, model fitting is introduced as a method for examining cause-effect relationships in experimental data, using techniques such as the least squares method and variance partitioning (ANOVA). The chapter provides a brief overview of statistical models and their components and presents several commonly used models in agriculture, such as simple linear regression models, one-way and two-way ANOVA models, illustrated through simple yet realistic examples. Additionally, suggestions for handling unbalanced data are provided to prevent misinterpretation in routine research in agriculture.