The Regressinator: A Simulation Tool for Teaching Regression Assumptions and Diagnostics in R

When students learn linear regression, they must learn to use diagnostics to check and improve their models. Model-building is an expert skill requiring the interpretation of diagnostic plots, an understanding of model assumptions, the selection of appropriate changes to remedy problems, and an intuition for how potential problems may affect results. Simulation offers opportunities to practice these skills, and is already widely used to teach important concepts in sampling, probability, and statistical inference. Visual inference, which uses simulation, has also recently been applied to regression instruction. This article presents the regressinator, an R package designed to facilitate simulation and visual inference in regression settings. Simulated regression problems can be easily defined with minimal programming, using the same modeling and plotting code students may already learn. The simulated data can then be used for model diagnostics, visual inference, and other activities, with the package providing functions to facilitate common tasks with a minimum of programming. Example activities covering model diagnostics, statistical power, and model selection are shown for both advanced undergraduate and Ph.D.-level regression courses.

学生在学习线性回归（linear regression）时，必须掌握诊断方法以检验并优化自身构建的模型。模型构建属于一项专业技能，要求学习者能够解读诊断绘图、理解模型假设、选取恰当的修正方案以解决建模问题，并具备预判潜在问题如何影响分析结果的直觉。模拟练习为学习者提供了锤炼上述技能的契机，目前该方法已被广泛用于讲授抽样、概率与统计推断中的核心概念。借助模拟的可视化推断（visual inference）方法，近来也被应用于回归教学领域。本文介绍了regressinator——一款专为回归场景下的模拟与可视化推断设计的R语言包。学习者可借助自身已掌握的建模与绘图代码，仅需编写少量程序即可便捷地定义模拟回归问题。生成的模拟数据可用于模型诊断（model diagnostics）、可视化推断及其他教学活动，该包提供了一系列函数，能够在最大程度缩减编程工作量的前提下完成常见任务。本文还针对高年级本科生与博士阶段的回归课程，提供了涵盖模型诊断、统计功效与模型选择的示例教学活动。