Abstract

We present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions. Students are guided to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By starting with graphical displays and simple linear regression, students are able to develop additive multiple linear regression models as well as interaction models to accomplish the task. We provide examples of student responses to the activity and suggestions for teachers based on our experiences. Supplementary materials for this article are available online.

摘要

我们提出了一项创新活动，该活动使用有关乐高积木的数据来帮助学生自我发现多元线性回归。学生将被引导使用乐高特性（例如件数、主题（即产品系列）和件的一般尺寸）预测在 Amazon.com 上发布的乐高套装的价格（亚马逊价格）。通过从图形显示和简单的线性回归开始，学生能够开发加法多元线性回归模型以及交互模型来完成任务。我们提供学生对活动的反应示例，​​并根据我们的经验为教师提供建议。本文的补充材料可在线获取。