Homoscedasticity:an overlooked critical assumption for linear regression

• 作者: Justin Tu Kun Yang Tian Chen
• 来源期刊: 综合精神医学(英文)
• 学科: 数学
• 关键词: ASSUMPTION CRITICAL validity
• 年，卷（期）: zhjsyxyw_2019,（5）
• 页码范围: 287-291
• 页数: 5页
• 分类号: O17

摘要

SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedasticity is often taken for granted when fitting linear regression models.However,contrary to popular belief,this assumption actually has a bigger impact on validity of linear regression results than normality.In this report,we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.