Homoscedasticity: an overlooked critical assumption for linear regression
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, contr...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2019-10-01
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| Series: | General Psychiatry |
| Online Access: | https://gpsych.bmj.com/content/32/5/e100148.full |
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| _version_ | 1850197516718440448 |
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| author | Kun Yang Justin Tu |
| author_facet | Kun Yang Justin Tu |
| author_sort | Kun Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9fc159ddfdaa439e931db18fac63d52e |
| institution | OA Journals |
| issn | 2517-729X |
| language | English |
| publishDate | 2019-10-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | General Psychiatry |
| spelling | doaj-art-9fc159ddfdaa439e931db18fac63d52e2025-08-20T02:13:07ZengBMJ Publishing GroupGeneral Psychiatry2517-729X2019-10-0132510.1136/gpsych-2019-100148Homoscedasticity: an overlooked critical assumption for linear regressionKun Yang0Justin Tu1Department of Evidence-based Medicine, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of China2 Department of Orthopedics, Emory Healthcare, Emory University, Atlanta, Georgia, USALinear 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.https://gpsych.bmj.com/content/32/5/e100148.full |
| spellingShingle | Kun Yang Justin Tu Homoscedasticity: an overlooked critical assumption for linear regression General Psychiatry |
| title | Homoscedasticity: an overlooked critical assumption for linear regression |
| title_full | Homoscedasticity: an overlooked critical assumption for linear regression |
| title_fullStr | Homoscedasticity: an overlooked critical assumption for linear regression |
| title_full_unstemmed | Homoscedasticity: an overlooked critical assumption for linear regression |
| title_short | Homoscedasticity: an overlooked critical assumption for linear regression |
| title_sort | homoscedasticity an overlooked critical assumption for linear regression |
| url | https://gpsych.bmj.com/content/32/5/e100148.full |
| work_keys_str_mv | AT kunyang homoscedasticityanoverlookedcriticalassumptionforlinearregression AT justintu homoscedasticityanoverlookedcriticalassumptionforlinearregression |