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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| 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. |
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| ISSN: | 2517-729X |