Timeline for Why we prefer normal distribution of data in linear regression
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Mar 28, 2022 at 10:48 | history | edited | Nick Cox | CC BY-SA 4.0 |
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| Mar 27, 2022 at 17:50 | comment | added | Demetri Pananos | @Nothing Woolridge notes that justifying the normality assumption via the CLT is not without weakness. First, the factors effecting the error term in may have very different distributions in the population, and the number of factors effecting the error plays effects appropriate the normal approx is. Second, justification using the CLT means that all factors are assumed to effect the error in an additive fashion. If the error is a complicated function of unobserved factors then the CLT does not apply. The connection between minimizing squared error and the gaussian loglik is more tenable. | |
| Mar 27, 2022 at 15:15 | vote | accept | Nothing | ||
| Mar 27, 2022 at 15:15 | comment | added | Nothing | But I found the reason for assuming normal data distribution from Gauss-Markov Theorem you mentioned, thanks! | |
| Mar 27, 2022 at 15:13 | comment | added | Nothing | I think the normality of the error term is not in the same context as this question. The reason for the normality assumption of the error term is mainly by the central limit theorem, as noise is the accumulation of a large number of small little factors. @Demetri Pananos | |
| Mar 27, 2022 at 12:56 | history | answered | Demetri Pananos | CC BY-SA 4.0 |