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Can you use linear regression on time series?
As I understand, one of the assumptions of linear regression is that the residues are not correlated. With time series data, this is often not the case. If there are autocorrelated residues, then linear regression will not be able to “capture all the trends” in the data.
What violates the assumptions of regression analysis?
Potential assumption violations include: Implicit independent variables: X variables missing from the model. Lack of independence in Y: lack of independence in the Y variable. Outliers: apparent nonnormality by a few data points.
What if assumptions of linear regression are violated?
Violating multicollinearity does not impact prediction, but can impact inference. For example, p-values typically become larger for highly correlated covariates, which can cause statistically significant variables to lack significance. Violating linearity can affect prediction and inference.
How does assumption violation affect multiple linear regression?
Often, the impact of an assumption violation on the multiple linear regression result depends on the extent of the violation (such as the how inconstant the variance of Y is, or how skewed the Y population distribution is).
Why is equal variance assumed in linear regression?
It is linear because we do not see any curve in there. It also meets equal variance assumption because we do not see the residuals “dots” fanning out in any triangular fashion. Linearity assumption is violated – there is a curve.
Why are the results of linear regression unreliable?
Normality: The residuals of the model are normally distributed. If one or more of these assumptions are violated, then the results of our linear regression may be unreliable or even misleading.
What happens when the assumption of normality is violated?
If the assumption of normality is violated, or outliers are present, then the linear regression goodness of fit test may not be the most powerful or informative test available, and this could mean the difference between detecting a linear fit or not.