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Outliers: To Drop or Not to Drop

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Advance thanks. In plot number 2, I do not understand why you want to drop the outlier?? To my point of view, it tells you that the model is rather robust.

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Remind that a statistical model should only been apply for prediction within the data range used for its calibration. The larger the data range, the more robust it will be for predicting in new situations. When cleaning a large dataset for outliers, does a separate outlier analysis have to be run for every single regression analysis one plans on running? For instance, does running 30 different regressions typically require 30 separate outlier analyses?

If so, do the outliers need to be added back into the data set before running the next outlier analysis? If multiple outlier analyses are not required in this case, is just one outlier analysis enough i. After checking all of the above, I do not understand the rationale for keeping an outlier that affects both assumptions and conclusion just by principle. In a survival analysis, maybe somebody died of a car accident but you dont have the death certificate.

Biomarkers cant predict that, neither can most genes. It is not really the outlier there is anything wrong with, but the inability of most parametric tests to deal with 1 or 2 extreme observations.

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If robust estimators are not available, downweighting or dropping a case that changes the entire conclusion of the model seems perfectly fair and reporting it. In example two, the outlier should have little effect on the slope estimate but it ought to have a BIG effect on the standard error of the slope estimate. It would definitely be worth investigating how it came about. A lot might depend on the physical situation involved, whether we are dealing with correlation or with truly independent and dependent variables, etc.

Can we remove outliers based on CV. To lower down CV, change the replication data value but without any change the mean value of treatment. I tried this in some study and the effects are not trivial.


First, my data had some observations which clearly were quite far from the mean sd of over I included them and my parameters were significant all through. I am analysing household consumption expenditure and conclusions based on outliers will most probably be unrepresentative.

I tried the robust errors suggested here as well. I think with outliers their effect is inflating the variances and hence parameter significance robust errors should be enough, as much as we trust the underlying framework. What happens if you take out the outlier, and things become more significant? What would you do in this situation? I have multivariable logistic regression results: With outlier in model p-values are as follows age Our practitioners can assess your tattoo and determine which solution is most appropriate in a free consultation.

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