query: How outliers influence your results? and what are good and bad leverage points?
positive_passages.docid: 27997
positive_passages.text: Precise meaning of and comparison between influential point, high leverage point, and outlier?
negative_passages.docid: 30128
negative_passages.text: When are confidence intervals useful?
negative_passages.docid: 7187
negative_passages.text: Explaining to laypeople why bootstrapping works
negative_passages.docid: 41983
negative_passages.text: What happens when I include a squared variable in my regression?
negative_passages.docid: 102141
negative_passages.text: Hacking linear regression
negative_passages.docid: 116610
negative_passages.text: Filtering and Replacing outliers
negative_passages.docid: 20763
negative_passages.text: Why could centering independent variables change the main effects with moderation?
negative_passages.docid: 61192
negative_passages.text: Why are lower p-values not more evidence against the null? Arguments from Johansson 2011
negative_passages.docid: 68925
negative_passages.text: Varying behavior for possible loss of precision
negative_passages.docid: 79636
negative_passages.text: Heteroskedasticity in logistic regression
negative_passages.docid: 143460
negative_passages.text: Non-normality in residuals
negative_passages.docid: 24350
negative_passages.text: Fixed vs Random Effects
negative_passages.docid: 102983
negative_passages.text: (Why) do overfitted models tend to have large coefficients?
negative_passages.docid: 35393
negative_passages.text: Outshone or outshined?
negative_passages.docid: 57829
negative_passages.text: Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
negative_passages.docid: 62360
negative_passages.text: Errors and Residuals
negative_passages.docid: 133753
negative_passages.text: When does the amount of skew or prevalence of outliers make the median preferable to the mean?