Top Ten Confusing Stats Terms Explained in “Plain English” (#8: Residual)

When I hear the word "residual", the pulp left over after I drink my orange juice pops into my brain, or perhaps the film left on the car after a heavy rain. However, when my regression model spits out an estimate of my model's residual, I'm fairly confident it isn't referring to OJ or automobile gunk...right? Not so fast, that imagery is more similar to it's statistical meaning than you might initially think.

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Top Ten Confusing Stats Terms Explained in “Plain English” (#9: Multicollinearity)

Multicollinearity said in "plain English" is redundancy. Unfortunately, it isn't quite that simple, but it's a good place to start. Put simply, multicollinearity is when two or more predictors in a regression are highly related to one another, such that they do not provide unique and/or independent information to the regression.

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Top Ten Confusing Stats Terms Explained in “Plain English” (#10: Standard Deviation)

Most people find statistics to be complicated, confusing, and just generally frustrating. One of the biggest causes of confusion is the complicated vocabulary that is associated with stats. Frankly, it sometimes seems that stats terms were made to be intentionally complicated. In fact, some concepts seem perfectly understandable when described inplain English, but seem incomprehensible when described in stats lingo.

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