c36202b6_nterpreting_Linear_Models_in_R__Name
[#] 1 [Description] The residuals are the difference between the actual values of the variable you're predicting and predicted values from your regression--y - ŷ. For most regressions you want your residuals to look like a normal distribution when plotted. If our residuals are normally distributed, this indicates the mean of the difference between our predictions and the actual values is close to 0 (good) and that when we miss, we're missing both short and long of the actual value, and the likelihood of a miss being far from the actual value gets smaller as the distance from the actual value gets larger. Think of it like a dartboard. A good model is going to hit the bullseye some of the time (but not everytime). When it doesn't hit the bullseye, it's missing in all of the other buckets evenly (i.e. not just missing in the 16 bin) and it also misses closer to the bullseye as opposed to on the outer edges of the dartboard. [Name] 
Residuals
ŷhat | Fitting & Interpreting Linear Models in R
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