s would be a good measure of total error except for two things. What's one of them?

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class: middle, center

# Quiz

What could go wrong?

$$ \sum_{i=1}^n\hat{y}_i - {y}_i$$ 

???

First, the sum would increase every time we add a new data point. That means models fit on larger data sets would have bigger errors than models fit on small data sets. That makes no sense, so we work with the mean error.

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class: middle, center

# Quiz

What could go wrong?

$$ \frac{1}{n} \sum_{i=1}^n\hat{y}_i - {y}_i$$ 
