PRESS (prediction sum of squares) tells you how well the model will predict new data.

## Details

The prediction sum of squares (PRESS) is the sum of squares of the prediction error. Each fitted to obtain the predicted value for the ith observation. Use PRESS to assess your model's predictive ability. Usually, the smaller the PRESS value, the better the model's predictive ability.

## References

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

## See also

Other influence measures:
`ols_hadi()`

,
`ols_leverage()`

,
`ols_pred_rsq()`

## Examples

```
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_press(model)
#> [1] 257.8314
```