Estimated error of prediction, assuming multivariate normality.

ols_msep(model)

## Arguments

model |
An object of class `lm` . |

## Value

Estimated error of prediction of the model.

## Details

Computes the estimated mean square error of prediction assuming that both
independent and dependent variables are multivariate normal.
$$MSE(n + 1)(n - 2) / n(n - p - 1)$$

where \(MSE = SSE / (n - p)\), n is the sample size and p is the number of
predictors including the intercept

## References

Stein, C. (1960). “Multiple Regression.” In Contributions to Probability and Statistics: Essays in Honor
of Harold Hotelling, edited by I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, and H. B. Mann,
264–305. Stanford, CA: Stanford University Press.

Darlington, R. B. (1968). “Multiple Regression in Psychological Research and Practice.” Psychological
Bulletin 69:161–182.

## See also

Other model selection criteria: `ols_aic`

,
`ols_apc`

, `ols_fpe`

,
`ols_hsp`

, `ols_mallows_cp`

,
`ols_sbc`

, `ols_sbic`

## Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_msep(model)

#> [1] 8.181044