The SSE (sum of squared errors) is the sum of the squared residuals.
S
The estimate of σ is:
Notation
Term
Description
SSE
sum of the squared residuals
N
number of observations
P
number of free parameters
Pure error lack-of-fit test
Minitab obtains the groups that are determined by distinct combinations of predictor values. Within each group with more than one observation, Minitab calculates the contribution to pure error:
Minitab then sums these contributions across groups.
The degrees of freedom for lack of fit is the degrees of freedom for error minus the degrees of freedom for pure error. The sum of squares for lack of fit is the sum of squares for error minus the sum of squares for pure error.
Minitab calculates the mean squares by dividing the sums of squares by their degrees of freedom.
The F statistic equals the mean square for lack of fit divided by the mean square for pure error.
Notation
Term
Description
n
nth observation
df
degrees of freedom = degrees of freedom for error minus degrees of freedom for pure error
wn
weight for observation n
μw
weighted mean within this group
Residuals
The residual is the difference between an observed value and the corresponding fitted value. This part of the observation is not explained by the model. The residual of an observation is:
