# Weight and Offset (generalized linear mixed models)

**Analysis weight. **The scale parameter is an estimated model
parameter related to the variance of the response. The analysis weights
are "known" values that can vary from observation to observation.
If the analysis weight field is specified, the scale parameter, which
is related to the variance of the response, is divided by the analysis
weight values for each observation. Records with analysis weight values
that are less than or equal to 0 or are missing are not used in the
analysis.

**Offset.** The offset term is a "structural" predictor. Its coefficient is
not estimated by the model but is assumed to have the value 1; thus,
the values of the offset are simply added to the linear predictor
of the target. This is especially useful in Poisson regression models,
where each case may have different levels of exposure to the event
of interest.

For example, when modeling accident rates for individual drivers, there is an important difference between a driver who has been at fault in one accident in three years of experience and a driver who has been at fault in one accident in 25 years! The number of accidents can be modeled as a Poisson or negative binomial response with a log link if the natural log of the experience of the driver is included as an offset term.

Other combinations of distribution and link types would require other transformations of the offset variable.