The example in Gill, seeks to explain the number of bill assignments in the first 100 days of the US’ 104th House of Representatives. The response variable is the number of bill assignments in the first 100 days over 20 Committees. The explanatory variables in the example are the number of assignments in the first 100 days of the 103rd House, the number of members on the committee, the number of subcommittees, the log of the number of staff assigned to the committee, a dummy variable indicating whether the committee is a high prestige committee, and an interaction term between the number of subcommittees and the log of the staff size.

The data returned by load are not cleaned to represent the above example.

```
Number of Observations - 20
Number of Variables - 6
Variable name definitions::
BILLS104 - Number of bill assignments in the first 100 days of the
104th House of Representatives.
SIZE - Number of members on the committee.
SUBS - Number of subcommittees.
STAFF - Number of staff members assigned to the committee.
PRESTIGE - PRESTIGE == 1 is a high prestige committee.
BILLS103 - Number of bill assignments in the first 100 days of the
103rd House of Representatives.
Committee names are included as a variable in the data file though not
returned by load.
```

Jeff Gill’s Generalized Linear Models: A Unifited Approach

Used with express permission from the original author, who retains all rights.