## Parameter Estimates

Analyses where some effects are classification
variables yield different parameter estimates
from those observed in a regression setting.
They represent a different additive contribution
for each level value (or combination of level values
for interaction effects), and thus the individual
elements in the table are not as easily interpretable
as they are in multiple regression.
**Figure 17.10:** Parameter Estimates Table

Because the overall level is set by the **INTERCEPT** parameter,
the set of parameters associated with an effect is redundant.
This shows up in the **Parameter Estimates**
table as parameters with degrees of freedom
(**DF**) that are **0** and estimates that are **0**.
An example of this is the parameter for
the **e** level of the **TYPE** variable.

From the **Parameter Estimates** table, ships of types **b** and
**c** have the lowest risk, and ships of type **e** the highest.
The oldest ships (built between 1960 and 1964)
have the lowest risk and ships built between
1965 and 1974 have the highest risk.
Ships operated between 1960 to 1974 have a lower
risk than ships operated between 1975 to 1979.

The analysis provides a table for the complete
fitted model, but you should not use these parameter
estimates and their associated statistics individually
to determine which parameters have an effect.
For further information on parameter estimates and
other features of the Fit window, see Chapter 39, "Fit Analyses."

Related Reading | Generalized Linear Models, Chapter 39. |

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