The onscreen output includes a row for each of parameter in the following table with a value for estimated value, standard error, z-score, p-value, and significance:
Parameter | Description |
---|---|
Intercept | The value of the logit (Y) when all predictors are 0. |
Predictors | A row for each predictor value (X1,X2,...,Xp). |
The following values are also output in the second column (estimate).
Value | Description (appears in significance column) |
---|---|
ITERATIONS# | The number of Fisher Scoring iterations performed on the function. With Step('true'), the function reports this number for each step.
|
ROWS# | The number of rows of data received as input. |
Residual deviance | The deviance, with degrees of freedom noted in the significance column. Residual deviance is not displayed when the Family is GAMMA, NEGATIVE_BINOMIAL, or INVERSE_GAUSSIAN.
|
Pearson goodness of fit | The sum of squared Pearson’s residual. |
AIC | Akaike information criterion, a measure of the relative quality of the model for the given set of data. |
BIC | Bayesian information criterion, partly based on the likelihood function and closely related to the AIC. BIC is a criterion for model selection among a finite set of models; the model with the lowest BIC is preferred. |
Wald Test | Tests the goodness of fit. |
Dispersion parameter | For GAUSSIAN, the value of this parameter is estimated from the data. For all other families, this parameter has the value 1. |
The coefficients are also stored in the table output_table for later use.
For the Gamma distribution density, AIC and BIC might have the value NaN when the dispersion parameter is very small (for example, 0.00170243) and goodness-of-fit is poor (for example, 0.011).