Basic Analysis and Graphing - SAS

222 Performing Simple Logistic Regression Chapter 7
The Logistic Report
Table 7.1 Description of the Whole Model Test (Continued)
–LogLikelihood
Chi-Square
Prob>ChiSq
Rsquare (U)
AICc
BIC
Observations
(sometimes called
Sum Wgts)
Measures variation, sometimes called uncertainty, in the sample.
Full (the full model) is the negative log-likelihood (or uncertainty) calculated
after fitting the model. The fitting process involves predicting response rates
with a linear model and a logistic response function. This value is minimized
by the fitting process.
Reduced (the reduced model) is the negative log-likelihood (or uncertainty)
for the case when the probabilities are estimated by fixed background rates.
This is the background uncertainty when the model has no effects.
The difference of these two negative log-likelihoods is the reduction due to
fitting the model. Two times this value is the likelihood-ratio Chi-square test
statistic.
The likelihood-ratio Chi-square test of the hypothesis that the model fits no
better than fixed response rates across the whole sample. It is twice the
–LogLikelihood for the Difference Model. It is two times the difference of
two negative log-likelihoods, one with whole-population response
probabilities and one with each-population response rates.
For more information, see “Statistical Details for the Whole Model Test
Report” on page 233.
The observed significance probability, often called the p value, for the
Chi-square test. It is the probability of getting, by chance alone, a Chi-square
value greater than the one computed. Models are often judged significant if
this probability is below 0.05.
The proportion of the total uncertainty that is attributed to the model fit.
To test that the factor variable has no effect on the response, look at the
difference between the following:
• the log-likelihood from the fitted model
• the log-likelihood from the model that uses horizontal lines
For more information, see “Statistical Details for the Whole Model Test
Report” on page 233.
The corrected Akaike Information Criterion.
The Bayesian Information Criterion.
The total sample size used in computations. If you specified a Weight
variable, this is the sum of the weights.