# Complex Samples Logistic Regression Statistics

Model Fit. Controls the display of statistics that measure the overall model performance.

• Pseudo R-square. The R 2 statistic from linear regression does not have an exact counterpart among logistic regression models. There are, instead, multiple measures that attempt to mimic the properties of the R 2 statistic.
• Classification table. Displays the tabulated cross-classifications of the observed category by the model-predicted category on the dependent variable.

Parameters. This group allows you to control the display of statistics related to the model parameters.

• Estimate. Displays estimates of the coefficients.
• Exponentiated estimate. Displays the base of the natural logarithm raised to the power of the estimates of the coefficients. While the estimate has nice properties for statistical testing, the exponentiated estimate, or exp(B), is easier to interpret.
• Standard error. Displays the standard error for each coefficient estimate.
• Confidence interval. Displays a confidence interval for each coefficient estimate. The confidence level for the interval is set in the Options dialog box.
• T test. Displays a t test of each coefficient estimate. The null hypothesis for each test is that the value of the coefficient is 0.
• Covariances of parameter estimates. Displays an estimate of the covariance matrix for the model coefficients.
• Correlations of parameter estimates. Displays an estimate of the correlation matrix for the model coefficients.
• Design effect. The ratio of the variance of the estimate to the variance obtained by assuming that the sample is a simple random sample. This is a measure of the effect of specifying a complex design, where values further from 1 indicate greater effects.
• Square root of design effect. This is a measure of the effect of specifying a complex design, where values further from 1 indicate greater effects.

Summary statistics for model variables. Displays summary information about the dependent variable, covariates, and factors.

Sample design information. Displays summary information about the sample, including the unweighted count and the population size.

To Obtain Statistics for Complex Samples Logistic Regression

This feature requires the Complex Samples option.