Quick Logit Regression

Creates logit regression for categorical (binary) dependent variables and a list of continuous predictor variables. Use the Generalized Linear Models facilities to perform stepwise and best subset selection of predictors in ANCOVA-like designs, or to analyze large analysis problems with many predictors.

Element Name Description
General
Detail of computed results reported Detail of computed results; if Minimal detail is requested, only the final parameter estimates are reported; if All results is requested, the covariance matrix of parameter estimates and classification odds ratios are also reported. Predicted and residual statistics can be requested as an option; plots of residuals, the fitted function (if applicable), and probability plots of residuals are reported as well if All results is requested.
Missing data deletion Missing data can be casewise deleted or substituted by the respective variable means.
Asymptotic standard errors Select this option to compute asymptotic standard errors for the parameter estimates of the logit regression model. Note that this (Quick Logit Regression) program uses less efficient derivative-free estimation methods, and the estimation of asymptotic standard errors may fail in difficult analysis problems (e.g., when predictors are highly redundant). Use the Generalized Linear Models facilities to handle large or difficult analysis problems, or to perform stepwise or best-subset selection of predictors in ANCOVA-like designs.
User Eta for differencing Use user-defined value of Eta for finite difference computations. The standard errors for the parameter estimates in Quick Logit Regression are computed via finite differencing. Specifically, the matrix of second-order partial derivatives is approximated. In order to obtain accurate estimates for the derivatives, some a priori knowledge is necessary of the reliability of the loss value. You can also use the Generalized Linear Models facilities to apply more powerful estimation techniques with explicit derivatives for difficult analysis problems.
Eta value; 1E- Specifies the negative exponent for a base-10 constant Eta; Eta will be used for the finite difference computations to estimate parameter standard errors. The standard errors for the parameter estimates in Quick Logit Regression are computed via finite differencing. Specifically, the matrix of second-order partial derivatives is approximated. In order to obtain accurate estimates for the derivatives, some a priori knowledge is necessary of the reliability of the loss value. You can also use the Generalized Linear Models facilities to apply more powerful estimation techniques with explicit derivatives for difficult analysis problems.
Number of iterations Specifies the maximum number of iterations to be performed during the parameter estimation.
Convergence criterion Set the convergence criterion value (by default, 0.0001); refer to the Electronic Manual for details.
p, for highlighting p value for highlighting significant results (parameter estimates) in results spreadsheets.
Residual analysis Creates predicted and residual values; if the All results Level of detail is selected, then probability plots, surface plots, etc. are also reported.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.