The Generalized Linear Models (GLZ) allows the user to search for both linear and nonlinear
relationships between a response variable and categorical or continuous predictor variables
(including multinomial logit and probit, signal detection models, and many others). Special
applications of generalized linear models include a number of widely used types of analyses,
such as binomial and multinomial logit and probit regression, or Signal Detection Theory (SDT)
models. The GLZ module will compute all standard results statistics, including likelihood ratio
tests, and Wald and score tests for significant effects, parameter estimates and their standard
errors and confidence intervals, etc. The user-interfaces, methods for specifying designs, and
"touch-and-feel" of the program is similar to GLM, GRM, and PLS. The user is able to easily specify
ANOVA or ANCOVA-like designs, response surface designs, mixture surface designs, etc.; thus, even
novice users will have no difficulty applying generalized linear models to analyze their data. In
addition, GLZ includes a comprehensive selection of model checking tools such as Spreadsheets and
graphs for various residuals and outlier detection statistics, including raw residuals, Pearson
residuals, deviance residuals, studentized Pearson residuals, studentized deviance residuals,
likelihood residuals, differential Chi-square statistics, differential deviance, and generalized
Cook distances, etc.
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