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Statistical Advisor, Multiple Relationships, Categorical Variables

LOG LINEAR: This chapter discusses a full implementation of the so-called log-linear model for analyzing multiway frequency tables. Using the log-linear model, you can assess the additive as well as multiplicative effects of one or more categorical independent (predictor) variables with a categorical dependent (criterion) variable. A typical application would be to predict staff turnover (a categorical yes/no variable) from various categorical independent variables such as gender, ethnic origin, occupation, etc.

CORRESPONDENCE ANALYSIS: Correspondence analysis is a descriptive/exploratory technique designed to analyze two-way and multi-way tables containing some measure of correspondence (typically frequencies) between the rows and columns. The results provide information which is similar in nature to those produced by factor analysis techniques, and they allow one to explore the structure of categorical variables included in the table (see also multiple correspondence analysis and predictive mapping).

VISUAL GENERALIZED LINEAR MODEL (VGLZ): This chapter discusses an implementation of the generalized linear model and allows you to compute a standard, stepwise, or best subset multiple regression analysis with continuous as well as categorical predictors, and for binomial or multinomial dependent variables (see Link Function).