# 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*).