The last 30 years have witnessed a proliferation of statistical
methods for the analysis of qualitative/categorical data, prominent
among which are linear logit (or logistic regression) models.
Unlike the more familiar linear models for regression analysis and
analysis of variance, linear logit models are appropriate for
analyzing qualitative/categorical dependent variables. Like linear
models, logit models are capable of handling one or several
independent variables, which may be both qualitative and quantitative.
This course will introduce logit and related models for dichotomous
(two-category) and polytomous (several-category) dependent variables,
including ordered categories. We shall also consider the application
of logit models to the analysis of multidimensional contingency
tables. A basic understanding of linear least-squares regression
analysis, including dummy-variable regression and analysis of
variance, is assumed.