STAT-Statistics

Reviews and extends ideas of multiple regression analysis to a wider class of models—Generalized Linear Models (GLMs)—involving the relationship between a response and one or more explanatory variables. Models for the analysis of quantitative and qualitative responses, including multiple regression, analysis of variance, covariance models, binomial models for binary responses (including logistic regression and probit models), and Poisson models for count data. Case studies drawn from social, engineering, and life sciences. (Formerly AMS 174.)

Methods for the design and analysis of computer simulation experiments: random number generation; estimation of sample size necessary to achieve desired precision goals; antithetic variables and other devices for increasing simulation efficiency; analysis of the output of large deterministic computer programs, exploring the sensitivity of outputs to changes in the inputs. Applications drawn mainly from engineering and environmental sciences. (Formerly AMS 162.)