Statistical Modelling in GLIM

Abstract

Part 1 Introducing GLIM 3: getting started in GLIM 3. Part 2 Statistical modelling and statistical inference: the Bernoulli distribution for binary data types of variables definition of a statistical model model criticism likelihood-based confidence intervals. Part 3 Normal regression and analysis of variance: the normal distribution and the Box-Cox transformation family link functions and transformations regression models for prediction the use of regression models for calibration fatorial designs midding data. Part 4 Binomial response data: binary responses transformations and link functions contingency table construction from binary data multidimensional contingency tables with a binary response. Part 5: multinomial and Poisson response data. Part 6 Survival data: probability plotting with censored data - the Kaplan-Meier estimator the Weibull distribution the Cox proportional hazards model and the piecewise exponential distribution the logistic and log logistic distributions time-dependent explanatory variables. Appendices: discussion GLIM directives system defined structures in GLIM datasets and macros.