| Abstrakt: |
Summary: This book introduces a unified representation-the generalized regression model-of various types of regression models, including the general linear, nonlinear regression, generalized linear, logistic regression, Poisson regression, multinomial regression, and Cox regression models. It also uses a likelihood-based approach for performing statistical inference from statistical evidence consisting of data and its statistical model. The book includes restricted versions of Mathematica and the author's own Statistical Inference Package (SIP) on DVD. The author also supplies the SIP and R code for several likelihood-based inference examples online. |
