Suchergebnisse

"A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two- and three-way contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available"--

Scratching down numbers (stem-and-leaf) -- Schematic summaries (pictures and numbers) -- Easy re-expression -- Effective comparison (including well-chosen expression) -- Plots of relationship -- Straightening out plots (using three points) -- Smoothing sequences -- Parallel and wandering schematic plots -- Delineations of batches of points -- Using two-way analyses -- Making two-way analyses -- Advanced fits -- Three-way fits -- Looking in two or more ways at batches of points -- Counted fractions -- Better smoothing -- Counts in bin after bin -- Product-ratio plots -- Shapes of distribution -- Mathematical distributions -- Postscript.