This book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables,but in this second edition,greater emphasis has been placed on logistic regression. Topics such as logistic discrimination and generalized linear models are also explored. The t
Log-Linear Models and Logistic Regression (Springer Texts in Statistics)
โ Scribed by Ronald Christensen
- Publisher
- Springer
- Year
- 1997
- Tongue
- English
- Leaves
- 501
- Edition
- 2nd
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. The book explores topics such as logistic discrimination and generalised linear models, and builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. It also carefully examines the differences in model interpretations and evaluations that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM, while numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. Throughoutthe book, the treatment is designed for students with prior knowledge of analysis of variance and regression.
โฆ Subjects
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๐ SIMILAR VOLUMES
This book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. Topics such as logistic discrimination and generalized linear models are also explored. The
<p><p>Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is