Structured regression for categorical data
By: Gerhard Tutz
Series: Cambridge series in statistical and probabilistic mathematics
Publisher: Cambridge University Press
Published: 2011
Place of publication: Cambridge; New York
Language: eng
Number of pages: 561
ISBN-13: 9781107009653
Subjects: Regression analysis; Categories (Mathematics)
Description: "Categorical data play an important role in many statistical analyses. They appear whenever the outcomes of one or more categorical variables are observed. A categorical variable can be seen as a variable for which the possible values form a set of categories, which can be finite or, in the case of count data, infinite. These categories can be records of answers (yes/no) in a questionnaire, diagnoses like normal/abnormal resulting from a medical examination or choices of brands in consumer behaviour. Data of this type are common in all sciences that use quantitative research tools, for example social sciences, economics, biology, genetics and medicine, but also engineering and agriculture. In some applications all of the observed variables are categorical and the resulting data can be summarized in contingency tables which contain the counts for combinations of possible outcomes. In other applications categorical data are collected together with continuous variables and one wants to investigate the dependence of one or more categorical variables on continuous and/or categorical variables"--