Regression for categorical data /

"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 cas...

Full description

Saved in:
Bibliographic Details
Main Author: Tutz, Gerhard.
Format: Book
Language:English
Published: New York : Cambridge University Press, 2012.
Series:Cambridge series in statistical and probabilistic mathematics.
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
LEADER 02119cam a2200265 a 4500
001 vtls027077229
003 UPM
005 20161020200102.0
008 130611s2012 nyua b 001 0 eng
020 |a 9781107009653 (hbk) 
039 9 |a 201401151221  |b ana  |c 201306261803  |d nuraida  |y 201306111125  |z hadizah 
040 |a DLC  |c DLC  |d DLC 
090 0 0 |a QA278.2 T967 
100 1 |a Tutz, Gerhard. 
245 1 0 |a Regression for categorical data /  |c Gerhard Tutz. 
260 |a New York :  |b Cambridge University Press,  |c 2012. 
300 |a 561p. :  |b ill. ;  |c 26cm. 
490 1 |a Cambridge series in statistical and probabilistic mathematics 
520 |a "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"-- œc Provided by publisher. 
650 0 |a Regression analysis. 
650 0 |a Categories (Mathematics). 
830 0 |a Cambridge series in statistical and probabilistic mathematics. 
942 |2 lcc  |c 10000 
999 |c 52061  |d 52061 
952 |0 0  |1 0  |4 0  |6 QA02782 T967  |7 0  |9 57387  |a 10000  |b 10000  |c 10000  |d 2016-10-20  |l 7  |o QA278.2 T967  |p 1000752936  |r 2023-05-25  |s 2023-04-28  |w 2016-10-20  |y 10000