Logistic Regression Models
Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The...
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, FL
CRC Press
2009
©2009 |
| Series: | Chapman & Hall/CRC texts in statistical science series
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| Subjects: | |
| Online Access: | Click Here to View Status and Holdings. |
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| 005 | 2020719111457 | ||
| 008 | 200819s2009 FLU ag# ##001 #deng#D | ||
| 020 | # | # | |a 9781420075755 |q alk. paper |
| 020 | # | # | |a 1420075756 |q alk. paper |
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| 041 | 0 | # | |a English |
| 090 | 0 | 0 | |a QA278.2 |b .H53 2009 |
| 100 | 1 | # | |a Hilbe, Joseph |e author |
| 245 | 1 | 0 | |a Logistic Regression Models |c Joseph M.Hilbe |
| 264 | # | 1 | |a Boca Raton, FL |b CRC Press |c 2009 |
| 264 | # | 1 | |c ©2009 |
| 300 | # | # | |a xviii,637 pages |b illustrations |c 25 cm |
| 336 | # | # | |a text |2 rdacontent |
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| 338 | # | # | |a volume |2 rdacarrier |
| 490 | 1 | # | |a Chapman & Hall/CRC texts in statistical science series |
| 500 | # | # | |a "A Chapman & Hall book |
| 504 | # | # | |a Includes bibliographical references (p. 613-623) and indexes |
| 520 | # | # | |a Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. |
| 650 | # | 0 | |a Logistic regression analysis |x Data processing |
| 856 | 4 | 0 | |z Click Here to View Status and Holdings. |u https://opac.uitm.edu.my/opac/detailsPage/detailsHome.jsp?tid=446673 |
| 964 | # | # | |c BOK |d CS |
Best practices in logistic regression