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Practical guide to logistic regression / Joseph M. Hilbe.

By: Material type: TextTextPublication details: Boca Raton : CRC Press, ©2016.Description: xv, 158 p. : illustrations ; 22 cmISBN:
  • 9781498709576 (pbk. : alk. paper)
Subject(s): DDC classification:
  • 000SA.063 23 H641
Contents:
1. Statistical models -- 2. Logistic models: single predictor -- 3. Logistic models: multiple predictors -- 4. Testing and fitting a logistic model -- 5. Grouped logistic regression -- 6. Bayesian logistic regression.
Summary: Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fisheries, astronomy, transportation, insurance, economics, recreation, and sports. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.063 H641 (Browse shelf(Opens below)) Available 136944
Total holds: 0

Includes bibliographical references and index.

1. Statistical models --
2. Logistic models: single predictor --
3. Logistic models: multiple predictors --
4. Testing and fitting a logistic model --
5. Grouped logistic regression --
6. Bayesian logistic regression.

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fisheries, astronomy, transportation, insurance, economics, recreation, and sports. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another.

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