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. 2005 Feb;9(1):112-8.
doi: 10.1186/cc3045. Epub 2005 Jan 13.

Statistics review 14: Logistic regression

Viv Bewick  1 , Liz Cheek, Jonathan Ball
Affiliations

Statistics review 14: Logistic regression

Viv Bewick et al. Crit Care. 2005 Feb.

Abstract

This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. Continuous and categorical explanatory variables are considered.

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Figures

Figure 1
Figure 1
Proportion of deaths plotted against the metabolic marker group midpoints for the data presented in Table 1.
Figure 2
Figure 2
Logit(p) plotted against the metabolic marker group mid-points for the data presented in Table 1.
Figure 3
Figure 3
Likelihood for a range of values of p. MLE, maximum likelihood estimate.
See this image and copyright information in PMC

References

    1. Kirkwood BR, Sterne JAC. Essential Medical Statistics. 2. Oxford, UK: Blackwell Science Ltd; 2003.
    1. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2. New York, USA: John Wiley and Sons; 2000.
    1. Bewick V, Cheek L, Ball J. Statistics review 7: Correlation and regression. Crit Care. 2003;7:451–459. doi: 10.1186/cc2401. - DOI - PMC - PubMed
    1. Bewick V, Cheek L, Ball J. Statistics review 13: Receiver operating characteristic (ROC) curves. Crit Care. 2004;8:508–512. doi: 10.1186/cc3000. - DOI - PMC - PubMed
    1. Bewick V, Cheek L, Ball J. Statistics review 11: Assessing risk. Crit Care. 2004;8:287–291. doi: 10.1186/cc2908. - DOI - PMC - PubMed

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