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Confidence intervals for predicted outcomes in regression models for categorical outcomes

  • Jun Xu

    (Indiana University)

  • J. Scott Long

    (Indiana University)

Registered author(s):

    We discuss methods for computing confidence intervals for predictions and discrete changes in predictions for regression models for categorical outcomes. The methods include endpoint transformation, the delta method, and bootstrap- ping. We also describe an update to prvalue and prgen from the SPost package, which adds the ability to compute confidence intervals. The article provides several examples that illustrate the application of these methods. Copyright 2005 by StataCorp LP.

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    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 5 (2005)
    Issue (Month): 4 (December)
    Pages: 537-559

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    Handle: RePEc:tsj:stataj:v:5:y:2005:i:4:p:537-559
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    1. Weihua Guan, 2003. "From the help desk: Bootstrapped standard errors," Stata Journal, StataCorp LP, vol. 3(1), pages 71-80, March.
    2. Tim Liao, 2000. "Estimated Precision for Predictions from Generalized Linear Models in Sociological Research," Quality & Quantity: International Journal of Methodology, Springer, vol. 34(2), pages 137-152, May.
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