Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of fail, and 0 of not-fail, or pass. The important point is that 1 indicates the foremost subject of interest for which a binary response study is designed. Modeling a binary response variable using normal linear regression introduces substantial bias into the parameter estimates. The standard linear model assumes that the response and error terms are normally or Gaussian distributed, that the variance, σ 2, is constant across observations, and that observations in the model are independent. When a binary variable is modeled using this method, the first two of the above assumptions are violated....
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References and Further Reading
Collett D (2003) Modeling binary regression, 2nd edn. Chapman & Hall/CRC Cox, London
Cox DR, Snell EJ (1989) Analysis of binary data, 2nd edn. Chapman & Hall, London
Hardin JW, Hilbe JM (2007) Generalized linear models and extensions, 2nd edn. Stata Press, College Station
Hilbe JM (2009) Logistic regression models. Chapman & Hall/CRC Press, Boca Raton
Hosmer D, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York
Kleinbaum DG (1994) Logistic regression; a self-teaching guide. Springer, New York
Long JS (1997) Regression models for categorical and limited dependent variables. Sage, Thousand Oaks
McCullagh P, Nelder J (1989) Generalized linear models, 2nd edn. Chapman & Hall, London
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Hilbe, J.M. (2011). Logistic Regression. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_344
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