Abstract
Linear models are well suited for regression analyses when the response variable is continuous and at least approximately normal. In some cases, an appropriate transformation is needed to ensure approximate normality of the response. In addition, the expectation of the response is assumed to be a linear combination of covariates. Again, these covariates may be transformed before being included in the linear predictor. However, in many applications, the response is not a continuous variable, but rather binary, categorical, or a count variable as in the following examples.
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Fahrmeir, L., Kneib, T., Lang, S., Marx, B.D. (2021). Generalized Linear Models. In: Regression. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63882-8_5
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DOI: https://doi.org/10.1007/978-3-662-63882-8_5
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