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Chapter 4: Beyond Linear Regression: The Method of Maximum Likelihood

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Generalized Linear Models With Examples in R

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Abstract

The linear regression model introduced in Chap. 2 assumes the variance is constant, possibly from a normal distribution.

Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.

Box [2, p. 792]

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Dunn, P.K., Smyth, G.K. (2018). Chapter 4: Beyond Linear Regression: The Method of Maximum Likelihood. In: Generalized Linear Models With Examples in R. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0118-7_4

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