Linear Models

W. D. Brinda
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W. D. Brinda: Yale University, Statistics and Data Science

Chapter Chapter 4 in Visualizing Linear Models, 2021, pp 81-102 from Springer

Abstract: Abstract In Chap. 2 , we learned how to do least-squares regression when the set of possible prediction vectors comprise a subspace: the least-squares prediction vector is the orthogonal projection of the data onto that subspace. Now we will formulate probabilistic models for which the possible expectation vectors of the response variable comprise a subspace. Each type of model we discuss has a counterpart in our earlier study of regression, and we will reanalyze the least-squares predictions and coefficients in the context of the model. To make it easy for the reader to compare the material from the two chapters, their structures perfectly parallel each other.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-64167-2_4

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DOI: 10.1007/978-3-030-64167-2_4

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