Abstract
Linear regression assumes that the spread of the outcome-values is homoscedastic: it is the same for each predictor value. This assumption is, however, not warranted in many real life situations. This chapter is to assess the advantages of weighted least squares (WLS) instead of ordinary least squares (OLS) linear regression analysis.
This chapter was previously published in “Machine learning in medicine-cookbook 1” as Chap. 10, 2013.
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Cleophas, T.J., Zwinderman, A.H. (2020). Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_25
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DOI: https://doi.org/10.1007/978-3-030-33970-8_25
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33969-2
Online ISBN: 978-3-030-33970-8
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