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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cleophas, T.J., Zwinderman, A.H. (2015). 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/10.1007/978-3-319-15195-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-15195-3_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15194-6
Online ISBN: 978-3-319-15195-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)