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• W2534977215 abstract "When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights." @default.
• W2534977215 created "2016-10-28" @default.
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• W2534977215 date "2016-10-18" @default.
• W2534977215 modified "2023-09-23" @default.
• W2534977215 title "On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression" @default.
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• W2534977215 doi "https://doi.org/10.1080/00273171.2016.1225563" @default.
• W2534977215 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27754699" @default.
• W2534977215 hasPublicationYear "2016" @default.
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