Prediction in Multiple Regression

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Replication Analysis in Exploratory Factor Analysis: What it is and why it makes your analysis better (2012, methodology): “In other words, as with prediction in multiple regression (e.g., Osborne, 2000; Osborne, 2008), EFA tends over-fit a model to the data—in other words, when the same model is applied to a new sample, the model is rarely as good a fit.”
The Superiority of Simple Alternatives to Regression for Social Science Predictions (2004, background): “(p. 189) More recently, rules of 40m for a stable cross-validity coefficient and 100m for estimating the population regression equation have been suggested (Osborne, 2000).”
Using Meta-Analysis and GIS for Value Transfer and Scaling Up: Valuing Climate Change Induced Losses of European Wetlands (2012, methodology): “Lower and upper bound values are calculated using the 95% prediction intervals for each wetland site, which are computed using the method proposed by Osborne (2000). The prediction intervals provide an indication of the precision with which the estimated value function can predict out of sample valu”
Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data (2018, background): “The calibration models should carry more relationships or underlying constructs between multi-spectral features to shed light on the concentrations of predicted soil available nutrients (Osborne, 2000).”