across several decades" (p. 693). Andreas Graefe applied an equal weighting approach to nine established multiple regression models for forecasting U.S. presidential elections. Across the ten elections from 1976 to 2012, equally weighted predictors reduced the forecast error of the original regression models on average by four percent. An equal-weights model that includes all variables provided calibrated forecasts that reduced the error of the most accurate regression model by 29% percent. Example An example may clarify how unit weights can be useful in practice. Brenna Bry and colleagues (1982) addressed the question of what causes drug use in adolescents. Previous research had made use of multiple regression; with this method, it is natural to look for the best predictor, the one with the highest beta weight. Bry and colleagues noted that one previous study had found that early use of alcohol was the best predictor. Another study had found that alienation from parents was the best predictor. Still another study had found that low grades in school were the best predictor. The failure to replicate was clearly a problem, a problem that could be caused by bouncing betas. Bry and colleagues suggested a different approach: instead of looking for the best predictor, they looked at the number of predictors. In other words, they gave a unit weight to each predictor. Their study had six predictors: 1) low grades in school, 2) lack of affiliation with religion, 3) early age of alcohol use, 4) psychological distress, 5) low self-esteem, and 6) alienation from parents. To convert the predictors to standard form, each risk factor was scored as absent (scored as zero) or present (scored as one). For example, the coding for low grades in school were as follows: "C or higher" = 0, "D or F" = 1. The results showed that the number of risk factors was a good predictor of drug use: adolescents with more risk factors were more likely to use drugs. The model used by Bry and colleagues was that drug users do not differ in any special way from non-drug users. Rather, they differ in the number of problems they must face. "The number of factors an individual must cope with is more important than exactly what those factors are" (p. 277). Given this model, unit-weighted regression is an appropriate method of analysis. Beta weights In standard multiple regression, each predictor is multiplied by a number