Once upon a time, a predictive modeler would assemble a data set with 20 variables and maybe 2000 records, then set her favorite statistical package to work on an automated regression procedure to build a model.
Using a forward or backward stepwise algorithm – or a combination of the two approaches – the software would spit out coefficients and p-values. In the final model, the coefficients of all “surviving” variables would be statistically significant, indicating important predictors. These coefficients would, in turn, be used to forecast new observations. Life was good.
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