Among explanations below, which one is not a reason to favor a probability model over a regression like model for long-run projections of customer behavior?A. In a regression-like model, it is necessary (and potentially difficult) to project future values for the independent variables.B. Regression like models are fine for a one-period-ahead prediction, but not beyond that horizon.C. If the observed behavior is viewed in an as if random manner, it would be wrong to put it into a regression like model as if its deterministically true.D. Regression like models can’t capture non stationarity (i.e. changes in behavioral propensities over time.E. Its often hard to come up with a full set of independent variables to adequately explain the observed behavior.
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