Among the explanations below, which one is not a reason to favor a probability model over a regression-like (e.g., data-mining) model for long-run projections of customer behavior? 1. In a regression-like model, it is necessary (and potentially difficult) to project future values for the independent variables 2. It’s often hard to come up with a full set of independent variables to adequately explain the observed behavior 3. Regression-like models are fine for a one-period-ahead prediction, but not beyond that horizon 4. 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 it’s deterministically true 5. Regression-like models can’t capture non-stationarity, i.e., changes in behavioral propensities over time
This question was answered on: Sep 21, 2023
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