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
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a) Regression-like models can’t capture non-stationarity, i.e., changes in behavioral propensities over time
b) In a regression-like model, it is necessary (and potentially difficult) to project future values for the independent variables
c) It’s often hard to come up with a full set of independent variables to adequately explain the observed behavior
d) Regression-like models are fine for a one-period-ahead prediction, but not beyond that horizon
e) 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
b) In a regression-like model, it is necessary (and potentially difficult) to project future values for the independent variables
c) It’s often hard to come up with a full set of independent variables to adequately explain the observed behavior
d) Regression-like models are fine for a one-period-ahead prediction, but not beyond that horizon
e) 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
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Answer:
d
Explanation:
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