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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate. 2015): 37-45. [3] 2] Scott, Steven L.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. arXiv preprint arXiv:1506.04416 (2015). [6]

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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

With the rise of advanced technology and globalized operations, statistical analyses grant businesses an insight into solving the extreme uncertainties of the market. While certain topics listed here are likely to stir emotion depending on one’s point of view, their inclusion is for data demonstration purposes only.