Sentence examples for most influential predictions from inspiring English sources

This is a fault that Tetlock and Gardner say is far too common among the talking heads on television and in the newspaper who make the most influential predictions in America, about things like whether the United States can defeat the Islamic State, which candidate will win the Iowa primary, or if the Fed will raise interest rates.

As the model framework can also pinpoint the signals that are the most influential for prediction, it can be used to infer underlying regulatory biology.

A similar regression analysis by De Jaegere et al. in infants <30 weeks gestation found FiO2 by 2 hours to be the most influential variable in prediction of later intubation [ 39].

Most influential for the prediction of 'overall' mini-CEX scores was the trainers' clinical position with a regression coefficient of 0.55 (95 %-CI: 0.26 0.84; p < .001) for residents compared to heads of department.

Predicted PM was the most influential variable for SD prediction, illustrating the relevance of the two-step approach tested.

The features that are most influential in making the predictions are expression correlation; various GO terms, mostly related to metabolism and transport; and the length, abundance, and localization of proteins.

The uncertainties in estimating the values of these three parameters ((s_{1}), (mu_{2}), and (s_{2})) are most influential in determining the prediction of (R_{s}) ((|mathrm{PRCC}|>0.5)) and are statistically significant ((ptext{-value}<0.05)).

The perception of facilitators to telemonitoring adoption, the compatibility of this technology with clinical practices and its perceived usefulness are the most influential variables in the prediction of nurses', GPs', and paediatricians' intention to use this new technology.

Multivariate analyses including treatment type, age, sex, B symptoms, stage, bulk, albumin, sodium, alkaline phosphatase, aspartate aminotransferase, lactate dehydrogenase and beta-2 microglobulin, placed beta-2 microglobulin among the three most influential independent variables for prediction of response rate, duration of remission and overall survival.

In the context of CZM, the shear fracture toughness (GIIC) is one of the most influential parameters for the strength prediction.

In this paper Artificial Neural Network based Yearly Auto-Regressive (ANNYAR) model have been used to figure out the most influential parameter's affecting wind prediction and corresponding range of yearly data set required for Time Horizon (TH) extending from 6 to 96 h.