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The final prediction by FiveThirtyEight on the morning of election day (November 8, 2016) had Hillary Clinton with a 71% chance to win the 2016 United States presidential election, [69] while other major forecasters had predicted Clinton to win with at least an 85% to 99% probability.
Even though the accuracy is 10 + 999000 / 1000000 ≈ 99.9%, 990 out of the 1000 positive predictions are incorrect. The precision of 10 / 10 + 990 = 1% reveals its poor performance. As the classes are so unbalanced, a better metric is the F1 score = 2 × 0.01 × 1 / 0.01 + 1 ≈ 2% (the recall being 10 + 0 / 10 ...
Their final prediction on November 8, 2016, gave Clinton a 71% chance to win the 2016 United States presidential election, [89] while other major forecasters had predicted Clinton to win with at least an 85% to 99% probability.
Confidence bands can be constructed around estimates of the empirical distribution function.Simple theory allows the construction of point-wise confidence intervals, but it is also possible to construct a simultaneous confidence band for the cumulative distribution function as a whole by inverting the Kolmogorov-Smirnov test, or by using non-parametric likelihood methods.
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Download our TSV files for state-by-state averages and state-by-state curves. 2. Likely Vote Counts. Finally, we simulate a Nov. 8 election 10 million times using the state-by-state averages. In each simulation, we generate a result for each state based on: Our state-by-state averages; The uncertainty in our average of national polls
A tolerance interval (TI) is a statistical interval within which, with some confidence level, a specified sampled proportion of a population falls. "More specifically, a 100×p%/100×(1−α) tolerance interval provides limits within which at least a certain proportion (p) of the population falls with a given level of confidence (1−α)."
Prediction intervals are commonly used as definitions of reference ranges, such as reference ranges for blood tests to give an idea of whether a blood test is normal or not. For this purpose, the most commonly used prediction interval is the 95% prediction interval, and a reference range based on it can be called a standard reference range.
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