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We deliberately add some uncertainty to the presidential forecast, as compared with the model's internal calculations of it, because statistical models based on a small number of data points — in this case, the eleven presidential elections since 1968, which is when the polling data becomes more robust — tend to underestimate real-world uncertainties because of a problem known as "overfitting".
They aim to discretize by considering the natural groups and prevent partitioning any interval that will possess a small number of data points.
We did this because our samples had a small number of data points, which is common for controlled experiments in software engineering involving humans as participant subjects.
The basic idea is to collect a small number of data points by using a statistical simulation and predict the performance measures of the system for a large set of input parameters based on these.
A restriction on the ability to generalise from econometric results is that a small number of data points may exercise a strong influence on the fit of the n-dimensional plane.
Although this reduces the dependence between individuals, it relies upon a small number of data points.
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In addition to the limitation of linear combinations, solving this optimization problem is only possible for a small number of kernels and small number of data points.
Several criteria exist to choose the preferable model, but the small number of data points (five) is a major drawback in our study.
Because of the small number of data points available, we could not test a wider range of model choices without a high risk of spurious statistical results.
Fuzzy regression is used to fit the small number of data points and the resulting fuzzy uncertainty is incorporated in the estimated probability of failure.
This could be due to the small number of data points.
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