Normal Distribution

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Related to Error Distribution: normal curve

Normal Distribution

The well known bell shaped curve. According to the Central Limit Theorem, the probability density function of a large number of independent, identically distributed random numbers will approach the normal distribution. In the fractal family of distributions, the normal distribution only exists when alpha equals 2, or the Hurst exponent equals 0.50. Thus, the normal distribution is a special case which in time series analysis is quite rare. See: Alpha, Central Limit Theorem, Fractal Distribution.

Bell Curve

A curve on a chart in which most data points cluster around the median and become less frequent the farther they fall to either side of the median. When plotted on a chart, a bell curve looks roughly like a bell.
References in periodicals archive ?
2, the error distributions in four continuous forecast bins (range from [0.
The good power prediction performance of the LS-SVM model after error correction can be seen directly from the results of the prediction curve and the error distribution of Figure 3-5.
The estimated CI for the quantile 1-a of the error distribution, once PB is corrected, allows us to determine an upper bound for the magnitude of the prediction error with a certain probability, and to use it in the evaluation of the evolving model whenever an improvement is required.
Assuming that the error distributions shown in Table 5 are applicable, we could say it is probable that absolute Percentage Error will not exceed 4.
In this section, the estimation of phase error distribution with and without PME is discussed.
Although transforms may achieve the desired linearisation and normal distribution, back-transformation of the predicted results to raw data format is subsequently required, leading to skewness and/or increasing bias in the back-transformed error distribution.
S&P/TSX Canadian index) is sensitive to the specification of error distribution.
In this study we propose the Resistant MAPE or R-MAPE index based on the use of Huber's M-estimator as an appropriate alternative as opposed to the arithmetic mean, in order to represent the absolute percentage error distribution in forecasting time series.
This method is suitable for fitting the measured distance, which includes random error distribution, because a simple linear regression analysis can be used to fit a predictive model to an observed data set of y and x values.
Given the unpredictable nature of random measurement error, 1 have plotted three different theoretical measurement error distributions for Hgb concentrations of 10 g/dL, 10.
Total weight, HW, ID, GW and GSI were modeled with an error distribution of normal with identity link, lognormal with the identity link, and gamma with inverse link.
To understand this approach, consider the extreme case: the error distribution for winter days can be very different from that for summer days.