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2 The components of the position error are unbiased The validity of the assumptions must be considered in the use of the subsequent results The Normal (Gaussian) assumption is motivated by the Central Limit Theorem Although the assumption may not be absolutely valid, the large number of small, independent error sources in a navigation system results in error distributions which can be accurately approximated as Gaussian This assumption is not typically a major source of di culty The unbiased assumption must be considered more carefully The unbiased assumption can be considered as a distinction between precision and accuracy Ideally, an analyst would calculate errors as x = x x where
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Many traders like Bollinger Bands because they are flexible enough to be used on many types of securities, not just stocks When you look at Bollinger Bands on a stock chart, you ll see an upper and lower band, most likely in blue or black There is also a dotted line in the middle between the bands This is actually the default 20-day simple moving average In addition, and this is important, you ll see the market price bouncing along between the upper and lower bands When the market price tags or pierces the upper band, it means that the market is overbought (ie, there has been too much buying) What many people don t realize is that the market can remain overbought for some time before changing direction Just because it touches the upper band doesn t mean it will suddenly reverse Put another way, the market can remain overbought longer than you can remain solvent, to paraphrase economist John Maynard Keynes
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x denotes the true value of the variable When x is unknown, the analyst x might instead calculate the error as xc = x where x denotes the average of the x values1 If there is a bias between x and x, then the second order statistics of xc (standard deviation or variance) will indicate the precision of x without indicating its accuracy 4911 Analysis in Three Dimensions
Let w = [n, e, d] denote the position error variables Denote the covariance matrix for w by the symmetric positive de nite matrix P = E ww Given the above assumptions that w N (0, P), the probability density is pw (n, e, d) = 1 (2 )
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