Average Error Rates
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Error Rates For Predicting Binary Variables
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Error Rates Statistics
The Real Cost of Human Data Errors – Part 1 of 2 Matt Harris 19 May 2014 At 2:45 pm on May 6, 2010, Wall Street essentially had a heart attack. In just minutes, the stock market plunged 1000 points, for reasons traders, analysts, and business media could not explain. The “flash crash” wiped out $1.1 Trillion of investor dollars and even though most of that was quickly regained, it left the market badly shaken. What happened? error rates should be measured when the network traffic ________ It appears that a single keystroke error was to blame. The letter “B” was inserted in a sell order instead of the letter “M”. Billion was input where Million should have been and it triggered a ripple effect through the automated financial markets. Costly errors in the events business might not have as many zeros as that epic fail, but when it’s your event or your exhibitor who has to deal with a problem caused by a keystroke mistake, it can seem just as bad. Today a surprising amount of venue managers and event organizers still work with separate CRM, operations, and financial systems that either require them to manually enter data multiple times, or have one-way information flow from system to system that can get out of sync. The result is costly – and often embarrassing – errors that stem from bad or out-of-step event detail data. But how acute is this problem? How exactly does it bleed energy and money from your organization? There are several ways in which poor or manual information flow can hinder your events business. The first issue is the cost of having a mistake creep into your information systems, customer orders, service or operation orders, or billing. You are particularly vulnerable if you have any manual “double-entry” of data from system to system. The (Flawed) Human Element If you take the average
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Average Percent Error
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Open Access Estimation of sequencing error rates in short readsXinVictoria Wang1, 2, NatalieBlades3, JieDing1, 2, RazvanSultana1, 4 and GiovanniParmigiani1, 2Email authorBMC Bioinformatics201213:185DOI: 10.1186/1471-2105-13-185© Wang et al.; licensee BioMed http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-185 Central Ltd.2012Received: 20December2011Accepted: 13July2012Published: 30July2012 Abstract Background Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be http://omega.albany.edu:8008/MaxEnt94/FS affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments. Results We developed a fast, scalable and error rate accurate approach to estimating error rates in short reads, which has the added advantage of not requiring a reference genome. We build on the fundamental observation that there is a linear relationship between the copy number for a given read and the number of erroneous reads that differ from the read of interest by one or two bases. The slope average error rate of this relationship can be transformed to give an estimate of the error rate, both by read and by position. We present simulation studies as well as analyses of real data sets illustrating the precision and accuracy of this method, and we show that it is more accurate than alternatives that count the difference between the sample of interest and a reference genome. We show how this methodology led to the detection of mutations in the genome of the PhiX strain used for calibration of Illumina data. The proposed method is implemented in an R package, which can be downloaded from http://bcb.dfci.harvard.edu/∼vwang/shadowRegression.html. Conclusions The proposed method can be used to monitor the quality of sequencing pipelines at the level of individual experiments without the use of reference genomes. Furthermore, having an estimate of the error rates gives one the opportunity to improve analyses and inferences in many applications of next-generation sequencing data. BackgroundThe rapid development of new DNA sequencing technologies is transforming biology by allowing individual investigators to sequence volumes previously requiring a major genome center. Before the dev
evaluation of digital communication systems, fails under certain, frequently encountered circumstances and in particular for large values of the signal to noise ratio, i.e. when the subsequent moments grow in absolute size. The maximum entropy method, on the other hand, continues to give reliable results for the average error rates as a function of the signal to noise ratio. Furthermore, when only few moments of the error probability distribution function are known the results obtained via the maximum entropy are far superior to the Gauss-Quadrature results. This is especially significant when the moments are obtained experimentally - typically only four moments are measured. As one would expect, two moments of a Gaussian error probability distribution suffice to give an analytically exact result. Finally, in practice one aims to work with high signal to noise ratios and with low error probabilities. Hence the accuracy of the tail probabilities is important. This is the area where the maximum entropy results give the most improvement on the results obtained via the traditional Gauss-Quadrature method. F. Solms, Dept Applied Mathematics, Rand Afrikaans University PO Box 524, Auckland Park,2092, South Africa Tel: +27 11 489 3145, E-Mail: FS@rau3.rau.ac.za J.S. Kunicki, Cybernetics Laboratory, Rand Afrikaans University PO Box 524, Auckland Park, 2092, South Africa P.G.W. van Rooyen: Alcatel/Altec/Telkom, PO Box 286, Boksburg, 1460, South Africa