Error Expressed
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1 ( x ) = 1 + x {\displaystyle P_{1}(x)=1+x} (red) at a = 0. The approximation error is the gap between the curves, and it increases for x values further from 0. The approximation error in some data standard error formula is the discrepancy between an exact value and some approximation to it. An approximation error can standard error calculator occur because the measurement of the data is not precise due to the instruments. (e.g., the accurate reading of a piece of paper standard error of the mean is 4.5cm but since the ruler does not use decimals, you round it to 5cm.) or approximations are used instead of the real data (e.g., 3.14 instead of π). In the mathematical field of numerical analysis, the numerical
Margin Of Error Formula
stability of an algorithm in numerical analysis indicates how the error is propagated by the algorithm. Contents 1 Formal Definition 1.1 Generalizations 2 Examples 3 Uses of relative error 4 Instruments 5 See also 6 References 7 External links Formal Definition[edit] One commonly distinguishes between the relative error and the absolute error. Given some value v and its approximation vapprox, the absolute error is ϵ = | v − v approx | , {\displaystyle \epsilon relative error formula =|v-v_{\text{approx}}|\ ,} where the vertical bars denote the absolute value. If v ≠ 0 , {\displaystyle v\neq 0,} the relative error is η = ϵ | v | = | v − v approx v | = | 1 − v approx v | , {\displaystyle \eta ={\frac {\epsilon }{|v|}}=\left|{\frac {v-v_{\text{approx}}}{v}}\right|=\left|1-{\frac {v_{\text{approx}}}{v}}\right|,} and the percent error is δ = 100 % × η = 100 % × ϵ | v | = 100 % × | v − v approx v | . {\displaystyle \delta =100\%\times \eta =100\%\times {\frac {\epsilon }{|v|}}=100\%\times \left|{\frac {v-v_{\text{approx}}}{v}}\right|.} In words, the absolute error is the magnitude of the difference between the exact value and the approximation. The relative error is the absolute error divided by the magnitude of the exact value. The percent error is the relative error expressed in terms of per 100. Generalizations[edit] These definitions can be extended to the case when v {\displaystyle v} and v approx {\displaystyle v_{\text{approx}}} are n-dimensional vectors, by replacing the absolute value with an n-norm.[1] Examples[edit] As an example, if the exact value is 50 and the approximation is 49.9, then the absolute error is 0.1 and the relative error is 0.1/50 = 0.002 = 0.2%. Another example would be if you measured a beaker and read 5mL. The correct reading would have been 6mL. This means that your percent error would be about
engineering, see Tolerance (engineering). For the eponymous movie, see Margin for error (film). The top portion charts probability density against actual percentage, showing the relative probability that the actual percentage is realised, based on the absolute error sampled percentage. In the bottom portion, each line segment shows the 95% confidence interval
Sampling Error
of a sampling (with the margin of error on the left, and unbiased samples on the right). Note the greater the
95 Confidence Interval
unbiased samples, the smaller the margin of error. The margin of error is a statistic expressing the amount of random sampling error in a survey's results. It asserts a likelihood (not a certainty) that the https://en.wikipedia.org/wiki/Approximation_error result from a sample is close to the number one would get if the whole population had been queried. The likelihood of a result being "within the margin of error" is itself a probability, commonly 95%, though other values are sometimes used. The larger the margin of error, the less confidence one should have that the poll's reported results are close to the true figures; that is, the figures for https://en.wikipedia.org/wiki/Margin_of_error the whole population. Margin of error applies whenever a population is incompletely sampled. Margin of error is often used in non-survey contexts to indicate observational error in reporting measured quantities. In astronomy, for example, the convention is to report the margin of error as, for example, 4.2421(16) light-years (the distance to Proxima Centauri), with the number in parentheses indicating the expected range of values in the matching digits preceding; in this case, 4.2421(16) is equivalent to 4.2421 ± 0.0016.[1] The latter notation, with the "±", is more commonly seen in most other science and engineering fields. Contents 1 Explanation 2 Concept 2.1 Basic concept 2.2 Calculations assuming random sampling 2.3 Definition 2.4 Different confidence levels 2.5 Maximum and specific margins of error 2.6 Effect of population size 2.7 Other statistics 3 Comparing percentages 4 See also 5 Notes 6 References 7 External links Explanation[edit] The margin of error is usually defined as the "radius" (or half the width) of a confidence interval for a particular statistic from a survey. One example is the percent of people who prefer product A versus product B. When a single, global margin of error is reported for a survey, it refers to the maximum margin of error for all reported percentages
Open Access A power law global error model for the identification of differentially expressed genes in microarray dataNormanPavelka†1, MattiaPelizzola†1, CaterinaVizzardelli†1, MonicaCapozzoli1, AndreaSplendiani1, FrancescaGranucci1 and PaolaRicciardi-Castagnoli1Email https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-5-203 author†Contributed equallyBMC Bioinformatics20045:203DOI: 10.1186/1471-2105-5-203© Pavelka et al; licensee BioMed Central Ltd.2004Received: 16July2004Accepted: 17December2004Published: 17December2004 Abstract Background High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper standard error knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence standard error formula in microarray data to improve the performance of existing methods for identifying DEG. Results In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. Conclusions The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples. BackgroundDNA microarrays have become common tools for monitoring genome-wide expression in biological samples harvested under different physiological, pathological or pharmacological conditions. One of the most challenging problems in microarray data analysis is probably the identification of differentially express