翻訳と辞書 Words near each other ・ Norman Bedard ・ Norman Bekkedahl ・ Norman Bel Geddes ・ Norman Belcher ・ Norman Bell ・ Norman Bellingham ・ Norman Bennett ・ Norman Bentwich ・ Norman Bergamelli ・ Normality test ・ Normalization ・ Normalization (Czechoslovakia) ・ Normalization (image processing) ・ Normalization (people with disabilities) ・ Normalization (sociology) ・ Normalization (statistics) ・ Normalization model ・ Normalization process model ・ Normalization process theory ・ Normalization property (abstract rewriting) ・ Normalized chromosome value ・ Normalized compression distance ・ Normalized Difference Vegetation Index ・ Normalized frequency ・ Normalized frequency (fiber optics) ・ Normalized frequency (unit) ・ Normalized Google distance ・ Normalized loop ・ Normalized number ・ Normalized Systems Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Normalization (statistics) ： ウィキペディア英語版 Normalization (statistics) In statistics and applications of statistics, normalization can have a range of meanings.〔 In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment. In the case of normalization of scores in educational assessment, there may be an intention to align distributions to a normal distribution. A different approach to normalization of probability distributions is quantile normalization, where the quantiles of the different measures are brought into alignment. In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some types of normalization involve only a rescaling, to arrive at values relative to some size variable. In terms of levels of measurement, such ratios only make sense for ''ratio'' measurements (where ratios of measurements are meaningful), not ''interval'' measurements (where only distances are meaningful, but not ratios). In theoretical statistics, parametric normalization can often lead to pivotal quantities – functions whose sampling distribution does not depend on the parameters – and to ancillary statistics – pivotal quantities that can be computed from observations, without knowing parameters. == Examples == There are various normalizations in statistics – nondimensional ratios of errors, residuals, means and standard deviations, which are hence scale invariant – some of which may be summarized as follows. Note that in terms of levels of measurement, these ratios only make sense for ''ratio'' measurements (where ratios of measurements are meaningful), not ''interval'' measurements (where only distances are meaningful, but not ratios). See also ... || Normalizing errors when population parameters are known. Works well for populations that are normally distributed |- | Student's t-statistic || $\backslash frac$ || Normalizing residuals when population parameters are unknown (estimated). |- | Studentized residual || $\backslash frac\; =\; \backslash frac$ || Normalizing residuals when parameters are estimated, particularly across different data points in regression analysis. |- | Standardized moment || $\backslash frac$ || Normalizing moments, using the standard deviation $\backslash sigma$ as a measure of scale. |- | Coefficient of variation || $\backslash frac$ || Normalizing dispersion, using the mean $\backslash mu$ as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution. |- | Feature scaling || $X\text{'}\; =\; \backslash frac-X\_\}$ || Feature scaling used to bring all values into the range (). This is also called unity-based normalization. This can be generalized to restrict the range of values in the dataset between any arbitrary points $a$ and $b$ using $X\text{'}\; =\; a\; +\; \backslash frac\}$. |- |} Note that some other ratios, such as the variance-to-mean ratio $\backslash left(\backslash frac\backslash right)$, are also done for normalization, but are not nondimensional: the units do not cancel, and thus the ratio has units, and are not scale invariant. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Normalization (statistics)」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.