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check standard 和 bracketing standard 标准检查和括号标准是两个与文本标记和识别相关的概念。
在文档处理和自然语言处理领域，这两个概念经常被用于解析和处理文本数据。
下面将对标准检查和括号标准进行详细探讨。
首先，标准检查是指文本处理过程中对于标记的一致性和合法性进行检查的方法。
标准检查的目的是确保文本数据按照预先定义的标准进行标记，并能够在后续的处理过程中正确地进行识别和解析。
标准检查通常包括以下几个方面：1.语法检查：对于文本数据中的句子结构和词法规则进行检查，确保其符合语言的语法规范。
2.语义检查：对于文本数据中的词汇和句子的语义进行检查，确保其符合语言的语义规范，避免歧义和不准确的表达。
3.标点符号检查：对于文本数据中的标点符号进行检查，确保其使用正确和恰当，避免标点符号的错用或遗漏。
4.标记一致性检查：对于文本数据中的标记进行检查，确保标记的一致性和合法性。
例如，在命名实体识别任务中，需要对人名、地名等进行标记，标准检查可以确保这些标记的一致性，避免错误的标记或遗漏。
标准检查在文本处理和自然语言处理领域具有重要的意义。
它可以提高文本数据的质量和准确性，确保后续的处理和分析过程能够顺利进行。
标准检查也是构建自然语言处理模型的重要步骤，可以为模型提供准确和一致的标记数据。
其次，括号标准是一种用于表示文本数据结构的标准方法。
在许多文本处理任务中，文本数据往往拥有复杂的结构，例如句子的嵌套、分组和层次结构等。
括号标准可以通过使用括号将文本数据进行分组和表示，从而清晰地表达其结构关系和层次关系。
括号标准通常包括以下几个方面：1.括号的使用：括号可以用于表示句子的嵌套结构和层次关系。
例如，对于一个复杂的句子，可以使用括号将其分成若干个子句，并表示它们的嵌套关系。
2.括号的顺序：括号标准通常要求括号的顺序是正确和一致的。
例如，在嵌套的括号结构中，内层括号应该在外层括号之前关闭。
Early Warnings Indicators offinancial crises via Auto Regressive Moving Average modelsDavide Faranda∗Laboratoire des Sciences du Climat et de l’Environnement,CEA Saclay,91191Gif-sur-Yvette,FranceandFlavio Maria Emanuele PonsDepartment of Statistics,University of Bologna,Via delle Belle Arti41,40126Bologna,ItalyandEugenio GiachinoandSandro VaientiAix Marseille Universit´e,CNRS,CPT,UMR7332,13288Marseille,FranceandUniversit´e de Toulon,CNRS,CPT,UMR7332,83957La Garde,France.andB´e reng`e re DubrulleLaboratoire SPHYNX,SPEC,DSM,CEA Saclay,CNRS URA2464,91191Gif-sur-Yvette,FranceJanuary7,2015AbstractWe address the problem of defining early warning indicators offinancial crises.To this purpose,wefit the relevant time series through a class of linear models,knownas Auto-Regressive Moving-Average(ARMA(p,q))models.By running such afiton intervals of the time series that can be considered stationary,wefirst determine ∗The authors gratefully acknowledge F.Daviaud for useful discussions and comments.the typical ARMA(p,q).Such a model exists over windows of about60days and turns out to be an AR(1).Then,we define a distanceΥfrom such typical model in the space of the likelihood functions and compute it on short intervals of stocks indexes.Such a distance is expected to increase when the stock market deviates from its normal state for the modifications of the volatility which happen commonly beforea crisis.We observe thatΥcomputed for the Dow Jones,Standard and Poor’s andEURO STOXX50indexes provides an effective early warning indicator which allows for detection of the crisis events that showed precursors.Keywords:Stochastic modeling,Tipping points detection,Extreme events1IntroductionIn the late’70s,a succession of currency crises generated interest in Early warning indica-tors[22,23].Over the year,the indicators spread more generally tofinancial and economic crisis,generating methodological debates[17,15].Traditional statistical approach to this issue are based on specific properties of ideal statistical systems near critical transition:crit-ical slowing down,modifications of the auto-correlation function or of thefluctuations[13], increase of variance and skewness[18],diverging susceptibility[21,4,20],diverging correla-tion length(see the book[25]for a comprehensive review).However,in many cases,these approaches fail to detect thefinancial crisis.First of all,such methods are attractor based, i.e.they assume that the system can be well described by relating the observation at the time t with the observation at the time t+τby an empirical deterministic law describing a stationary state of the system(the so called attractor).This approach fails in describing financial data because,such processes involve a family of time scales rather than a single scaleτ,[9,3].A second origin for the failure of traditional early warning indicators is due to the presence of human feed-backs on the system i.e.the constant attempt to keep economy in a statefit to make profits.Such feed-backs create some delays between the first early warning signals and the time at which the crisis is observed.In addition,tradi-tional early warning indicators may be inapplicable in datasets containing a small number of observations(see e.g.[14]),which is usually the case infinancial time series.This sug-gests that indicators based on single statistical properties are not well suited forfinancial analysis and that crisis detection must involve indicators based on global properties of the whole stochastic process.Here,we build a class of indicators based on the auto-regressive moving-average processes of order p,q ARMA(p,q),widely used to model and forecast the behavior offinancial time series.We remark that the goal of this paper will not be tofind the best model to describe stock indexes and make predictions:this would require at least the estimation of fractionally integrated(ARFIMA(p,d,q))or conditionally heteroskedas-tic(GARCH(p))models,among others.We will rather assess a typical ARMA(p,q)model able to capture the general features of the analyzed stock market and define the early warning indicators as deviations from such a model in a suitable likelihood space.In the first part of the paper,we recall some basics on ARMA(p,q)modeling and define corre-sponding early-warning indicators.We then check that these indicators are able to detect the transition in theoreticalfinancial models.We conclude the paper by presenting and discussing the results of the analysis for some stock indexes.2The methodLet us consider a series X t of an observable with unknown underlying dynamics.Wefurther assume that for a time scaleτof interest,the time series X t1,X t2,...,X tτrepresentsa stationary phenomenon.Since X t is stationary,we may then model it by an ARMA(p,q) process such that for all t:X t=pi=1φi X t−i+εt+qj=1θjεt−j(1)withεt∼W N(0,σ2)-where W N stands for white noise-and the polynomialsφ(z)= 1−φ1z t−1−···−φp z t−p andθ(z)=1−θ1z t−1−···−θq z t−q,with z∈C,have no common factors.Notice that,hereinafter,the noise termεt will be assumed to be a white noise, which is a very general condition[7].For a general stationaryfinancial time series,this model is not unique.However there are several standard procedures for selecting the model whichfits at best the data.The one we exploit in this paper is the Box-Jenkis procedure [5].We choose the lowest p and q such that the residuals of ARMA(p,q)fit are uncorre-lated:to this purpose,we perform a Ljung-Box test for the absence of serial correlation (see,for example,[7]).Thisfixes p and q,and thus our statistical model.There are other model selection procedures based on information criteria(Bayesan or Akaike information criteria).We tested,that they all give clear indications for discriminating the model and that they provide qualitatively the same results of the Box-Jenkis procedure.Intuitively,p and q are related to memory lag of the process,while the coefficientsφi andθi represent the persistence:the higher their sum(in absolute value),the slower the system is forgetting its past history.Our definition of early warning indicators requiresfirst the identification of the basic ARMA(p,q)process(with p and qfixed)which is best suited to describe a Stock index,fora time interval such that it can be considered stationary.This basic process plays the role of an attractor in the sense that it contains the information related to the dynamical prop-erties of the system.With respect to the common attractors used in dynamical systems theory,dynamical indicators as the Lyapunov exponents are replaced by the coefficients φi andθj and the analogous of the attractor dimension is the number of terms p and q to be considered.We will comment on these analogies and on the possibility of choosing a reliable ARMA(p,q)for stock market indexes in the next section.Now we turn to the ARMA based definition of the early warning indicators.ARMA based Early warning indicators We consider a given Stock index,that is assumed to faithfully reflect thefinancial or economic conjuncture.In the absence of crisis, such index can be considered as stationary over a given time intervalτand can befitted by a reference ARMA model.When crisis approach,the volatility of the index increases, and the best ARMA model describing the market will deviate from the basic one.The strongest the crisis,the larger the deviations will be.This suggest to introduce an early warning indicator as a suitable distance in the ARMA space from the reference model.For this,we introduce the Bayesian information criterion(BIC),measuring the relative quality of a statistical model,as:BIC=−2lnˆL(n,ˆσ2,p,q)+k[ln(n)+ln(2π)],(2)whereˆL(n,ˆσ2,p,q)is the likelihood function for the investigated model and in our case k=p+q and n the length of the sample.The varianceˆσ2is computed from the sample and is a series-specific quantity.We can define a normalized distance between the referece ARMA(p,q)and any other ARMA(p,q)model as the normalized difference between the BIC(n,ˆσ2,p+1,q)and the ARMA(p,q)BIC(n,ˆσ2,p,q):Υ=1−exp{|BIC(p+1,q)−BIC(p,q)|}/n.(3)with0≤Υ≤1:it goes to zero if the dataset is well described by an ARMA(p,q)model and tends to one in the opposite case.It has been already observed that such indicators perform well in different physical systems,providing more information than the usual ones,based on the critical slow down due to the increase of correlations in the systems at the transition.These analyses have been reported in[12]where indicators similar toΥhave been used to model different physical systems:Ising and Langevin models,climate and turbulence.3Assessment of the reference modelWe now perform the analysis on different stock indexes:the Dow Jones,the Standard& Poor’s and the EURO STOXX50.We consider databases of such indexes from January 1st1990to June30th2014containing daily observations.The considered EURO STOXX 50series is slightly longer(January1st1986to June30th2014).Wefirst determine the time interval on which the series can be considered stationary. We run the Kwiatkowski,Phillips,Schmidt,and Shin(KPSS)test for a unit root on the time series with increasingτforτ>10days.The test is successful for10<τ<90 days but,as expected,results depend on how close to a crisis the window is taken.In order to preserve enough statistical information we chose a time windowτ=60days.We tested that results presented are in fact stable for40<τ<80days.We then compute for each index the reference ARMA(p,q)model using a statistical strategy:for each window {X t,...X t+τ}for t=1,2,...,T−τ,being T the total length of the series,wefit the best ARMA(p,q)describing the series in this time lag.We then count the frequency of all ARMA(p,q)that have the same total order p+q and compute histograms of p+q.This is shown in Fig.1for each of the three indexes.From thefigure,we see that there is a peak of probability around p+q=1.Since p and q are integers,and since we exclude pure moving average(MA(1))fits,this means that the most probable model has p=1,q=0. So,for each index,we choose the reference model as an AR(1).Once the reference model is established,we can now compute theΥindicator over all the time series,and see how it perform over a database offinancial crises and by comparison with otherfinancial indicators linked to the volatility of the markets.We thus consider the database[1]which reports the crises between1940and1999,and the database[19]for theFigure1:Assessment of the best ARMA(p,q)model.The histograms represent the number of times(normalized to1)that a certain p+q ARMA order appear as bestfit for sub-series of the originals in running windows ofτ=60days.Left panel:analysis for the Dow Jones index.Central panel:analysis for the Standard&Poor’s index.Right panel:analysis for the EURO STOXX50index.most recent ones.Naturally,we do not expect to get early warning indicators for crises connected to exogenous shocks,such as natural disasters(hurricanes,floods,earthquakes) or terrorism(Oklahoma bombs or September11th2001events).The early warning,if any, should appear before the corresponding crisis.4AnalysisFor the daily series of the Dow Jones,the Standard&Poor’s and the EURO STOXX50 indexes,we compute the quantityΥdefined in Eq.(3)using a running time windows of τ=60days.At day t+τwe associate theΥindex computed using the observations {X t,..X t+τ}.We then smoothΥdata by using the moving average method with span of5 days.We consider only values ofΥ>0.3.For the Dow Jones index results are shown in Figure2.The upper panel shows the series of Dow Jones from January1st1990to June30th2014,the central panel the daily changes of the Dow Jones index and the lower panelΥearly warning indicator(blue),thresholded as described before.The red stars indicate crisis of economical origins as derived from the databases[1],whereas yellow stars refer to crisis of non-economical origin.Early warnings provided byΥare empirically associated to the respective crisis by red lines.Questionmarks represent early warnings not associated to effective economical crises.Thefirst thing to remark is that more than one warning is associated to the same crisis and that the interval between the early warning and the crisis is not constant.If we compare these results with the ones arising from physical systems and discussed in[12],the warnings forfinancial crises seem to appear too early with respect to what observed in controllable physical systems.The main difference of markets with respect to natural systems is that the former feel and react to the effect of the crisis by delaying its emergence until the time an official institution points to an economical problem.This usually happens when the crisis itself is unavoidable.For the Dow Jones time series,the matching between crisis and early warnings seem satisfactory although some warnings are of difficult interpretation.A slightly different scenario appears when the Standard&Poor’s stock index analysis is considered,as reported in Fig.3.For this index some crises are well anticipated by an increase onΥ,some others instead are not captured.In order to explain the difference between the Dow Jones and the Standard&Poor’s behavior we have to recall the way they are constructed:The major difference between them is that the Dow Jones includes a price-weighted average of30stocks whereas the Standard&Poor’s is a market value-weighted index of500stocks.We can speculate that for indexes computed on a larger number of companies,crisis early warnings may be averaged out.In fact,if a relevant number of them will not suffer the effects of the crisis,no warning will be provided.Another possibility is that not all these companies have access to latest speculations on the market and therefore most of them cannot react in advance producing no early warnings at all.The latter analysis concerns the EURO STOXX50index,extensevily analysed in[6], and it is reported in Fig.4.As for the Dow Jones,the number of stocks considered is quite limited and crisis are well highlighted.We can remark that the average delay between early warning and crisis is shorter for the EURO STOXX than for the American indexes.This might be due to the fact that the European market usually follows the warnings and the speculations happening on the American side.Figure2:Early detection of crisis based on the Dow Jones stock index analysis.Upper panel:the series of Dow Jones from January1st1990to June30th2014.Central panel: daily changes of the Dow Jones index,red stars indicate crisis of economical origin found in the databases[1,19],yellow stars refer to crisis of non-economical origin.Lower panel:Υearly warning indicator(blue)associated to the respective crisis by red lines.Question marks represent early warnings not associated to effective economical crisis.See text formore descriptions.Figure3:Early detection of crisis based on the Standard&Poor’s stock index analysis. Upper panel:the series of Standard&Poor’s from January1st1990to June30th2014. Central panel:daily changes of the Standard&Poor’s index,red stars indicate crisis of economical origin found in the databases[1,19],yellow stars refer to crisis of non-economical origin.Lower panel:Υearly warning indicator(blue)associated to the respective crisis by red lines.Question marks represent early warnings not associated to effective economicalcrisis.See text for more descriptions.Figure4:Early detection of crisis based on the EURO STOXX50index analysis.Upper panel:the series of EURO STOXX50from January1st1986to June30th2014.Central panel:daily changes of the EURO STOXX50index,red stars indicate crisis of economical origin found in the databases[1,19],yellow stars refer to crisis of non-economical origin. Lower panel:Υearly warning indicator(blue)associated to the respective crisis by red lines.Question marks represent early warnings not associated to effective economical crisis.See text for more descriptions.5DiscussionWe have introduced an early warning indicatorΥforfinancial crises of economical origin based on the ARMA models.The indicator provides,for each day,a[0-1]distance with respect to a reference model ARMA(p,q)which is able tofit the data on a certain window τfar from the crisis events.Thefirst result of the paper is that it is possible to statistically deduct such ARMA(p,q) model which turns out to be the simple AR(1)often emerging in the description of natural phenomena driven by a Langevin equation,such as heavy particles in gas[2],polymers [24]and even turbulence[10].We strongly remark that such model is not the best model to describe all the data of a specific index,but is the best one on a certain windowτchosen as parameter of the method.In some sense,the introduction of this model plays the role of the attractor of a physical system:attractor dimension is replaced by the total terms p and q needed to describe the series and Lyapunov exponents are linked to the magnitude of coefficientsφi andθi.In physical systems crisis happen when the system departs from its attractor and explore new portions of the phase space.In terms of ARMA processes,crises happen when the model departs from the reference one and the series isfitted by other orders than p,q.It has been reported in[12]that this picture is true for physical systems ranging from toy models(Ising dynamics,Langevin problem)up to complex systems(turbulentflows).For stock indexes,the indicator is useful for most of the crises reported in the databases, at least for the years we tested(1990-2014).TheΥindicator has different response functions according to the indexes to which it is applied.The larger the number of stocks used to construct the index,the lower is the early warning power ofΥ.We have conjectured that such phenomenon is due to the fact that in indexes including more companies early effects of the crisis get averaged out either because some companies is not interested by the crisis,either because they do not have at their disposal any instruments tofigure it out.The instruments currently used to keep track of the markets volatility have been in-troduced by the CBOE(Chicago Board Options Exhange).Among such indexes,one of the most used is the VIX[16],which measures the expected volatility at30days for theStandard&Poor’s.This index has been created for the investors to have an information on the pure volatility of the market in the next month.In some sense,the VIX index should feel the modification of the markets and anticipate their change.However,as reported by CBOE[11,8]VIX was not devised to predict stock prices,the direction of the market and highs or lows.Moreover,ourΥindicator is entirely based on the available data,rather on a forecast as the CBOE indexes.For a such a reasonΥwill not be used as a substitute for the VIX index but rather as a completely differentfinancial tool.As explained in[16], the VIX index is used to foresee on a month window the change of volatility in the mar-ket,not systemic changes in the behavior of the market.When a statistical comparison between the two indexes is done,the two indexes appear to be not correlated and they do not have the same statistical behavior:by construction the VIX index possess the same long memory and correlation structures of the S&P data series,whereasΥconsists of a series of peaks whose significance appears only when the original data series deviates from the AR(1)model.Υtherefore does not replace the VIX indexes:it is intended to forecast market shocks on the long term and not to foresee short terms volatilityfluctuations.Acknowledgments.DF acknowledges the support of a CNRS postdoctoral grant.DF and SV were supported by the ANR-Project Perturbations and by the project MOD TER COM of the french Region PACAReferences[1]Crisis events,djia declines and subsequent performance.http://www.xpertadvice.com/Advisors/xpertadvice/market\%20reactions\%20to\%20crisis.pdf.Ac-cessed:2014-09-30.[2]S.Adelman and J.Doll.Generalized langevin equation approach for atom/solid-surface scattering:General formulation for classical scattering offharmonic solids.The Journal of Chemical Physics,64(6):2375–2388,1976.[3]I.Andreadis and A.Serletis.Evidence of a random multifractal turbulent structure inthe dow jones industrial average.Chaos,Solitons&Fractals,13(6):1309–1315,2002.[4]M.Berhanu,B.Gallet,R.Monchaux,M.Bourgoin,P.Odier,J.Pinton,N.Plihon,R.Volk,S.Fauve,N.Mordant,et al.Bistability between a stationary and an oscillatory dynamo in a turbulentflow of liquid sodium.Journal of Fluid Mechanics,641:217, 2009.[5]G.E.Box and G.M.Jenkins.Time Series Analysis:Forecasting and Control.Holden-D.iv,1970.[6]E.C.Brechmann and C.Czado.Risk management with high-dimensional vine copulas:An analysis of the 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threshold detection in the vks experiment.Preprint,2013.[21]R.Monchaux,M.Berhanu,S.Aumaˆıtre,A.Chiffaudel,F.Daviaud,B.Dubrulle,F.Ravelet,S.Fauve,N.Mordant,F.P´e tr´e lis,et al.The von k´a rm´a n sodium experi-ment:turbulent dynamical dynamos.Physics offluids,21:035108,2009.[22]R.H.Pettway and J.F.Sinkey.Establishing on-site bank examination priorities:An early-warning system using accounting and market information.The Journal of Finance,35(1):137–150,1980.[23]S.Sharma and V.Mahajan.Early warning indicators of business failure.The Journalof Marketing,pages80–89,1980.[24]I.Sokolov.Cyclization of a polymer:first-passage problem for a non-markovian pro-cess.Physical review letters,90(8):080601,2003.[25]D.Sornette.Why stock markets crash:critical events in complexfinancial systems.Princeton University Press,2009.。
⾼中英语补差补弱---名词汇编学习-----好资料⾼中英语补差补弱课时⼀名词Part1 单词记忆⽅法：⼀、词根词缀法（词根词缀记忆法总⼝诀：前缀改变词义，后缀改变词性。
）常⽤的前缀主要有：1.bi 表⽰两、重如：bisexual 双性的; ___________/doc/68614fe0b80d4a7302768e9951e79b89680268f4.html （con）表⽰共同如：compose组成，构成; connect 连接；___________3.pro 表⽰向前如：progress 进步；prolong 延长；_____________________4.re 表⽰回、重新如：rebuild 重建；recycle 回收；__________5.dis 表⽰分开或者否定前缀如：distract 分⼼；dismiss 解散；disappointed ; ___________6.im 表⽰否定前缀如：impolite 不礼貌的；impossible 不可能的; _____________7.in 表⽰否定前缀或者向内如：incorrect不正确的; indirect 间接的;into ;____________8.non 表⽰否定，⽆如：none没有；nonsense胡说，废话；_____________9.un 表⽰否定，不、⾮如：uncountable不可数的；uncomfortable不舒服的；____________注意：5-9是表_____________________常⽤的后缀主要有：1.er 名词后缀，表⽰⼈、物如：singer ;leader；________2.or名词后缀，表⽰⼈、物如：actor; doctor;_________3.ist名词后缀，表⽰⼈如: scientist; physicist;__________4.ment名词后缀，表⽰⾏为如：movement; agreement;___________5.tion 名词后缀表⾏为，如：action; communication;_______6.al 形容词后缀，表⽰⼈、物的如：personal个⼈的；cultural ⽂化的；natural ⾃然的;___________5.able形容词后缀，表⽰可能的如：probable; capable;___________6.ful形容词后缀，表⽰充满.. 如：thankful; wonderful;____________8.ing形容词后缀，表⽰令⼈.. 如：disappointing; embarrassing;____________9.ed形容词后缀，表⽰感到…如：disappointed; delighted;___________10.less 否定后缀，表⽰没有的如：endless; hopeless;_______11.ly 副词后缀如：honestly; happily;___________其他anti 反，相反的，反对的anti-American 反美的antifreeze 防冻剂ex 在什么以前ex-president 前总统ex-girl friend 前⼥友tele 遥远telephone 电话telescope 望远镜telegraph 电报词尾icide 杀害suicide ⾃杀pesticide 杀⾍剂ism 表⽰什么主义socialism 社会主义optimism 乐观主义pessimism 悲观主义cs 表⽰学科physics 物理学politics 政治学⼆、拆分法将⽣词拆分成⼩部分并且每部分都有⼀定的意思。
2024年高三英语统计学分析单选题30题1.The average height of a group of people is calculated by adding up all the heights and then dividing by the _____.A.number of peopleB.sum of heightsC.difference in heightsD.product of heights答案：A。
本题考查平均数的计算方法。
平均数是所有数据之和除以数据的个数，这里就是把所有人的身高加起来然后除以人数。
选项B“sum of heights”是身高总和，不是计算平均数的除数。
选项C“difference in heights”是身高差，与平均数计算无关。
选项D“product of heights”是身高乘积，也与平均数计算无关。
2.In a statistical survey, the mode is the value that _____.A.appears most frequentlyB.has the highest sumC.is the averageD.is the middle value答案：A。
本题考查众数的概念。
众数是一组数据中出现次数最多的数值。
选项B“has the highest sum”是和最大，与众数无关。
选项C“is the average”是平均数，与众数不同。
选项D“is the middle value”是中位数，不是众数。
3.The median of a set of data is found by arranging the data in orderand then finding the _____.rgest valueB.smallest valueC.middle valueD.average value答案：C。
均数、标准差、标准误（Mean, standard deviation, standard error）Statistical methods for population health studies, including mean, standard deviation, and standard error, secondMean, standard deviation, standard errorI. requirements1., the significance of the concept of mean, standard deviation and standard error is defined.2., learn the basic method of calculating average, standard deviation and standard error.3., the correct use of the average, standard deviation, standard error for statistical analysis.Two, content and steps(1) review, think and understand the following questions correctly and choose the answers.[multiple-choice question]1.X is an index that represents the value of a variable. (1) average level; (2) range of change;(3) frequency distribution; (4) the size of each other.2. serological titer data are most frequently calculated toindicate their average level.(1) arithmetic mean; (2) median;(3) geometric mean; (4) total distance.3. simple method to calculate the mean.(1) equal distance between groups; (2) no group spacing is required;(3) the group spacing is equal and unequal; (4) the values of variables are relatively close.4. use frequency distribution performance and formula M=L+i/fx (n/2- Sigma fL) to calculate median.(1) the group distance is equal; (2) the distance between groups is equal;(3) the data distribution is symmetrical; (4) the data is required to be lognormal distribution.5. the original data is divided by a constant that is neither equal to 0 nor equal to 1.(1) X invariant and M variable; (M is median); (2) X change and M invariant;(3) both X and M remain unchanged; (4) X and m are both changed.6. the raw data is subtracted from the same constant that is not equal to 0.(1) X invariant, s change; (2) X change, s invariant;(3) X and s remain unchanged; (4) X and s are both changed.7. according to the standard deviation of the normal distribution, the 95% constant value range can be estimated.(1) X + 1.96s (2) X + 2.58s;(3) X + t0.05 (n′) (4) X + t0.05 (n′) s;In 8., X and s.(1) X will be negative, s will not; (2) s will be negative; X will not;(3) neither will; (4) both will.9. coefficient of variation CV.(1) must be greater than 1; (2) must be less than 1;(3) may be greater than 1; also may be less than 1; (4) must be smaller than S.10. the 95% confidence limit of the population mean can be expressed.(1) + 1.96 sigma; (2) + + 1.96 Sigma X;(3) X + t0.05 (n′) sX (4) X + 1.96s.11. of the two samples from the same population, the smaller sample estimate is more reliable.(1) sX; (2) CV;(3) s; (4) t0.05 (n′) sX.12. in the same normal population, the sample with N content is randomly extracted. In theory, 99% of the samples are in the range.(1) X + 2.58sX; (2) X + 1.96sX;(3) + 1.96 Sigma X; (4) + + 2.58 Sigma X;13. sigma x indicates.(1) the standard error of population mean; (2) the degree of dispersion of population mean:(3) the reliability of the variable value X; (4) the standard deviation of the sample mean.14., the most feasible way to reduce the sampling error is.(1) increasing the number of observations (2) to control individual variability(3) follow the randomization principle (4), select the observation objects strictly15. as the standard deviation of a set of observations is 0.(1) the sample number was 0 (2), and the sampling error was 0(3) the average number is 0 (4) or more16., a set of data is normally distributed, in which less than X+1.96s variables have values.(1) 5% (2) 95%(3) 97.5% (4) 92.5%[a yes, no problem]The values of the mean and median of the data of the 1. symmetrical distributions are consistent. ()2. standard error is the index of individual difference distribution. ()3., the standard deviation is large, then the sampling error is also inevitable. ()4. in sampling studies, when the sample size tends to infinity,the X tends to be equal to mu, and the sX tends to be equal to sigma X. ()5. calculate the mean by the frequency table, and the spacing of each group must be equal. ()(two) exercises1. data of total cholesterol in serum of 101 healthy men aged 30~49 years in a given area, please calculate their mean, standard deviation and standard error.(mg/dl)184219.7151.7181.4178.8157.5185.0117.5168.9172.6170.0130.0176.0201.0183.1139.4185.1206.2175.7166.3131.2207.8237.0168.8199.9135.2171.6204.8163.8129.3176.7150.9150.0152.5208.0222.6169.0171.1191.7166.9188.022.07104.2177.7137.4243.1184.9188.6155.7122.7184.0160.9252.9177.5172.6163.2201.0197.8241.2225.7199.1245.6225.7183.6157.9140.6166.3278.8200.6205.5157.9196.7188.5199.2177.9230.0167.6181.7214.0197.0173.6129.2226.3214.3174.6168.8211.5199.9237.1125.1117.9159.2251.4181.1164.0153.4246.9196.6155.4175.7One hundred and eighty-nine point two2. after 10 people were inoculated with a vaccine, the titer of antibody was determined as follows:1:2, 1:2, 1:4, 1:4, 1:4, 1:4, 1:8, 1:16, 1:32.Antibody titer of the vaccine was determined3. this 94 electric ophthalmia patients, the disease from contact welding time (hours) the following table, mean and median trial suggests that exposure to welding to the average time of onset. What indicators of civilization do you think is appropriate?Total 0-2-4-6-8-10-12-14-16-18-20-22-24- at onset contact welding hoursThe number of cases was 810211922640, 10012944., a spot check 120 samples of berberine content (mg/100g) average 4.38, standard deviation of 0.18, assuming that the data obey normal distribution:(1) what range is the content of berberine in 95% samples of Coptis?What is the overall average of the content of berberine inCoptis chinensis Franch?There is a sample of Coptis chinensis. The content of berberine is 4.80. How do you evaluate it?In the 120 samples, how many samples are there in the sample between 4 and 4.4?5. the 95% normal range and 95% confidence interval of serum total cholesterol in 101 healthy men were calculated by 1.T testI. requirements。
加工等级英语
"加工等级"的英文表达方式有多种，以下是几种常见的表达方式：
1. Processing level：这是一种比较通用的表达方式，用于描述材料、产品或信息在加工过程中的等级或阶段。
2. Manufacturing grade：这种表达方式常用于制造业，特别是指产品在制造过程中的质量等级。
3. Processing grade：与"Processing level"类似，"Processing grade"也可以用来表示加工等级，但它更强调加工的质量或精度。
4. Workmanship level：这个表达方式侧重于手工或手工艺方面的加工等级，适用于描述手工艺品或需要人工操作的加工过程。
5. Processing standard："Processing standard"表示加工的标准或要求，可用于描述不同等级的加工水平。
6. Quality level of processing：这种表达方式强调加工的质量水平，常用于描述产品或材料的加工质量。
7. Gradation of processing："Gradation"表示等级或层次，因此"Gradation of processing"可以用来表示加工等级的划分。
需要根据具体的上下文和行业领域选择合适的表达方式，以确保准确传达加工等级的含义。
这些表达方式可以根据具体情况进行调整和组合，以满足不同的需求。
标准差（Standard Deviation），也称均方差（mean squar e error），是各数据偏离平均数的距离的平均数，它是离均差平方和平均后的方根，用σ表示。
标准差是方差的算术平方根。
标准差能反映一个数据集的离散程度。
平均数相同的，标准差未必相同。
简介标准差也被称为标准偏差，或者实验标准差，公式如图。
简单来说，标准差是一组数据平均值分散程度的一种度量。
一个较大的标准差，代表大部分数值和其平均值之间差异较大；一个较小的标准差，代表这些数值较接近平均值。
例如，两组数的集合 {0, 5, 9, 14} 和 {5, 6, 8, 9} 其平均值都是 7 ，但第二个集合具有较小的标准差。
标准差可以当作不确定性的一种测量。
例如在物理科学中，做重复性测量时，测量数值集合的标准差代表这些测量的精确度。
当要决定测量值是否符合预测值，测量值的标准差占有决定性重要角色：如果测量平均值与预测值相差太远（同时与标准差数值做比较），则认为测量值与预测值互相矛盾。
这很容易理解，因为如果测量值都落在一定数值范围之外，可以合理推论预测值是否正确。
标准差应用于投资上，可作为量度回报稳定性的指标。
标准差数值越大，代表回报远离过去平均数值，回报较不稳定故风险越高。
相反，标准差数值越细，代表回报较为稳定，风险亦较小。
例如，A、B两组各有6位学生参加同一次语文测验，A组的分数为95、85、75、65、55、45，B组的分数为73、72、71、69、68、67。
这两组的平均数都是70，但A组的标准差为17.07分，B组的标准差为2.37分（此数据时在R统计软件中运行获得），说明A组学生之间的差距要比B组学生之间的差距大得多。
如是总体，标准差公式根号内除以n如是样本，标准差公式根号内除以（n-1)因为我们大量接触的是样本，所以普遍使用根号内除以（n-1)公式意义所有数减去其平均值的平方和，所得结果除以该组数之个数（或个数减一)，再把所得值开根号，所得之数就是这组数据的标准差。
Summary:Societe GeneraleRationaleThe ratings on France-based bank SociétéGénérale(SocGen)are supported,in Standard&Poor's Ratings Services' view,by the bank's diversified business profile,strong commercial position in its main businesses,and consistent strategy.Negative rating factors are the bank's sizable balance sheet that entails significant credit risk,complex books of illiquid assets and trading instruments,and large operations in emerging markets,notably Russia. Although expected to improve,SocGen's capitalization is only moderate and not supportive of its current ratings.We believe SocGen has high systemic importance in France.We classify the country as supportive under our ratings methodology.Although SocGen benefits from government support for the French banking system as a whole,the ratings on the bank reflect its stand-alone credit profile and do not currently incorporate any explicit uplift for extraordinary government support.We consider SocGen's strategy as consistent and predictable.We understand its universal banking model will fundamentally hold,although more emphasis will be put on streamlining,exiting areas,or developing partnerships where the group's performance has been disappointing.We take a positive view of this more focused strategy.SocGen's risk-adjusted profitability measures are adequate.Pretax profit(excluding fair value of own debt and related noneconomic items)rebounded sharply to€5.5billion in2010.It continued to increase in the first half of 2011,by4%,representing a still average20.5%of revenues.The second-quarter results declined compared with the 2010equivalent period,due to a€395million impairment on Greek sovereign bonds.Excluding this impact, SocGen results were resilient,with a moderate growth in business revenues,including a4.8%growth in corporate and investment banking(CIB)revenues,and a decline in cost of risk.The management of SocGen's large legacy portfolio of structured assets had a much les negative impact on results in this semester than in the three previous years.SocGen's diversified business profile is a positive rating factor.The bank benefits in our view from a sound domestic footprint and some sizable retail positions abroad,including in nonmature markets.We understand that CIB remains a key pillar of SocGen's strategy;the division's franchise in equity derivatives is particularly strong.SocGen has an average risk profile in our opinion.Its larger areas of risk are credit risk embedded in its large balance sheet(particularly in emerging markets)and structured assets,as well as trading risk in CIB,but we see its asset quality as adequate overall.The bank has limited credit commitments to the European countries most exposed to recession,such as Ireland and Portugal,and its exposure to Greek sovereign debt has reduced through impairment.We consider related risks as manageable in relation to earnings generation.We view SocGen's enterprise risk management as adequate.Standard&Poor's calculates SocGen's risk-adjusted capital(RAC)ratio after diversification adjustments at7.8%at year-end2010,but at only5.8%before diversification adjustments.We therefore consider SocGen's capitalization as only moderate compared with international peers'and not supportive of its current'A+'long-term rating.WeStandard & Poor’s | RatingsDirect on the Global Credit Portal | August 11, 20112Summary: Societe Generaleexpect SocGen to gradually enhance its capital base,including its RAC ratio,notably to comply with Basel III phased implementation starting in2013.The bank's core tier1ratio increased to9.3%at June30,2011,from8.5%in December2010.The progressive amortization of the bank's tax loss carried forward,fully deducted from its adjusted common equity,will also contribute to mechanically improving our RAC ratio in the future.OutlookThe stable outlook reflects our expectation that SocGen will post improving although still subdued core earnings in 2011.We also expect SocGen to contain its risk profile--particularly in more volatile markets--and to continue to become more client-centric in CIB.Strategically,we understand SocGen intends to continue focusing on diversified growth,while maintaining the current balance in its business profile.A negative rating action could result if the bank's quest for growth significantly accelerates,particularly in nonmature markets.Material deterioration in SocGen's earnings--notably in the case of an unexpected market shock or write-offs exceeding our expectations--could also lead to a downgrade.Still,given SocGen's high systemic importance,we would expect government support to be forthcoming in case of need.We consider that this support would act to limit or completely offset any resulting damage to SocGen's creditworthiness.Conversely,we could consider a positive rating action if SocGen's diversified business model and measures to adapt to the economic and financial downturns lead to a substantial recovery in its financial performance.This should also combine with a material improvement in the bank's risk-adjusted capitalization.Related Criteria And ResearchAll articles listed below are available on RatingsDirect on the Global Credit Portal,unless otherwise stated.•French Banks Proved Resilient To Crisis But Will Need To Bulk Up Ahead Of Stricter Regulation,Feb.11,2011•Societe Generale,Feb.10,2011•Bank Capital Methodology And Assumptions,Dec.6,2010•Stand-Alone Credit Profiles:One Component Of A Rating,Oct.1,2010•How Systemic Importance Plays A Significant Role In Bank Ratings,July3,2007•Principles Of Credit Ratings,Feb.16,2011Additional Contact:Financial Institutions Ratings Europe;FIG_Europe@Additional Contact:Financial Institutions Ratings Europe;FIG_Europe@/ratingsdirect3S&P may receive compensation for its ratings and certain credit-related analyses, normally from issuers or underwriters of securities or from obligors. 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