Cross-over in scaling laws A simple example from micromagnetics
ResearchObjectives

Research ObjectivesThe MILC Collaboration is engaged in a broad research program in Quantum Chromodynamics (QCD).This research addresses fundamental questions in high energy and nuclear physics,and is directly related to major experimental programs in thesefields.It includes studies of the mass spectrum of strongly interacting particles,the weak interactions of these particles,and the behavior of strongly interacting matter under extreme conditions.The Standard Model of High Energy Physics encompasses our current knowledge of the funda-mental interactions of subatomic physics.It consists of two quantumfield theories:the Weinberg-Salaam theory of electromagnetic and weak interactions,and QCD,the theory of the strong interac-tions.The Standard Model has been enormously successful in explaining a wealth of data produced in accelerator and cosmic ray experiments over the past thirty years;however,our knowledge of it is incomplete because it has been difficult to extract many of the most interesting predictions of QCD,those that depend on the strong coupling regime of the theory,and therefore require non-perturbative calculations.At present,the only means of carrying out non-perturbative QCD calculations fromfirst principles and with controlled errors,is through large scale numerical sim-ulations within the framework of lattice gauge theory.These simulations are needed to obtain a quantitative understanding of the physical phenomena controlled by the strong interactions,to de-termine a number of the fundamental parameters of the Standard Model,and to make precise tests of the Standard Model’s range of validity.Despite the many successes of the Standard Model,it is believed by high energy physicists that to understand physics at the shortest distances,a more general theory,which unifies all four of the fundamental forces of nature,will be required.The Standard Model is expected to be a limiting case of this more general theory,just as classical mechanics is a limiting case of the more general quantum mechanics.A central objective of the experimental program in high energy physics,and of lattice QCD simulations,is to determine the range of validity of the Standard Model,and to search for new physics beyond it.Thus,QCD simulations play an important role in efforts to obtain a deeper understanding of the fundamental laws of physics.QCD is formulated in the four-dimensional space-time continuum;however,in order to carry out numerical calculations one must reformulate it on a lattice or grid.It should be emphasized that the lattice formulation of QCD is not merely a numerical approximation to the continuum formu-lation.The lattice regularization of QCD is every bit as valid as continuum regularizations.The lattice spacing a establishes a momentum cutoffπ/a that removes ultraviolet divergences.Stan-dard renormalization methods apply,and in the perturbative regime they allow a straightforward conversion of lattice results to any of the standard continuum regularization schemes.Lattice QCD calculations proceed in two steps.In thefirst,one uses importance sampling tech-niques to generate gauge configurations,which are representative samples from the Feynman path integrals that define QCD.These configurations are saved,and in the second step they are used to calculate a wide variety of physical quantities.It is necessary to generate configurations with a range of lattice spacings,and then perform extrapolations to the zero lattice spacing limit.Fur-thermore,the computational cost of calculations rises as the masses of the quarks,the fundamental constituents of strongly interacting matter,decrease.Until recently,it has been too expensive to carry out calculations with the masses of the two lightest quarks,the up and the down,set to their physical values.Instead,one has performed calculations for a range of up and down quark masses, and extrapolated to their physical values guided by chiral perturbation theory,an effectivefield theory that determines how physical quantities depend on the masses of the lightest quarks.The extrapolations in lattice spacing(continuum extrapolation)and quark mass(chiral extrapolation) are the major sources of systematic errors in QCD calculations,and both must be under control in order to obtain trustworthy results.In our current simulations,we are,for thefirst time,working at or near the physical masses of the up and down quarks.The gauge configurations produced in these simulations greatly reduce,and will eventually eliminate,the systematic errors associatedwith the chiral extrapolation.A number of different formulations of QCD on the lattice are currently in use by lattice gauge theorists,all of which are expected to give the same results in the continuum limit.In recent years, major progress has been made in thefield through the development of improved formulations(im-proved actions)which reducefinite lattice spacing artifacts.Approximately twelve years ago,we developed one such improved action called asqtad[1],which significantly increased the accuracy of our simulations for a given amount of computing resources.We have used the asqtad action to generate an extensive library of gauge configurations with small enough lattice spacings and light enough quark masses to perform controlled calculations of a number of physical quantities. Computational resources provided by the DOE and NSF have enabled us to complete our program of generating asqtad gauge configurations.These configurations are publicly available,and have been used by us and by other groups to study a wide range of physical phenomena of importance in high energy and nuclear physics.Ours was thefirst set of full QCD ensembles that enabled control over both the continuum and chiral extrapolations.We have published a review paper describing the asqtad ensembles and the many calculations that were performed with them up to2009[2]. Over the last decade,a major component of our work has been to use our asqtad gauge config-urations to calculate quantities of importance to experimental programs in high energy physics. Particular emphasis was placed on the study of the weak decays and mixings of strongly interact-ing particles in order to determine some of the least well known parameters of the standard model and to provide precise tests of the standard model.The asqtad ensembles have enabled the calcu-lation of a number of physical quantities to a precision of1%–5%,and will enable many more quantities to be determined to this precision in the coming years.These results are already having an impact on experiments in high energy physics;however,in some important calculations,partic-ularly those related to tests of the standard model,higher precision is needed than can be provided by the existing asqtad ensembles.In order to obtain the required precision,we are now working with the Highly Improved Staggered Quark(HISQ)action developed by the HPQCD Collabora-tion[3].We have performed tests of scaling in the lattice spacing using HISQ valence quarks with gauge configurations generated with HISQ sea quarks[4].We found that lattice artifacts for the HISQ action are reduced by approximately a factor of2.5from those of the asqtad action for the same lattice spacing,and taste splittings in the pion masses are reduced by approximately a factor of three,which is sufficient to enable us to undertake simulations with the mass of the Goldstone pion at or near the physical pion mass.(“Taste”refers to the different ways one can construct the same physical particle in the staggered quark formalism.Although particles with different tastes become identical in the continuum limit,their masses can differ atfinite lattice spacing).More-over,the improvement in the quark dispersion relation enables us to include charm sea quarks in the simulations.The properties of the HISQ ensembles are described in detail in Ref.[5],and the first physics calculations using the physical quark mass ensembles in Refs.[6,7,8].The current status of the HISQ ensemble generation project is described at the link HISQ Lattice Generation and some initial calculations with them at Recent Results.The HISQ action also has major advan-tages for the study of QCD at high temperatures,so we have started to use it in our studies of this subject.Projects using the HISQ action will be a major component of our research for the next several years.Our research is currently focused on three major areas:1)the properties of light pseudoscalar mesons,2)the decays and mixings of heavy-light mesons,3)the properties of strongly interacting matter at high temperatures.We briefly discuss our research in each of these areas at the link Recent Results.References[1]The MILC Collaboration:C.Bernard et al.,Nucl.Phys.(Proc.Suppl.),60A,297(1998);Phys.Rev.D58,014503(1998);G.P.Lepage,Nucl.Phys.(Proc.Suppl.),60A,267(1998);Phys.Rev.D59,074501(1999);Kostas Orginos and Doug Toussaint(MILC),Nucl.Phys.(Proc.Suppl.),73,909(1999);Phys.Rev.D59,014501(1999);Kostas Orginos,Doug Tou-ssaint and R.L.Sugar(MILC),Phys.Rev.D60,054503(1999);The MILC Collaboration:C.Bernard et al.,Phys.Rev.D61,111502(2000).[2]The MILC Collaboration: A.Bazavov et al.,Rev.Mod.Phys.82,1349-1417(2010)[arXiv:0903.3598[hep-lat]].[3]The HPQCD/UKQCD Collaboration: E.Follana et al.,Phys.Rev.D73,054502(2007)[arXiv:hep-lat/0610092].[4]The MILC Collaboration: A.Bazavov al.,Phys.Rev.D82,074501(2010)[arXiv:1004.0342].[5]The MILC Collaboration: A.Bazavov al.,Phys.Rev.D87,054505(2013)[arXiv:1212.4768].[6]The MILC Collaboration: A.Bazavov et al.,Phys.Rev.Lett.110,172003(2013)[arXiv:1301.5855].[7]The Fermilab Lattice and MILC Collaborations:A.Bazavov,et al.,Phys.Rev.Lett.112,112001(2014)[arXiv:1312.1228].[8]The MILC Collaboration:A.Bazavov et al.,Proceedings of Science(Lattice2013)405(2013)[arXiv:1312.0149].。
计量经济学中英文词汇对照

cross-loading Cross-over design Cross-section analysis Cross-section survey
Cross-sectional
Crosstabs Cross-tabulation table Cube root Cumulative distribution function Cumulative probability Curvature Curvature Curve fit Curve fitting Curvilinear regression Curvilinear relation Cut-and-try method Cycle
Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covariance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Cyclist DDD D test Data acquisition Data bank Data capacity Data deficiencies Data handling Data manipulation Data processing Data reduction Data set Data sources Data transformation Data validity Data-in Data-out Dead time Degree of freedom Degree of precision Degree of reliability Degression Density function Density of data points Dependent variable Dependent variable Depth Derivative matrix Derivative-free methods Design Determinacy Determinant Determinant Deviation Deviation from average Diagnostic plot Dichotomous variable Differential equation Direct standardization Discrete variable DISCRIMINANT Discriminant analysis Discriminant coefficient
统计学专业英语词汇完整版(可编辑修改word版)

统计学专业英语词汇AAbsolute deviation,绝对离差Absolute number,绝对数Absolute residuals,绝对残差Acceleration array,加速度立体阵Acceleration in an arbitrary direction,任意方向上的加速度Acceleration normal,法向加速度Acceleration space dimension,加速度空间的维数Acceleration tangential,切向加速度Acceleration vector,加速度向量Acceptable hypothesis,可接受假设Accumulation,累积Accuracy,准确度Actual frequency,实际频数Adaptive estimator,自适应估计量Addition,相加Addition theorem,加法定理Additivity,可加性Adjusted rate,调整率Adjusted value,校正值Admissible error,容许误差Aggregation,聚集性Alternative hypothesis,备择假设Among groups,组间Amounts,总量Analysis of correlation,相关分析Analysis of covariance,协方差分析Analysis of regression,回归分析Analysis of time series,时间序列分析Analysis of variance,方差分析Angular transformation,角转换ANOVA(analysis of variance),方差分析ANOVA Models,方差分析模型Arcing,弧/弧旋Arcsine transformation,反正弦变换Area under the curve,曲线面积AREG,评估从一个时间点到下一个时间点回归相关时的误差ARIMA,季节和非季节性单变量模型的极大似然估计Arithmetic grid paper,算术格纸Arithmetic mean,算术平均数Arrhenius relation,艾恩尼斯关系Assessing fit,拟合的评估Associative laws,结合律Asymmetric distribution,非对称分布Asymptotic bias,渐近偏倚Asymptotic efficiency,渐近效率Asymptotic variance,渐近方差Attributable risk,归因危险度Attribute data,属性资料Attribution,属性Autocorrelation,自相关Autocorrelation of residuals,残差的自相关Average,平均数Average confidence interval length,平均置信区间长度Average growth rate,平均增长率BBar chart,条形图Bar graph,条形图Base period,基期Bayes theorem, 贝叶斯定理Bell-shaped curve,钟形曲线Bernoullidistribution,伯努力分布Best-trim estimator,最好切尾估计量Bias,偏性Binary logistic regression,二元逻辑斯蒂回归Binomial distribution,二项分布Bisquare,双平方Bivariate Correlate,二变量相关Bivariate normal distribution,双变量正态分布Bivariate normal population,双变量正态总体Biweight interval,双权区间Biweight M-estimator,双权M 估计量Block,区组/配伍组BMDP(Biomedical computer programs),BMDP 统计软件包Box plots,箱线图/箱尾图Break down bound,崩溃界/崩溃点CCanonical correlation,典型相关Caption,纵标目Case-control study,病例对照研究Categorical variable,分类变量Catenary,悬链线Cauchy distribution,柯西分布Cause-and-effect relationship,因果关系Cell,单元Censoring,终检Center of symmetry,对称中心Centering and scaling,中心化和定标Central tendency,集中趋势Central value,中心值CHAID-χ2AutomaticInteractionDetector,卡方自动交互检测Chance,机遇Chance error,随机误差Chance variable,随机变量Characteristic equation,特征方程Characteristic root,特征根Characteristic vector,特征向量Chebshev criterion of fit,拟合的切比雪夫准则Chernoff faces,切尔诺夫脸谱图Chi-square test,卡方检验/χ2 检验Choleskey decomposition,乔洛斯基分解Circle chart,圆图Class interval,组距Class mid-value,组中值Class upper limit,组上限Classified variable,分类变量Cluster analysis,聚类分析Cluster sampling,整群抽样Code,代码Coded data,编码数据Coding,编码Coefficient of contingency,列联系数Coefficientof determination,决定系数Coefficient ofmultiple correlation,多重相关系数Coefficient ofpartial correlation,偏相关系数Coefficient of production-moment correlation,积差相关系数Coefficient of rank correlation,等级相关系数Coefficient of regression,回归系数Coefficient of skewness,偏度系数Coefficient of variation,变异系数Cohort study,队列研究Column,列Column effect,列效应Column factor,列因素Combination pool,合并Combinative table,组合表Common factor,共性因子Common regression coefficient,公共回归系数Common value,共同值Common variance,公共方差Common variation,公共变异Communality variance,共性方差Comparability,可比性Comparison of bathes,批比较Comparison value,比较值Compartment model,分部模型Compassion,伸缩Complement of an event,补事件Complete association,完全正相关Complete dissociation,完全不相关Complete statistics,完备统计量Completely randomized design,完全随机化设计Composite event,联合事件/复合事件Concavity,凹性Conditional expectation,条件期望Conditional likelihood,条件似然Conditional probability,条件概率Conditionally linear,依条件线性Confidence interval,置信区间Confidence limit,置信限Confidence lower limit,置信下限Confidence upper limit,置信上限Confirmatory Factor Analysis,验证性因子分析Confirmatory research,证实性实验研究Confounding factor,混杂因素Conjoint,联合分析Consistency,相合性Consistency check,一致性检验Consistent asymptotically normal estimate,相合渐近正态估计Consistent estimate,相合估计Constrained nonlinear regression,受约束非线性回归Constraint,约束Contaminated distribution,污染分布Contaminated Gausssian,污染高斯分布Contaminated normal distribution,污染正态分布Contamination,污染Contamination model,污染模型Contingency table,列联表Contour,边界线Contribution rate,贡献率Control,对照Controlled experiments,对照实验Conventional depth,常规深度Convolution,卷积Corrected factor,校正因子Corrected mean,校正均值Correction coefficient,校正系数Correctness,正确性Correlation coefficient,相关系数Correlation index,相关指数Correspondence, 对应Counting,计数Counts,计数/频数Covariance,协方差Covariant,共变Cox Regression, Cox 回归Criteria for fitting,拟合准则Criteria of least squares,最小二乘准则Critical ratio,临界比Critical region,拒绝域Critical value,临界值Cross-over design,交叉设计Cross-section analysis,横断面分析Cross-section survey,横断面调查Cross tabs,交叉表Cross-tabulation table,复合表Cube root,立方根Cumulative distribution function,累计分布函数Cumulative probability,累计概率Curvature,曲率/弯曲Curve fit,曲线拟和Curve fitting,曲线拟合Curvilinear regression,曲线回归Curvilinear relation,曲线关系Cut-and-try method,尝试法Cycle,周期Cyclist,周期性DD test, D 检验Data acquisition,资料收集Databank,数据库Data capacity,数据容量Data deficiencies,数据缺乏Data handling,数据处理Data manipulation,数据处理Data processing,数据处理Data reduction,数据缩减Data set,数据集Data sources,数据来源Data transformation,数据变换Data validity,数据有效性Data-in,数据输入Data-out,数据输出Dead time,停滞期Degree of freedom,自由度Degree of precision,精密度Degree of reliability,可靠性程度Degression,递减Density function,密度函数Density of datapoints,数据点的密度Dependent variable,应变量/依变量/因变量Depth,深度Derivative matrix,导数矩阵Derivative-free methods,无导数方法Design,设计Determinacy,确定性Determinant,行列式Determinant,决定因素Deviation,离差Deviation from average,离均差Diagnostic plot,诊断图Dichotomousvariable,二分变量Differentialequation,微分方程Directstandardization,直接标准化法Discrete variable,离散型变量Discriminant,判断Discriminant analysis,判别分析Discriminant coefficient,判别系数Discriminant function,判别值Dispersion,散布/分散度Disproportional,不成比例的Disproportionate sub-class numbers,不成比例次级组含量Distribution free,分布无关性/免分布Distribution shape,分布形状Distribution-free method,任意分布法Distributive laws,分配律Disturbance,随机扰动项Dose response curve,剂量反应曲线Double blind method,双盲法Doubleblind rial,双盲试验Double exponential distribution,双指数分布Double logarithmic,双对数Downward rank,降秩Dual-space plot,对偶空间图DUD,无导数方法Duncan's new multiple range method,新复极差法/Duncan 新法EEffect, 实验效应Eigen value,特征值Eigen vector,特征向量Ellipse,椭圆Empirical distribution,经验分布Empirical probability,经验概率单位Enumeration data,计数资料Equal sun-class number,相等次级组含量Equally likely,等可能Equal variance,同变性Error,误差/错误Error of estimate,估计误差Error type I,第一类错误Error type II,第二类错误Estimand,被估量Estimated error mean squares,估计误差均方Estimated error sum of squares,估计误差平方和Euclidean distance,欧式距离Event,事件Exceptional data point,异常数据点Expectation plane,期望平面Expectation surface,期望曲面Expected values,期望值Experiment,实验Experimental sampling,试验抽样Experimental unit,试验单位Explanatory variable,说明变量/解释变量Exploratory data analysis,探索性数据分析Explore Summarize,探索-摘要Exponential curve,指数曲线Exponential growth,指数式增长Exsooth,指数平滑方法Extended fit,扩充拟合Extra parameter,附加参数Extra polation,外推法Extreme observation,末端观测值Extremes,极端值/极值FF distribution, F 分布F test, F 检验Factor,因素/因子Factor analysis,因子分析Factor score,因子得分Factorial,阶乘Factorial design,析因试验设计False negative,假阴性False negative error,假阴性错误Family of distributions,分布族Family of estimators,估计量族Fanning,扇面Fatality rate,病死率Field investigation,现场调查Field survey,现场调查Finitepopulation,有限总体Finite-sample, 有限样本Firstderivative,一阶导数First principal component,第一主成分First quartile,第一四分位数Fisher information,费雪信息量Fitted value,拟合值Fitting a curve,曲线拟合Fixed base,定基Fluctuation,随机起伏Forecast,预测Four fold table,四格表Fourth, 四分点Fraction blow,左侧比率Fractional error,相对误差Frequency,频率Frequency polygon,频数多边图Frontier point,界限点Function relationship,泛函关系GGamma distribution,伽玛分布Gauss increment,高斯增量Gaussian distribution,高斯分布/正态分布Gauss-Newton increment,高斯-牛顿增量General census,全面普查GENLOG(Generalized liner models),广义线性模型Geometric mean,几何平均数Gini's mean difference,基尼均差GLM(General liner models),通用线性模型Goodness of fit,拟和优度/配合度Gradientof determinant,行列式的梯度Graeco-Latin square,希腊拉丁方Grand mean,总均值Gross errors,重大错误Gross-error sensitivity,大错敏感度Group averages,分组平均Grouped data,分组资料Guessed mean,假定平均数HHalf-life,半衰期Hampel M-estimators,汉佩尔M 估计量Happenstance,偶然事件Harmonic mean,调和均数Hazard function,风险均数Hazard rate,风险率Heading,标目Heavy-tailed distribution,重尾分布Hessian array,海森立体阵Heterogeneity,不同质Heterogeneity of variance,方差不齐Hierarchical classification,组内分组Hierarchical clustering method,系统聚类法High-leverage point,高杠杆率点HILOGLINEAR,多维列联表的层次对数线性模型Hinge,折叶点Histogram,直方图Historical cohort study,历史性队列研究Holes,空洞HOMALS,多重响应分析Homogeneity of variance,方差齐性Homogeneity test,齐性检验Huber M-estimators,休伯M 估计量Hyperbola,双曲线Hypothesis testing,假设检验Hypothetical universe,假设总体IImpossible event,不可能事件Independence,独立性Independent variable,自变量Index,指标/指数Indirect standardization,间接标准化法Individual,个体Inference band, 推断带Infinite population,无限总体Infinitely great, 无穷大Infinitely small,无穷小Influence curve,影响曲线Information capacity,信息容量Initial condition,初始条件Initial estimate,初始估计值Initial level,最初水平Interaction,交互作用Interaction terms,交互作用项Intercept,截距Interpolation,内插法Inter quartile range,四分位距Interval estimation,区间估计Intervals of equal probability,等概率区间Intrinsic curvature,固有曲率Invariance, 不变性Inverse matrix,逆矩阵Inverse probability,逆概率Inverse sine transformation,反正弦变换Iteration,迭代JJacobian determinant,雅可比行列式Joint distribution function,联合分布函数Joint probability,联合概率Joint probability distribution,联合概率分布KK means method,逐步聚类法Kaplan-Meier,评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier 图Kendall's rank correlation, Kendall 等级相关Kinetic,动力学Kolmogorov-Smirnove test,柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal 及Wallis 检验/多样本的秩和检验/H 检验Kurtosis,峰度LLack of fit,失拟Ladder of powers,幂阶梯Lag,滞后Large sample,大样本Large sample test,大样本检验Latin square,拉丁方Latin square design,拉丁方设计Leakage,泄漏Least favorable configuration,最不利构形Least favorable distribution,最不利分布Least significant difference,最小显著差法Least square method,最小二乘法Least-absolute-residuals estimates,最小绝对残差估计Least-absolute-residuals fit,最小绝对残差拟合Least-absolute-residuals line,最小绝对残差线Legend,图例L-estimator,L 估计量L-estimator of location,位置L 估计量L-estimator of scale,尺度L 估计量Level,水平Life expectance,预期期望寿命Life table,寿命表Life table method,生命表法Light-taile distribution,轻尾分布Likelihood function,似然函数Likelihood ratio,似然比Line graph,线图Linear correlation,直线相关Linear equation,线性方程Linear programming,线性规划Linear regression,直线回归/线性回归Linear trend,线性趋势Loading,载荷Location and scale equi variance,位置尺度同变性Location equi variance,位置同变性Location invariance,位置不变性Location scale family,位置尺度族Log rank test,时序检验Logarithmic curve,对数曲线Logarithmic normal distribution,对数正态分布Logarithmic scale,对数尺度Logarithmic transformation,对数变换Logic check,逻辑检查Logistic distribution,逻辑斯蒂分布Logit transformation, Logit 转换LOGLINEAR,多维列联表通用模型Lognormal distribution,对数正态分布Lost function,损失函数Low correlation,低度相关Lower limit,下限Lowest-attained variance,最小可达方差LSD,最小显著差法的简称Lurking variable,潜在变量MMain effect,主效应Major heading,主辞标目Marginal density function,边缘密度函数Marginal probability,边缘概率Marginal probability distribution,边缘概率分布Matched data,配对资料Matched distribution,匹配过分布Matching of distribution,分布的匹配Matching of transformation,变换的匹配Mathematical expectation,数学期望Mathematical model,数学模型MaximumL-estimator,极大L 估计量Maximumlikelihood method,最大似然法Mean,均数Mean squares between groups,组间均方Mean squares within group,组内均方Means (Compare means),均值-均值比较Median,中位数Median effective dose,半数效量Median lethal dose,半数致死量Median polish,中位数平滑Median test,中位数检验Minimal sufficient statistic,最小充分统计量Minimum distance estimation,最小距离估计Minimum effective dose,最小有效量Minimum lethal dose,最小致死量Minimum variance estimator,最小方差估计量MINITAB,统计软件包Minor heading,宾词标目Missing data,缺失值Model specification,模型的确定Modeling Statistics ,模型统计Models for outliers,离群值模型Modifying the model,模型的修正Modulus of continuity,连续性模Morbidity,发病率Most favorable configuration,最有利构形Multidimensional Scaling (ASCAL),多维尺度/多维标度Multinomial Logistic Regression ,多项逻辑斯蒂回归Multiple comparison,多重比较Multiple correlation ,复相关Multiple covariance,多元协方差Multiple linear regression,多元线性回归Multiple response ,多重选项Multiple solutions,多解Multiplication theorem,乘法定理Multiresponse,多元响应Multi-stage sampling,多阶段抽样Multivariate T distribution,多元T 分布Mutual exclusive,互不相容Mutual independence,互相独立NNatural boundary,自然边界Natural dead,自然死亡Natural zero,自然零Negative correlation,负相关Negative linear correlation,负线性相关Negatively skewed,负偏Newman-Keuls method, q 检验NK method, q 检验No statistical significance,无统计意义Nominal variable,名义变量Nonconstancy of variability,变异的非定常性Nonlinear regression,非线性相关Nonparametric statistics,非参数统计Nonparametric test,非参数检验Normal deviate,正态离差Normal distribution,正态分布Normal equation,正规方程组Normal ranges,正常范围Normal value,正常值Nuisance parameter,多余参数/讨厌参数Null hypothesis,无效假设Numerical variable,数值变量OObjective function,目标函数Observation unit,观察单位Observed value, 观察值One sided test,单侧检验One-way analysis of variance,单因素方差分析One way ANOVA ,单因素方差分析Open sequential trial,开放型序贯设计Optrim, 优切尾Optrim efficiency,优切尾效率Order statistics,顺序统计量Ordered categories,有序分类Ordinal logistic regression ,序数逻辑斯蒂回归Ordinal variable,有序变量Orthogonal basis,正交基Orthogonal design,正交试验设计Orthogonality conditions,正交条件ORTHOPLAN,正交设计Outlier cutoffs,离群值截断点Outliers,极端值OVERALS ,多组变量的非线性正规相关Overshoot,迭代过度PPaired design,配对设计Paired sample,配对样本Pairwise slopes,成对斜率Parabola,抛物线Parallel tests,平行试验Parameter,参数Parametric statistics,参数统计Parametric test,参数检验Partial correlation,偏相关Partial regression,偏回归Partial sorting,偏排序Partials residuals,偏残差Pattern,模式Pearson curves,皮尔逊曲线Peeling,退层Percent bar graph,百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves,百分位曲线Periodicity,周期性Permutation,排列P-estimator,P 估计量Pie graph,饼图Pitman estimator,皮特曼估计量Pivot,枢轴量Planar,平坦Planar assumption,平面的假设PLANCARDS,生成试验的计划卡Point estimation,点估计Poisson distribution,泊松分布Polishing,平滑Polled standard deviation,合并标准差Polled variance,合并方差Polygon,多边图Polynomial,多项式Polynomial curve,多项式曲线Population,总体Population attributable risk,人群归因危险度Positive correlation,正相关Positively skewed,正偏Posterior distribution,后验分布Power of a test,检验效能Precision,精密度Predicted value,预测值Preliminary analysis,预备性分析Principalcomponent analysis,主成分分析Priordistribution,先验分布Prior probability,先验概率Probabilistic model,概率模型probability,概率Probability density,概率密度Product moment,乘积矩/协方差Profile trace,截面迹图Proportion,比/构成比Proportion allocation in stratified random sampling,按比例分层随机抽样Proportionate,成比例Proportionate sub-class numbers,成比例次级组含量Prospective study,前瞻性调查Proximities, 亲近性Pseudo F test,近似F 检验Pseudo model,近似模型Pseudo sigma,伪标准差Purposive sampling,有目的抽样QQR decomposition, QR 分解Quadratic approximation,二次近似Qualitative classification,属性分类Qualitative method,定性方法Quantile-quantile plot,分位数-分位数图/Q-Q 图Quantitative analysis,定量分析Quartile,四分位数Quick Cluster,快速聚类RRadix sort,基数排序Random allocation,随机化分组Random blocks design,随机区组设计Random event,随机事件Randomization,随机化Range,极差/全距Rank correlation,等级相关Rank sum test,秩和检验Rank test,秩检验Ranked data,等级资料Rate,比率Ratio,比例Raw data,原始资料Rawresidual,原始残差Rayleigh's test,雷氏检验Rayleigh's Z,雷氏Z 值Reciprocal,倒数Reciprocal transformation,倒数变换Recording,记录Redescending estimators,回降估计量Reducing dimensions,降维Re-expression,重新表达Reference set,标准组Regionof acceptance,接受域Regression coefficient,回归系数Regression sum of square,回归平方和Rejection point,拒绝点Relative dispersion,相对离散度Relative number,相对数Reliability,可靠性Reparametrization,重新设置参数Replication,重复Report Summaries,报告摘要Residual sum of square,剩余平方和Resistance,耐抗性Resistant line,耐抗线Resistant technique,耐抗技术R-estimator of location,位置R 估计量R-estimator of scale,尺度R 估计量Retrospective study,回顾性调查Ridge trace,岭迹Ridit analysis , Ridit 分析Rotation, 旋转Rounding,舍入Row,行Row effects,行效应Row factor,行因素RXC table, RXC 表SSample,样本Sample regression coefficient,样本回归系数Sample size,样本量Sample standard deviation,样本标准差Sampling error,抽样误差SAS(Statistical analysis system ),SAS 统计软件包Scale,尺度/量表Scatter diagram,散点图Schematic plot,示意图/简图Score test,计分检验Screening,筛检SEASON, 季节分析Second derivative,二阶导数Second principal component,第二主成分SEM (Structural equation modeling),结构化方程模型Semi-logarithmic graph,半对数图Semi-logarithmic paper,半对数格纸Sensitivity curve,敏感度曲线Sequential analysis,贯序分析Sequential data set,顺序数据集Sequential design,贯序设计Sequential method,贯序法Sequential test,贯序检验法Serial tests,系列试验Short-cut method,简捷法Sigmoid curve, S 形曲线Sign function,正负号函数Sign test,符号检验Signed rank,符号秩Significance test,显著性检验Significant figure,有效数字Simple cluster sampling,简单整群抽样Simple correlation,简单相关Simple random sampling,简单随机抽样Simple regression,简单回归simple table,简单表Sine estimator,正弦Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skeweddistribution, 偏斜分布Skewness,偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS 统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwiseregression, 逐步回归Storage,存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t 化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival,生存分析Survival rate,生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Test(检验)Test of linearity, 线性检验Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Timeseries, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Twosided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α 错误TypeII error, 二类错误/β 错误UMVU, 方差一致最小无偏估计简称Unbiasedestimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Unweightedleast squares, 未加权最小平方法Upper limit,上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W 检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran 检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W 估计量W-estimation of location, 位置W 估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z 检验Zero correlation, 零相关Z-transformation, Z 变换。
Annual and interannual (ENSO) variability of spatial scaling properties of (NDVI) in Amazonia

Annual and interannual (ENSO)variability of spatial scaling propertiesof a vegetation index (NDVI)in AmazoniaGerma ´n Poveda *,Luis F.SalazarPosgrado en Recursos Hidra ´ulicos,Escuela de Geociencias y Medio Ambiente,Universidad Nacional de Colombia,Medellı´n,ColombiaReceived 15January 2004;received in revised form 3August 2004;accepted 5August 2004AbstractThe space–time variability of the Normalized Difference Vegetation Index (NDVI)over the Amazon River basin is quantified through thebi-dimensional Fourier spectrum,and moment-scaling analysis of monthly imagery at 8km resolution,for the period July 1981–November 2002.Monthly NDVI fields exhibit power law Fourier spectra,E (k )=ck Àb ,with k denoting the wavenumber,c the prefactor,and b the scaling exponent.Fourier spectra exhibit two scaling regimes separated at approximately 29km,above which NDVI exhibit long-range spatial correlations (0b b b 2),and below which NDVI behaves like white noise in space (b g 0).Series of monthly values of c (t )and b (t )exhibit high negative correlation (À0.88,P N 0.99),which suggest their linkages in power laws,but also that E t (k )=c (t )k Àb (t ),with t the time index.Results show a significant negative simultaneous correlation (À0.82,P N 0.95)between monthly series of average precipitation over the Amazon,h P (t )i ,and scaling exponents,b (t );and high positive lagged correlation (0.63,P N 0.95),between h P (t )i and h NDVI(t +3)i .Parameters also reflect the hydrological seasonal cycle over Amazonia:during the wet season (November–March),b (t )ranges between 0.9and 1.15,while during the dry season (May–September),b (t )g 1.30.These results reflect the more (less)coherent spatial effect of the dry (wet)season over Amazonia,which translates into longer (shorter)-range spatial correlations of the NDVI field,as witnessed by higher (lower)values of b (t ).At interannual timescales,both phases of ENSO reflect on both parameters,as b (t )is higher during El Nin ˜o than during La Nin ˜a,due to the more coherent effects of El Nin ˜o-related dryness,whereas NDVI spatial variability is enhanced during La Nin ˜a,due to positive rainfall anomalies.Results from the moment-scale analysis indicate the existence of multi-scaling in the spatial variability of NDVI fields.Departures from single scaling exhibit also annual and interannual variability,which consistently reflect the effects from both phases of ENSO.Furthermore,departures from single scaling are independent of the order moment,q ,as the PDF of departures scaled by the mean collapse to a unique distribution.These results point out that ideas of spatial scaling constitute a promising framework to synthesize important hydro-ecological processes of Amazonia.D 2004Elsevier Inc.All rights reserved.Keywords:Annual and interannual variability;Spatial scaling;Amazonia1.Introduction1.1.NDVI and the hydrologic cycleSatellite information has contributed to improve our understanding of the spatial variability of hydro-climatic and ecological processes.Vegetation activity is tightlycoupled with climate,hydro-ecological fluxes,and terrain dynamics,and it controls water,energy and carbon budgets in river basins at a wide range of space–time scales.Indices of vegetation activity are constructed using satellite infor-mation of reflectance of the relevant spectral bands which enhance the contribution of vegetation.One such an index is the Normalized Difference Vegetation Index (NDVI),defined as the ratio of (NIR ÀRed)and (NIR+Red),where NIR is the surface-reflected radiation in the near-infrared band (0.73–1.1A m),and Red is the reflected radiation in the red band (0.55–0.68A m).Theoretically,NDVI takes values in the range from À1to 1,but the observed range is usually0034-4257/$-see front matter D 2004Elsevier Inc.All rights reserved.doi:10.1016/j.rse.2004.08.001*Corresponding author.School of Geosciences and Environment,Universidad Nacional de Colombia,Carrera 80x Calle 65,Bloque M2-315,Medellin AA1027,Colombia.Tel.:+5744255122;fax:+5744255003.E-mail address:gpoveda@.co (G.Poveda).Remote Sensing of Environment 93(2004)391–401smaller,with values around0for bare soil(low or no vegetation),and values of0.9or larger for dense vegetation. The work of Tucker(1979)pioneered the study of vegetation dynamics using red and near infrared spectral measurements.Sellers(1985)showed that NDVI is directly related to the photosynthetic capacity of plant canopies, which explains why NDVI is highly and directly correlated to the intercepted fraction of photosynthetically active radiation.As such,NDVI is independent of solar radiation, although variations in solar radiation can affect retrievals of NDVI.The meaning of diverse spectral vegetation indices is explained and summarized in Myneni et al.(1995).As NDVI represents the photosynthetic capacity or photosynthetic active radiation(PAR)absorption by green leaves,it is associated with fundamental hydro-ecological processes such as precipitation,which in turn is also directly linked to photosynthesis and hence plant growth.A recent work by Lotsch et al.(2003b)provides a comprehensive global analysis of NDVI and precipitation.Other variables pertaining to the hydrologic cycle have also been linked to NDVI,such as evaporation(Szilagyi et al.,1998;Lotsch et al.,2003b),and soil moisture(Nicholson&Farrar,1994; Farrar et al.,1994;Poveda et al.,2001).A strong relation-ship between evapotranspiration and NDVI have been identified in wet environments by Seevers and Ottmann (1994),and Nicholson et al.(1996),but also in water-limited environments,as reported by Tucker and Choudhury (1987),Malo and Nicholson(1990),Nicholson et al.(1994), Grist et al.(1997),Szilagyi et al.(1998),and Lotsch et al. (2003a).Changes in vegetation patterns have been studied at a global scale through NDVI estimates(Lucht et al., 2002).In turn,Nemani et al.(2003)identify those regions of the world where primary production is limited by water,by temperature or by both.1.2.Physical settingThe Amazon River basin provides an excellent example of the coupling and feedbacks in the land surface–atmos-phere system,due to its area larger than6.4million km2, constitute largest in the world,its tropical setting,and complex eco-hydro-climatological dynamics that exert a global influence.Scientific research towards understanding the hydro-climatic and ecological functioning of the Amazon is currently undergoing within the b Large-Scale Atmos-phere–Biosphere Experiment in Amazonia Q(LBA)(see Avissar and Nobre,2002;Roberts et al.,2003).Both observations and modelling results suggest strong changes in global,regional and local atmospheric circulation patterns associated with deforestation or perturbations in the land surface–atmosphere interactions over the Amazon(Salati& V ose,1984;Silva Dias et al.,1987;Zeng et al.,1996;Zhang et al.,1996;Poveda&Mesa,1997;Marengo&Nobre,2001; Werth&Avissar,2002;Nobre et al.,2004).The seasonal cycle of precipitation exhibits a wet season during Novem-ber–March and a dry season during May–September,as a result of the latitudinal migration of the Intertropical Convergence Zone(Obregon&Nobre,1990;Zeng,1999), which interacts with the seasonal cycle of moisture-laden low level winds from the Atlantic Ocean,but also with complex feedbacks of the land surface–atmosphere system,including the significant role of evapotranspiration in precipitation recycling(Salati,1985;Eltahir&Bras,1994).This work aims to quantify how the spatial statistics of NDVI reflect the seasonal hydro-climatic variability of the Amazon.1.3.Interannual variability at ENSO timescaleAt interannual timescales,tropical South America exhib-its coherent hydro-climatic anomalies during both phases of the El Nin˜o/Southern Oscillation(ENSO)(Aceituno,1988; Kiladis&Diaz,1989;Chu,1991;Marengo&Hastenrath, 1993;Ropelewski&Halpert,1996;Poveda et al.,2001; Waylen&Poveda,2002).With minor regional exceptions in timing and amplitude,the region experiences negative anomalies in rainfall,river discharges,and soil moisture during the warm phase of ENSO(El Nin˜o),and positive anomalies during the cold phase(La Nin˜a).Both large-scale forcing and land surface hydrology play a key role on the dynamics of hydro-climatic effects of ENSO over the region (Marengo&Hastenrath,1993;Poveda&Mesa,1997), which lag anomalies in the tropical Pacific sea surface temperatures by several months.The ENSO signal prop-agates to the east in northern South America,leading hydrological anomalies by1month over western Colombia (Poveda&Mesa,1997)and by6–10months in the Amazon River basin(Richey et al.,1989;Chu,1991;Eagleson, 1994).Consistently,NDVI diminishes over tropical South America during the occurrence of the warm phase of ENSO (Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001). This work aims to quantify how the spatial statistics of NDVI reflect the interannual hydro-climatic variability of the Amazon,associated with both phases of ENSO.1.4.Scaling theories of hydro-ecological processesScaling theories have provided important clues towards understanding and modelling the space–time dynamics of diverse bio-geophysical processes,such as vegetation sur-face fluxes(Katul et al.,2001),tropical convective storms (Yano et al.,2001),modeling of rainfall fields through fractal,multi-scaling,and random cascade models(Lovejoy, 1981,1982;Lovejoy&Schertzer,1991,1992;Gupta& Waymire,1990;Over&Gupta,1994;Perica&Foufoula-Georgiou,1996;Foufoula-Georgiou,1998;Deidda et al., 1999;Harris et al.,2000;Jotithyangkoon et al.,2000; Nordstrom&Gupta,2003),maximum annual river flows (Gupta&Waymire,1990;Gupta&Dawdy,1995;Goodrich et al.,1997;Ogden&Dawdy,2003),infiltration in porous media(Barenblatt,1996),low river flows(Furey&Gupta, 2000),ecological processes(Tilman&Kareiva,1997; Bascompte&Sole,1998),and vegetation dynamics(HarteG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401 392et al.,1999;Milne&Cohen,1999;Milne et al.,2002).For instance,in the study of river floods,Gupta(2004)has explained how scaling statistics in maximum annual river flows can be used to test different physical hypotheses covering complex runoff dynamics on channel networks.Diverse multi-scale statistical techniques are used to characterize and quantify the scale dependence of geo-biophysical fields,including Fourier spectra,structure and moment-scale functions.These functions are easily comput-able and allow an understanding of the spatial structure of the fields over a wide range of scales.Also,the use of multi-scale functions allows one to identify the range of scales where the scale dependence of modelled and observed variability may deviate,and the range of scales where the two agree(Harris et al.,2000).Towards those ends,we estimate the bi-dimensional Fourier spectra of monthly NDVI fields over Amazonia,and quantify the time variability of its parameters,and how they reflect the time–space variability of NDVI and precipitation fields at annual and interannual timescales.By the same token,we like to investigate whether monthly NDVI fields exhibit simple of multi-scaling properties in space,and how they reflect the annual and interannual variability of NDVI. Thirdly,we investigate the time correlation between series of monthly values of c and b with average values of NDVI and precipitation over the entire Amazon,so as to encapsulate the hydro-ecological dynamics of Amazonia. The data sets and methodologies are described in Section2, while results are presented in Section3,and the conclusions are provided in Section4.2.Data sets and methodologiesWe used digital maps of monthly NDVI from the NASA Global Inventory Modeling and Mapping Studies (GIMMS NDVI),covering the period July1981through November2002.The imagery,which consists of8km spatial resolution NDVI images,was provided by C.J. Tucker and his colleagues at NASA Goddard Space Flight Center.The GIMMS NDVI database exhibits a great deal of improvements with respect to previous NDVI data sets, including corrections for:(i)residual sensor degradation and sensor intercalibration differences;(ii)distortions caused by persistent cloud cover in tropical evergreen broadleaf forests;(iii)solar zenith angle and viewing angle effects;(iv)volcanic aerosols;(v)missing data in the Northern Hemisphere during winter using interpolation; and(vi)short-term atmospheric aerosol effects,atmos-pheric water vapor effects,and cloud cover.For details of the GIMMS NDVI data set,see Pinzo´n et al.(submitted for publication).The GIMMS NDVI data set has been rescaled in such a way that the original values in theÀ1 to1range are obtained as ndvi=(NDVIÀ1)/249À0.05, with values larger than1representing water bodies or bad data.Precipitation data for the Amazon basin were obtained from the data set produced by the Earth Observing System-Amazon Project(EOSAP)developed by Instituto Nacional de Pesquisas Espaciais(INPE),Brazil,and the University of Washington,and contains gridded monthly rainfall(0.28 latitudeÂ0.28longitude),for the period1972–1992.This data set was provided by the Global Hydrology and Climate Center of NASA.For details of this data set,see http:// /.With the purpose of implementing the spatial scaling analysis,a2048km scale region was defined inside the Amazon basin.The observed NDVI field for July1981is shown in Fig.1,aggregated at a32km scale.Character-ization of the spatial scaling properties of NDVI monthly fields was performed through estimation of the bi-dimen-sional Fourier spectrum,and moment-scale analysis.A detailed description of the methods is provided in the following section.2.1.Bi-dimensional Fourier spectrumMany geophysical phenomena exhibit power law decay-ing Fourier spectra,(Korvin,1992;Mandelbrot,1998),i.e., E kðÞf kÀb¼ckÀbð1Þwith k being the wavenumber,c is the prefactor,and b is the scaling exponent.The spectral slope,b,becomes a measure of roughness(Davis et al.,1996;Harris et al., 1996),with low spectral slopes corresponding to rougher, less correlated fields.Scaling exponents in Fourier spectra contain key insights on the dynamics underlying the physics of highly complex phenomena.For instance,the well-known behavior of dissipation of kinetic energy in turbulent flows,for which E(k)~k5/3(Kolmogorov,1941, 1962;Frisch,1995),whose scaling exponent,5/3,summa-rizes the rate at which kinetic energy is gradually trans-ferred from larger to smaller spatial scales,such that the mean kinetic energy per unit mass per unit time is conserved.Many other geophysical phenomena exhibit power law Fourier spectra,whose scaling exponents reflect different types of statistical memory and the scale of fluctuation which is inherent to their space–time correla-tions(Korvin,1992;Mesa&Poveda,1993;Turcotte, 1997;Mandelbrot,1998;Yano et al.,2001).The bi-dimensional power spectrum is computed using standard2-D Fast Fourier Transform(FFT)algorithms (Press et al.,1992).The Fourier power or energy spectrum, E(k x,k y)of a two-dimensional field,is found by multiplying the2-D FFT by its complex conjugate,where k x and k y are the wavenumber components(Harris et al.,2000).To facilitate visualization and comparison,the2-D power spectra from the NDVI fields are averaged angularly about k x=k y=0to produce the isotropic energy spectrum,E(k), with k¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffik2xþk2yq.Such isotropic energy spectrum does not mean that the field is isotropic,but rather that the angularG.Poveda,L.F.Salazar/Remote Sensing of Environment93(2004)391–401393averaging about k x =k y =0integrates the anisotropy (Harris et al.,2000).2.2.Moment-scaling analysisMoment-scaling analysis allows the quantification of the spatial intermittency (roughness)of a field,and provides a test for the type of spatial (single or multi-)scaling behavior of random fields (Over &Gupta,1994;Harris et al.,2000).Statistical self-similarity can be thought of as statistical similarity of a random field across multiple scales,then simple scaling is a type of statistical self-similarity.Consider a random field,{X (t );t a I },where I represents an index set,and an arbitrary scalar k N 0.The random field is defined to be simple scaling if the following holds,X k t ðÞ¼dk h X t ðÞð2Þwhere the equality is understood in the sense of all finite dimensional distribution functions.From the definition of statistical moments given by E [X q]=R x q f (x )d x ,q =1,2,3,...,it is concluded that for a simple scaling random field,X (t ),E X q k t ðÞ½ ¼k h q E X q t ðÞ½ ;q ¼1;2;3;Nor ;log E X q k t ðÞ½ ¼q h log k þc q t ðÞ;ð3Þwhere c q (t )=log E [X q (t )].Eq.(3)shows that simple scaling must satisfy two conditions:(i)log–log linearity;(ii)linear slope growth,i.e.,s (q )=q h ,whereas multi-scaling holds for a nonlinear slope growth.In our case,the expected value in Eq.(3)arises from the equation that defines the scaling moments of a field X j ,which are computed for a range of averaging scales,r ,with higher values of r implying examining the phenomena at finer spatial resolution.Therefore,M q r ðÞ¼hj X r x ;y ðÞj q ið4Þwhere X r represents field values at scale r ,q is the order of the moment,and h ...i denotes the expected value of NDVI over all pixels at scale r .Typically,the scale of the image is dyadically reduced from its original highest resolution (r =1/(1pixel))by successive spatial averaging of the field by a factor of 2at each step,i.e.,r =1/(2pixels)=0.5,r =1/(4pixels)=0.25,...,r =1/(256pixels)=3.9Â10À3.Scaling of the moments means that (Gupta &Waymire,1993),M q r ðÞf r Às q ðÞð5Þwhere s (q )is the moment scaling exponent function that is estimated by log–log linear regressions of the q th moment of the NDVI field,on a scan by scan basis,as |X r |vs.log r ,for each q .It is easy to check that s (1)=0since the mean of the entire field does not depend upon the scale.Thelog–logFig.1.Location of the study region depicting the NDVI field for July 1981,aggregated at a 32km scale.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401394linearity of log M q (r )vs.log r provides a test of the scaling hypothesis for the moment of order q .3.Results3.1.Bi-dimensional Fourier spectrumOur generalized results indicate that the Fourier spectra exhibit two regions characterized by different scaling exponents,b ,separated at the wavenumber k =0.034km À1,which corresponds to 28.6km.Fig.2shows the 2-D Fourier spectra for the September 1989NDVI field.At larger spatial scales,the NDVI fields exhibit long-range correlations characterized by 0b b b 2,whereas for larger wavenumbers (smaller spatial scales),the spectrum becomes scale independent,with b g 0,thus meaning that the spatial variability of NDVI behaves irregularly,as white noise in space.Long-range correlations in the spatial distribution of water and energy-limited vegetation have been identified for the Columbia River basin in the USA (Milne et al.,2002).Analysis of the time evolution of monthly estimated values of scaling exponents,b (t ),and prefactors,c (t ),t =1,...,257,indicates a high negative correlation coefficient (À0.88,P N 0.95),as shown in Fig.3,which means that c (t )=f [b (t )],with f [d ]representing a linear function.This result points out to the existence of a strong association between these two parameters in power laws and scaling relationships;an idea that was introduced in the context of the Hurst effect in geophysical records (Mesa &Poveda,1993),which deserves further investigation.Furthermore,our results indicate that both parameters of the Fourier spectra vary with time,and thus E t (k )=c (t )k Àb (t ),where k denotes the wavenumber,and t represents the time index.Time series of monthly values of average precipitation and NDVI over the Amazon were estimated by averaging values of each field for a fixed month,as,h P t ðÞi ¼1=nXn i ¼1p i !t;and h NDVI t ðÞi ¼1=nX n i ¼1ndvi i!tð6Þwhere n denotes the number of pixels with information foreach field:12,991for precipitation,and 65,536for NDVI.Results show a significant negative correlation (À0.82,P N 0.95)between monthly values of average precipitation,h P (t )i over the Amazon and scaling exponents,b (t ),as illustrated in Fig.4.Such negative correlation indicates that wet months exhibit rougher (less spatially correlated)NDVI fields,which are encapsulated in lower values of b (t ).On the contrary,dry periods are associated with more coherent and longer-range correlated NDVI fields,which are reflected in higher values of b (t ).Accordingly,the afore-mentioned seasonal cycle of average precipitation,and the concomitant spatial variability of NDVI are also reflectedinFig.2.Bi-dimensional Fourier spectra of the NDVI field for September 1989,with scaling exponent,b =1.11,and prefactor,c =0.034.E (k )has arbitrary units,as NDVI is dimensionless.Values of NDVI are rescaled is such a manner that original data are recovered as ndvi=(NDVI À1)/249–0.05.The range of spatial scales covers from 512to 16km.The dotted line separates the two scaling regions of the spectrum at k =0.035km À1,which corresponds to a spatial scale of 28.6km.Fig.3.Time evolution of prefactors,c (t ),and scaling exponents,b (t ),for estimated bi-dimensional Fourier spectra of NDVI monthly fields,during the study period.Simultaneous correlation coefficient is À0.88(P N0.99).Fig. 4.Time evolution of monthly mean precipitation,h P (t )i ,over Amazonia and scaling exponents,b (t ).Simultaneous correlation is À0.82(P N 0.95).G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401395a high positive correlation coefficient (0.72,P N 0.95)between the monthly series of average precipitation,h P (t )i and that of 6-month lagged scaling exponents,hb (t +6)i (not shown here).Despite that no significant correlation (À0.062)appears between the series of h P (t )i and h NDVI(t )i ,there is a significant positive correlation at 3-month lag (0.63,P N 0.95),see Fig.5,when precipitation leads NDVI.Such time lag suggests an integrated timescale at which rainfall affects NDVI dynamics at basin scale.The physical origin of this observation lies in the complex interactions of the land surface–atmosphere system,which include the afore-mentioned important effect of precipitation recycling in Amazonia (Salati,1985;Eltahir &Bras,1994).This observation deserves further investigation.3.1.1.Annual and interannual timescalesThe average long-term annual cycle of b (t )and c (t )were estimated from the 257estimated values (July 1981–November 2002).There is a strong negatively correlated seasonal cycle of prefactors,c (t ),and scaling exponents,b (t ),as evidenced in Fig.6.During the wet season (November–March),the estimated values of b (t )lie between 0.9and 1.15,while during the dry season (May–September),higher values are on the order of b (t )=1.30.These results are explained by the more coherent spatial effects of the dry season over the Amazon basin,which produce long-range spatial correlations in the NDVI field,reflected in higher estimates of b (t ).The annual cycle of prefactors,c (t ),exhibit higher values (~0.09–0.10)during the wet season,and lower values (~0.02–0.30)during the dry season.An understanding of the physical processes that govern such a strong association between scaling exponents and prefactors at seasonal scales is a topic of further research.At interannual timescales,ENSO strongly affects vege-tation activity and NDVI variability in Amazonia (Gutman,1991;Kogan &Sullivan,1993;Myneni et al.,1996;Asner et al.,2000;Poveda et al.,2001;Pinzo ´n,2002).Fig.7shows the annual cycle of scaling exponents,b (t ),during two contrasting ENSO years,i.e.,the 1991–1992El Nin ˜o,and the 1988–1989La Nin ˜a,as well as the average during normal years.The annual cycle is defined from June (year 0)through May (year +1),to better capture the aforementioned delayed effects of both ENSO phases.Overall,results indicate that the phase of the annual cycle of b (t )remains unchanged during both phases of ENSO,but there is a clear-cut effect on its amplitude.This is evidenced by higher values of b (t )during El Nin ˜o as compared with those during La Nin ˜a and normal years,throughout the annual cycle.Interestingly enough,the highest values of the scaling exponent are attained during August for both ENSO phases,and the lower values appears in February–March during El Nin ˜o,and in November–February during La Nin ˜a.This observation can be explained by the spatially coherent dryness caused over the Amazon by the warm phase of ENSO.It is well known that,in general,the Amazon basin experiences strong droughts during El Nin ˜o,and positive rainfall anomalies during La Nin ˜a (Richey et al.,1989;Fig.5.Time evolution of average values of monthly precipitation,h P (t )i ,and NDVI h NDVI(t )i ,over Amazonia.The caption of Fig.2explains the range of NDVI values.The low simultaneous correlation coefficient (À0.06)increases to À0.63(P N 0.95),when precipitation lead values of NDVI by 3months.Fig.6.Long-term annual cycle of estimated prefactors,c (t ),and scaling exponents,b (t ),from the estimated 2-D Fourierspectra.Fig.7.Annual cycle of scaling exponents,b (t ),during the 1991–1992El Nin ˜o event,the 1988–1989La Nin ˜a event,and during normal years.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401396Marengo,1992;Marengo &Hastenrath,1993;Obregon &Nobre,1990;Poveda &Mesa,1997;Poveda et al.,2001),whose effects are stronger in northern and central Amazonia(Marengo et al.,1998).The most remarkable differences occur in the November–March wet season during both phases of ENSO.During this epoch,both ENSO phases attain their maximum amplitude,and the associated tele-connections are more strongly developed,which in con-junction with land surface–atmosphere feedbacks cause stronger hydro-ecological anomalies,which affect the NDVI response over the Amazon.Our results confirm the coherent large spatial scale effects of El Nin ˜o-related drought over the Amazon basin,as a result of the 1991–1992event.It is concluded that scaling exponents,b (t ),exhibit significant variability at ENSO timescales,which are consistent with and reflect the identified hydrological anomalies.Similar to the temporal behavior of h NDVI(t )i ,results for the scaling exponents confirm that NDVI fields are more spatially correlated during El Nin ˜o than during La Nin ˜a.The interannual variability associated with both phases of ENSO is consistently exemplified by the evolution of b (t )and c (t )during the 1997–1998El Nin ˜o event,and during the 1998–2000La Nin ˜a event,shown in Fig.3.3.2.Moment-scaling analysisMoment-scale analysis were performed after checking for the condition that b (t )b 2(Harris et al.,2000).Fig.8shows the results for July 1991,with the scaling of marginal moments with q =0.5,1.0,...,4(top),and the estimated s ðq ˆÞcurve (bottom).Results indicate that monthly NDVI fields exhibit multi-scaling behavior in space,as indicated by the nonlinear behavior of the s ðq ˆÞfunction.Deviations from simple scaling were quantified as D q ¼s ðq ˆÞobserved Às q ðÞtheoretical ,e.g.,the difference between the sample values of the function s ðq ˆÞ,with respect to the linear growth for simple scaling (see Fig.9).Two features are worth mentioning:(i)there is a clear-cut annualandFig.8.Scaling of marginal moments with q =0.5,1.0,...,4(top),and estimated s ðq ˆÞcurve (bottom),for NDVI in July 1991.The straight continuous line and 95%confidence intervals (dashed)in the bottom pannel denote the theoretical behavior for simplescaling.Fig.9.Time evolution of departures from simple scaling,of the estimated s ðq ˆÞcurve,for q =1.5,...,4.0,during the study period.G.Poveda,L.F .Salazar /Remote Sensing of Environment 93(2004)391–401397。
物理化学基本概念

物理化学概念及术语A B C D E F G H I J K L M N O P Q R S T U V W X Y Z概念及术语 (16)BET公式BET formula (16)DLVO理论 DLVO theory (16)HLB法hydrophile-lipophile balance method (16)pVT性质 pVT property (16)ζ电势 zeta potential (16)阿伏加德罗常数 Avogadro’number (16)阿伏加德罗定律 Avogadro law (16)阿累尼乌斯电离理论Arrhenius ionization theory (16)阿累尼乌斯方程Arrhenius equation (17)阿累尼乌斯活化能 Arrhenius activation energy (17)阿马格定律 Amagat law (17)艾林方程 Erying equation (17)爱因斯坦光化当量定律 Einstein’s law of photochemical equivalence (17)爱因斯坦-斯托克斯方程 Einstein-Stokes equation (17)安托万常数 Antoine constant (17)安托万方程 Antoine equation (17)盎萨格电导理论Onsager’s theory of conductance (17)半电池half cell (17)半衰期half time period (18)饱和液体 saturated liquids (18)饱和蒸气 saturated vapor (18)饱和吸附量 saturated extent of adsorption (18)饱和蒸气压 saturated vapor pressure (18)爆炸界限 explosion limits (18)比表面功 specific surface work (18)比表面吉布斯函数 specific surface Gibbs function (18)比浓粘度 reduced viscosity (18)标准电动势 standard electromotive force (18)标准电极电势 standard electrode potential (18)标准摩尔反应焓 standard molar reaction enthalpy (18)标准摩尔反应吉布斯函数 standard Gibbs function of molar reaction (18)标准摩尔反应熵 standard molar reaction entropy (19)标准摩尔焓函数 standard molar enthalpy function (19)标准摩尔吉布斯自由能函数 standard molar Gibbs free energy function (19)标准摩尔燃烧焓 standard molar combustion enthalpy (19)标准摩尔熵 standard molar entropy (19)标准摩尔生成焓 standard molar formation enthalpy (19)标准摩尔生成吉布斯函数 standard molar formation Gibbs function (19)标准平衡常数 standard equilibrium constant (19)标准氢电极 standard hydrogen electrode (19)标准态 standard state (19)标准熵 standard entropy (20)标准压力 standard pressure (20)标准状况 standard condition (20)表观活化能apparent activation energy (20)表观摩尔质量 apparent molecular weight (20)表观迁移数apparent transference number (20)表面 surfaces (20)表面过程控制 surface process control (20)表面活性剂surfactants (21)表面吸附量 surface excess (21)表面张力 surface tension (21)表面质量作用定律 surface mass action law (21)波义尔定律 Boyle law (21)波义尔温度 Boyle temperature (21)波义尔点 Boyle point (21)玻尔兹曼常数 Boltzmann constant (22)玻尔兹曼分布 Boltzmann distribution (22)玻尔兹曼公式 Boltzmann formula (22)玻尔兹曼熵定理 Boltzmann entropy theorem (22)泊Poise (22)不可逆过程 irreversible process (22)不可逆过程热力学thermodynamics of irreversible processes (22)不可逆相变化 irreversible phase change (22)布朗运动 brownian movement (22)查理定律 Charle’s law (22)产率 yield (23)敞开系统 open system (23)超电势 over potential (23)沉降 sedimentation (23)沉降电势 sedimentation potential (23)沉降平衡 sedimentation equilibrium (23)触变 thixotropy (23)粗分散系统 thick disperse system (23)催化剂 catalyst (23)单分子层吸附理论 mono molecule layer adsorption (23)单分子反应 unimolecular reaction (23)单链反应 straight chain reactions (24)弹式量热计 bomb calorimeter (24)道尔顿定律 Dalton law (24)道尔顿分压定律 Dalton partial pressure law (24)德拜和法尔肯哈根效应Debye and Falkenhagen effect (24)德拜立方公式 Debye cubic formula (24)德拜-休克尔极限公式 Debye-Huckel’s limiting equation (24)等焓过程 isenthalpic process (24)等焓线isenthalpic line (24)等几率定理 theorem of equal probability (24)等温等容位Helmholtz free energy (25)等温等压位Gibbs free energy (25)等温方程 equation at constant temperature (25)低共熔点 eutectic point (25)低共熔混合物 eutectic mixture (25)低会溶点 lower consolute point (25)低熔冰盐合晶 cryohydric (26)第二类永动机 perpetual machine of the second kind (26)第三定律熵 Third-Law entropy (26)第一类永动机 perpetual machine of the first kind (26)缔合化学吸附 association chemical adsorption (26)电池常数 cell constant (26)电池电动势 electromotive force of cells (26)电池反应 cell reaction (27)电导 conductance (27)电导率 conductivity (27)电动势的温度系数 temperature coefficient of electromotive force (27)电动电势 zeta potential (27)电功electric work (27)电化学 electrochemistry (27)电化学极化 electrochemical polarization (27)电极电势 electrode potential (27)电极反应 reactions on the electrode (27)电极种类 type of electrodes (27)电解池 electrolytic cell (28)电量计 coulometer (28)电流效率current efficiency (28)电迁移 electro migration (28)电迁移率 electromobility (28)电渗 electroosmosis (28)电渗析 electrodialysis (28)电泳 electrophoresis (28)丁达尔效应 Dyndall effect (28)定容摩尔热容 molar heat capacity under constant volume (28)定容温度计 Constant voIume thermometer (28)定压摩尔热容 molar heat capacity under constant pressure (29)定压温度计 constant pressure thermometer (29)定域子系统 localized particle system (29)动力学方程kinetic equations (29)动力学控制 kinetics control (29)独立子系统 independent particle system (29)对比摩尔体积 reduced mole volume (29)对比体积 reduced volume (29)对比温度 reduced temperature (29)对比压力 reduced pressure (29)对称数 symmetry number (29)对行反应reversible reactions (29)对应状态原理 principle of corresponding state (29)多方过程polytropic process (30)多分子层吸附理论 adsorption theory of multi-molecular layers (30)二级反应second order reaction (30)二级相变second order phase change (30)法拉第常数 faraday constant (31)法拉第定律 Faraday’s law (31)反电动势back E.M.F. (31)反渗透 reverse osmosis (31)反应分子数 molecularity (31)反应级数 reaction orders (31)反应进度 extent of reaction (32)反应热heat of reaction (32)反应速率rate of reaction (32)反应速率常数 constant of reaction rate (32)范德华常数 van der Waals constant (32)范德华方程 van der Waals equation (32)范德华力 van der Waals force (32)范德华气体 van der Waals gases (32)范特霍夫方程 van’t Hoff equation (32)范特霍夫规则 van’t Hoff rule (33)范特霍夫渗透压公式 van’t Hoff equation of osmotic pressure (33)非基元反应 non-elementary reactions (33)非体积功 non-volume work (33)非依时计量学反应 time independent stoichiometric reactions (33)菲克扩散第一定律 Fick’s first law of diffusion (33)沸点 boiling point (33)沸点升高 elevation of boiling point (33)费米-狄拉克统计Fermi-Dirac statistics (33)分布 distribution (33)分布数 distribution numbers (34)分解电压 decomposition voltage (34)分配定律 distribution law (34)分散系统 disperse system (34)分散相 dispersion phase (34)分体积 partial volume (34)分体积定律 partial volume law (34)分压 partial pressure (34)分压定律 partial pressure law (34)分子反应力学 mechanics of molecular reactions (34)分子间力 intermolecular force (34)分子蒸馏molecular distillation (35)封闭系统 closed system (35)附加压力 excess pressure (35)弗罗因德利希吸附经验式 Freundlich empirical formula of adsorption (35)负极 negative pole (35)负吸附 negative adsorption (35)复合反应composite reaction (35)盖.吕萨克定律 Gay-Lussac law (35)盖斯定律 Hess law (35)甘汞电极 calomel electrode (35)感胶离子序 lyotropic series (35)杠杆规则 lever rule (35)高分子溶液 macromolecular solution (36)高会溶点 upper consolute point (36)隔离法the isolation method (36)格罗塞斯-德雷珀定律 Grotthus-Draoer’s law (36)隔离系统 isolated system (37)根均方速率 root-mean-square speed (37)功 work (37)功函work content (37)共轭溶液 conjugate solution (37)共沸温度 azeotropic temperature (37)构型熵configurational entropy (37)孤立系统 isolated system (37)固溶胶 solid sol (37)固态混合物 solid solution (38)固相线 solid phase line (38)光反应 photoreaction (38)光化学第二定律 the second law of actinochemistry (38)光化学第一定律 the first law of actinochemistry (38)光敏反应 photosensitized reactions (38)光谱熵 spectrum entropy (38)广度性质 extensive property (38)广延量 extensive quantity (38)广延性质 extensive property (38)规定熵 stipulated entropy (38)过饱和溶液 oversaturated solution (38)过饱和蒸气 oversaturated vapor (38)过程 process (39)过渡状态理论 transition state theory (39)过冷水 super-cooled water (39)过冷液体 overcooled liquid (39)过热液体 overheated liquid (39)亥姆霍兹函数 Helmholtz function (39)亥姆霍兹函数判据 Helmholtz function criterion (39)亥姆霍兹自由能 Helmholtz free energy (39)亥氏函数 Helmholtz function (39)焓 enthalpy (39)亨利常数 Henry constant (39)亨利定律 Henry law (39)恒沸混合物 constant boiling mixture (40)恒容摩尔热容 molar heat capacity at constant volume (40)恒容热 heat at constant volume (40)恒外压 constant external pressure (40)恒压摩尔热容 molar heat capacity at constant pressure (40)恒压热 heat at constant pressure (40)化学动力学chemical kinetics (40)化学反应计量式 stoichiometric equation of chemical reaction (40)化学反应计量系数 stoichiometric coefficient of chemical reaction (40)化学反应进度 extent of chemical reaction (41)化学亲合势 chemical affinity (41)化学热力学chemical thermodynamics (41)化学势 chemical potential (41)化学势判据 chemical potential criterion (41)化学吸附 chemisorptions (41)环境 environment (41)环境熵变 entropy change in environment (41)挥发度volatility (41)混合熵 entropy of mixing (42)混合物 mixture (42)活度 activity (42)活化控制 activation control (42)活化络合物理论 activated complex theory (42)活化能activation energy (43)霍根-华森图 Hougen-Watson Chart (43)基态能级 energy level at ground state (43)基希霍夫公式 Kirchhoff formula (43)基元反应elementary reactions (43)积分溶解热 integration heat of dissolution (43)吉布斯-杜亥姆方程 Gibbs-Duhem equation (43)吉布斯-亥姆霍兹方程 Gibbs-Helmhotz equation (43)吉布斯函数 Gibbs function (43)吉布斯函数判据 Gibbs function criterion (44)吉布斯吸附公式Gibbs adsorption formula (44)吉布斯自由能 Gibbs free energy (44)吉氏函数 Gibbs function (44)极化电极电势 polarization potential of electrode (44)极化曲线 polarization curves (44)极化作用 polarization (44)极限摩尔电导率 limiting molar conductivity (44)几率因子 steric factor (44)计量式 stoichiometric equation (44)计量系数 stoichiometric coefficient (45)价数规则 rule of valence (45)简并度 degeneracy (45)键焓bond enthalpy (45)胶冻 broth jelly (45)胶核 colloidal nucleus (45)胶凝作用 demulsification (45)胶束micelle (45)胶体 colloid (45)胶体分散系统 dispersion system of colloid (45)胶体化学 collochemistry (45)胶体粒子 colloidal particles (45)胶团 micelle (45)焦耳Joule (45)焦耳-汤姆生实验 Joule-Thomson experiment (46)焦耳-汤姆生系数 Joule-Thomson coefficient (46)焦耳-汤姆生效应 Joule-Thomson effect (46)焦耳定律 Joule's law (46)接触电势contact potential (46)接触角 contact angle (46)节流过程 throttling process (46)节流膨胀 throttling expansion (46)节流膨胀系数 coefficient of throttling expansion (46)结线 tie line (46)结晶热heat of crystallization (47)解离化学吸附 dissociation chemical adsorption (47)界面 interfaces (47)界面张力 surface tension (47)浸湿 immersion wetting (47)浸湿功 immersion wetting work (47)精馏 rectify (47)聚(合)电解质polyelectrolyte (47)聚沉 coagulation (47)聚沉值 coagulation value (47)绝对反应速率理论 absolute reaction rate theory (47)绝对熵 absolute entropy (47)绝对温标absolute temperature scale (48)绝热过程 adiabatic process (48)绝热量热计adiabatic calorimeter (48)绝热指数 adiabatic index (48)卡诺定理 Carnot theorem (48)卡诺循环 Carnot cycle (48)开尔文公式 Kelvin formula (48)柯诺瓦洛夫-吉布斯定律 Konovalov-Gibbs law (48)科尔劳施离子独立运动定律 Kohlrausch’s Law of Independent Migration of Ions (48)可能的电解质potential electrolyte (49)可逆电池 reversible cell (49)可逆过程 reversible process (49)可逆过程方程 reversible process equation (49)可逆体积功 reversible volume work (49)可逆相变 reversible phase change (49)克拉佩龙方程 Clapeyron equation (49)克劳修斯不等式 Clausius inequality (49)克劳修斯-克拉佩龙方程 Clausius-Clapeyron equation (49)控制步骤 control step (50)库仑计 coulometer (50)扩散控制 diffusion controlled (50)拉普拉斯方程 Laplace’s equation (50)拉乌尔定律 Raoult law (50)兰格缪尔-欣谢尔伍德机理 Langmuir-Hinshelwood mechanism (50)雷利公式 Rayleigh equation (50)兰格缪尔吸附等温式 Langmuir adsorption isotherm formula (50)冷冻系数coefficient of refrigeration (50)冷却曲线 cooling curve (51)离解热heat of dissociation (51)离解压力dissociation pressure (51)离域子系统 non-localized particle systems (51)离子的标准摩尔生成焓 standard molar formation of ion (51)离子的电迁移率 mobility of ions (51)离子的迁移数 transport number of ions (51)离子独立运动定律 law of the independent migration of ions (51)离子氛 ionic atmosphere (51)离子强度 ionic strength (51)理想混合物 perfect mixture (52)理想气体 ideal gas (52)理想气体的绝热指数 adiabatic index of ideal gases (52)理想气体的微观模型 micro-model of ideal gas (52)理想气体反应的等温方程 isothermal equation of ideal gaseous reactions (52)理想气体绝热可逆过程方程 adiabatic reversible process equation of ideal gases (52)理想气体状态方程 state equation of ideal gas (52)理想稀溶液 ideal dilute solution (52)理想液态混合物 perfect liquid mixture (52)粒子 particles (52)粒子的配分函数 partition function of particles (53)连串反应consecutive reactions (53)链的传递物 chain carrier (53)链反应 chain reactions (53)量热熵 calorimetric entropy (53)量子统计quantum statistics (53)量子效率 quantum yield (53)临界参数 critical parameter (53)临界常数 critical constant (53)临界点 critical point (53)临界胶束浓度critical micelle concentration (53)临界摩尔体积 critical molar volume (54)临界温度 critical temperature (54)临界压力 critical pressure (54)临界状态 critical state (54)零级反应zero order reaction (54)流动电势 streaming potential (54)流动功 flow work (54)笼罩效应 cage effect (54)路易斯-兰德尔逸度规则 Lewis-Randall rule of fugacity (54)露点 dew point (54)露点线 dew point line (54)麦克斯韦关系式 Maxwell relations (55)麦克斯韦速率分布 Maxwell distribution of speeds (55)麦克斯韦能量分布 MaxwelIdistribution of energy (55)毛细管凝结 condensation in capillary (55)毛细现象 capillary phenomena (55)米凯利斯常数 Michaelis constant (55)摩尔电导率 molar conductivity (56)摩尔反应焓 molar reaction enthalpy (56)摩尔混合熵 mole entropy of mixing (56)摩尔气体常数 molar gas constant (56)摩尔热容 molar heat capacity (56)摩尔溶解焓 mole dissolution enthalpy (56)摩尔稀释焓 mole dilution enthalpy (56)内扩散控制 internal diffusions control (56)内能 internal energy (56)内压力 internal pressure (56)能级 energy levels (56)能级分布 energy level distribution (57)能量均分原理 principle of the equipartition of energy (57)能斯特方程 Nernst equation (57)能斯特热定理 Nernst heat theorem (57)凝固点 freezing point (57)凝固点降低 lowering of freezing point (57)凝固点曲线 freezing point curve (58)凝胶 gelatin (58)凝聚态 condensed state (58)凝聚相 condensed phase (58)浓差超电势 concentration over-potential (58)浓差极化 concentration polarization (58)浓差电池 concentration cells (58)帕斯卡pascal (58)泡点 bubble point (58)泡点线 bubble point line (58)配分函数 partition function (58)配分函数的析因子性质 property that partition function to be expressed as a product of the separate partition functions for each kind of state (58)碰撞截面 collision cross section (59)碰撞数 the number of collisions (59)偏摩尔量 partial mole quantities (59)平衡常数(理想气体反应) equilibrium constants for reactions of ideal gases (59)平动配分函数 partition function of translation (59)平衡分布 equilibrium distribution (59)平衡态 equilibrium state (60)平衡态近似法 equilibrium state approximation (60)平衡状态图 equilibrium state diagram (60)平均活度 mean activity (60)平均活度系统 mean activity coefficient (60)平均摩尔热容 mean molar heat capacity (60)平均质量摩尔浓度 mean mass molarity (60)平均自由程mean free path (60)平行反应parallel reactions (61)破乳 demulsification (61)铺展 spreading (61)普遍化范德华方程 universal van der Waals equation (61)其它功 the other work (61)气化热heat of vaporization (61)气溶胶 aerosol (61)气体常数 gas constant (61)气体分子运动论 kinetic theory of gases (61)气体分子运动论的基本方程 foundamental equation of kinetic theory of gases (62)气溶胶 aerosol (62)气相线 vapor line (62)迁移数 transport number (62)潜热latent heat (62)强度量 intensive quantity (62)强度性质 intensive property (62)亲液溶胶 hydrophilic sol (62)氢电极 hydrogen electrodes (62)区域熔化zone melting (62)热 heat (62)热爆炸 heat explosion (62)热泵 heat pump (63)热功当量mechanical equivalent of heat (63)热函heat content (63)热化学thermochemistry (63)热化学方程thermochemical equation (63)热机 heat engine (63)热机效率 efficiency of heat engine (63)热力学 thermodynamics (63)热力学第二定律 the second law of thermodynamics (63)热力学第三定律 the third law of thermodynamics (63)热力学第一定律 the first law of thermodynamics (63)热力学基本方程 fundamental equation of thermodynamics (64)热力学几率 thermodynamic probability (64)热力学能 thermodynamic energy (64)热力学特性函数characteristic thermodynamic function (64)热力学温标thermodynamic scale of temperature (64)热力学温度thermodynamic temperature (64)热熵thermal entropy (64)热效应heat effect (64)熔点曲线 melting point curve (64)熔化热heat of fusion (64)溶胶 colloidal sol (65)溶解焓 dissolution enthalpy (65)溶液 solution (65)溶胀 swelling (65)乳化剂 emulsifier (65)乳状液 emulsion (65)润湿 wetting (65)润湿角 wetting angle (65)萨克尔-泰特洛德方程 Sackur-Tetrode equation (66)三相点 triple point (66)三相平衡线 triple-phase line (66)熵 entropy (66)熵判据 entropy criterion (66)熵增原理 principle of entropy increase (66)渗透压 osmotic pressure (66)渗析法 dialytic process (67)生成反应 formation reaction (67)升华热heat of sublimation (67)实际气体 real gas (67)舒尔采-哈迪规则 Schulze-Hardy rule (67)松驰力relaxation force (67)松驰时间time of relaxation (67)速度常数reaction rate constant (67)速率方程rate equations (67)速率控制步骤rate determining step (68)塔费尔公式 Tafel equation (68)态-态反应 state-state reactions (68)唐南平衡 Donnan equilibrium (68)淌度 mobility (68)特鲁顿规则 Trouton rule (68)特性粘度 intrinsic viscosity (68)体积功 volume work (68)统计权重 statistical weight (68)统计热力学 statistic thermodynamics (68)统计熵 statistic entropy (68)途径 path (68)途径函数 path function (69)外扩散控制 external diffusion control (69)完美晶体 perfect crystalline (69)完全气体 perfect gas (69)微观状态 microstate (69)微态 microstate (69)韦斯顿标准电池 Weston standard battery (69)维恩效应Wien effect (69)维里方程 virial equation (69)维里系数 virial coefficient (69)稳流过程 steady flow process (69)稳态近似法 stationary state approximation (69)无热溶液athermal solution (70)无限稀溶液 solutions in the limit of extreme dilution (70)物理化学 Physical Chemistry (70)物理吸附 physisorptions (70)吸附 adsorption (70)吸附等量线 adsorption isostere (70)吸附等温线 adsorption isotherm (70)吸附等压线 adsorption isobar (70)吸附剂 adsorbent (70)吸附量 extent of adsorption (70)吸附热 heat of adsorption (70)吸附质 adsorbate (70)析出电势 evolution or deposition potential (71)稀溶液的依数性 colligative properties of dilute solutions (71)稀释焓 dilution enthalpy (71)系统 system (71)系统点 system point (71)系统的环境 environment of system (71)相 phase (71)相变 phase change (71)相变焓 enthalpy of phase change (71)相变化 phase change (71)相变热 heat of phase change (71)相点 phase point (71)相对挥发度relative volatility (72)相对粘度 relative viscosity (72)相律 phase rule (72)相平衡热容heat capacity in phase equilibrium (72)相图 phase diagram (72)相倚子系统 system of dependent particles (72)悬浮液 suspension (72)循环过程 cyclic process (72)压力商 pressure quotient (72)压缩因子 compressibility factor (73)压缩因子图 diagram of compressibility factor (73)亚稳状态 metastable state (73)盐桥 salt bridge (73)盐析 salting out (73)阳极 anode (73)杨氏方程 Young’s equation (73)液体接界电势 liquid junction potential (73)液相线 liquid phase lines (73)一级反应first order reaction (73)一级相变first order phase change (74)依时计量学反应 time dependent stoichiometric reactions (74)逸度 fugacity (74)逸度系数 coefficient of fugacity (74)阴极 cathode (75)荧光 fluorescence (75)永动机 perpetual motion machine (75)永久气体 Permanent gas (75)有效能 available energy (75)原电池 primary cell (75)原盐效应 salt effect (75)增比粘度 specific viscosity (75)憎液溶胶 lyophobic sol (75)沾湿 adhesional wetting (75)沾湿功 the work of adhesional wetting (75)真溶液 true solution (76)真实电解质real electrolyte (76)真实气体 real gas (76)真实迁移数true transference number (76)振动配分函数 partition function of vibration (76)振动特征温度 characteristic temperature of vibration (76)蒸气压下降 depression of vapor pressure (76)正常沸点 normal point (76)正吸附 positive adsorption (76)支链反应 branched chain reactions (76)直链反应 straight chain reactions (77)指前因子 pre-exponential factor (77)质量作用定律mass action law (77)制冷系数coefficient of refrigeration (77)中和热heat of neutralization (77)轴功 shaft work (77)转动配分函数 partition function of rotation (77)转动特征温度 characteristic temperature of vibration (78)转化率 convert ratio (78)转化温度conversion temperature (78)状态 state (78)状态方程 state equation (78)状态分布 state distribution (78)状态函数 state function (78)准静态过程quasi-static process (78)准一级反应 pseudo first order reaction (78)自动催化作用 auto-catalysis (78)自由度 degree of freedom (78)自由度数 number of degree of freedom (79)自由焓free enthalpy (79)自由能free energy (79)自由膨胀free expansion (79)组分数 component number (79)最低恒沸点 lower azeotropic point (79)最高恒沸点 upper azeotropic point (79)最佳反应温度 optimal reaction temperature (79)最可几分布 most probable distribution (80)最可几速率 most propable speed (80)概念及术语BET公式BET formula1938年布鲁瑙尔(Brunauer)、埃米特(Emmett)和特勒(Teller)三人在兰格缪尔单分子层吸附理论的基础上提出多分子层吸附理论。
统计学常用英语词汇

统计学常用英语词汇Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additive Noise, 加性噪声Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alpha factoring,α因子法Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis Of Effects, 效应分析Analysis Of Variance, 方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型ANOVA table and eta, 分组计算方差分析Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area 区域图Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
品质英语

品质英语A-DAbsolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法E-LEffect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量。
AnInquiryintoHis...

Cliodynamics: the Journal of Theoretical and Mathematical HistoryCorresponding author’s e-mail: ********************Citation: Krakauer, David C., John Gaddis, and Kenneth Pomeranz. 2011. Editors’Column: An Inquiry into History, Big History and Metahistory. Cliodynamics 2: 1–5.Editors’ Column: An Inquiry into History, Big History, and MetahistoryDavid C. Krakauer Santa Fe InstituteJohn Gaddis Yale UniversityKenneth PomeranzUniversity of California at IrvineWhat is history anyway? Most people would say it’s what happened in the past, but how far back does the past extend? To the first written sources? To what other forms of evidence reveal about pre-literate civilizations? What does that term mean – an empire, a nation, a city, a village, a family, a lonely hermit somewhere? Why stop with people: shouldn’t history also comprise the environment in which they exist, and if so on what scale and how far back? And as long as we’re headed in that direction, why stop with the earth and the solar system? Why not go all the way back to the Big Bang itself? There’s obviously no consensus on how to answer these questions, but even asking them raises another set of questions about history: who should be doing it? Traditionally trained historians, for whom archives are the only significant source? Historians willing to go beyond archives, who must therefore rely on, and to some extent themselves become, psychologists, sociologists, anthropologists, archeologists? But if they’re also going to take environments into account, don’t they also have to know something about climatology, biology, paleontology, geology, and even astronomy? And how can they do that without knowing some basic physics, chemistry, and mathematics?You see where this is going: history, by this capacious definition, includes everything that has happened up until the present moment – and because the present moment has already become the past by the time you’ve finished reading this sentence, history must also provide a basis (what other one could there be?) for anticipating the future.What is to prevent history, then, from being the study of “life, the universe, and everything,” as the late Douglas Adams proposed in his The Hitchhiker’s Guide to the Galaxy ? Nothing in principle, but there is a problem in practice, which is that no one person, or academic department, or professional discipline, or method of inquiry, can do it all. Students of this kind of Very BigKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 12History have for very good reasons divided themselves into fields, sub-fields, and even micro-fields, knowing that things rarely get simpler the more closely you look at them.Much good has come of this. Our knowledge of this capaciously defined past has expanded exponentially over the past several hundred years. We now have a much clearer sense of who we are and where we came from than was available, say, to Copernicus, when he first ventured the suggestion that the universe did not revolve around us.Some bad has come of this process as well, however. For if the volume of information in relation to time looks like a hockey stick as it approaches our era, rapidly accelerating in the production of contemporary knowledge –then it is a laminated hockey stick, the parts of which define a trajectory without interacting with one another. How much do we really know, therefore, about where we came from, who we are – and where we may be going – if the disciplines we’ve divided ourselves into have lost the languages that would allow them to speak to anyone apart from themselves?Moreover, it seems likely that the disciplines themselves develop less than optimally when they lack ready access to each other’s insights and methods. Indeed it seems likely that history suffers most of all from such segmentation. At least to some extent history, more than the study of literature, or economics, or political science (though perhaps not much more than anthropology or sociology) aims to integrate the understanding of how human social arrangements, technologies, interactions with the larger biosphere, intellectual creations, and even our habitual cognitive and emotional responses to the world around us have changed over a given period of time: no matter what s/he emphasizes as a researcher, the person who teaches a history of 19th century England knows it cannot omit dramatic changes in birth and death rates, the expansion of suffrage, the publication of The Origin of Species , the expansion of overseas possessions, or the environmental consequences of industrialization. So despite what has sometimes seemed a strong allergy to “theory” (of various sorts) in history departments, historians may have the most to gain by opening more lines of communication to people studying change over time in various phenomena and on various time-scales. These papers have grown out of a series of conversations and meetings, sponsored by the Santa Fe Institute, on how we might recover such languages. It proceeds from the proposition that if generalization is necessary within particular disciplines – how could it not be? – then it should also be useful across all the disciplines that take, as the subject of their inquiries, Very Big History. It pursues the possibility of taking what one of our contributors, Murray Gell-Mann, has called “a crude look at the whole.” It explores the possibility that the sciences of complexity and its many tributary fields and concepts pioneered at Santa Fe, may provide new methods, or minimallyKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 13metaphors, by which to do this. It is premised on the notion that curiosity – the foundation of all knowledge – requires the ability to be both a specialist and a generalist at the same time. And that this simultaneity of perspective is in need of new trans-disciplinary approaches and ideas. Our title History, Big History and Metahistory , requires a brief explanation. By “history,” we mean the study, chiefly, of written records, extending from the most ancient cuneiform tablet through the most recent e-mails and twitters. By “big history,” we mean all reconstructions of the past that do not rely on written materials. By “metahistory,” we mean the patterns that emerge from both modes of inquiry that make generalization, and hence analysis, possible. We do not mean to imply by this sequence of terms that moving to the method and scale of “big history” is the only way to search for meaningful patterns. We are, however, confident that juxtaposing types of inquiry developed to deal with change in literate societies and those developed to deal with a much longer record of change has proved to be one very useful way of exposing important, often neglected questions, both about what it makes sense to look for in the always incomplete records of the past and about how to do the looking. As in any good discussion, our contributors do not all agree with one another. Some insist that there are unifying principles, or laws, to which both human and biological history are subject. Others seek ideas, tools, and perhaps standards of truth from dynamical systems, evolution, and statistics that could augment traditional approaches to history, but do not necessarily see such borrowings as requiring that history and big history become a single discipline. One contributor sees any attempt at unification in the humanities as dangerous, and citing as precedents the extent to which social Darwinism was used to abuse less powerful people and societies. All do share the view, however, that history is too important – and too encompassing – to be analyzed exclusively through the methods of qualitative text-based narratives. We have arranged our contributors alphabetically, for no better reason than to shuffle their ideas and to avoid enforcing on this journal’s readers the editor’s conclusions.We start with David Christian who discusses the chronometric revolution, and how this has lead to a single historical continuum stretching all the way back to the big bang, allowing for what he calls, Grand Unified Stories.Douglas Erwin explores how paleontologists deal with the vagaries of preservation, and how statistical techniques developed in biology, have been applied to textual evidence, and the complexities of non-uniform trends leading to convergent and parallel events. John Gaddis shows that several 19th century searches for a science of history – those of Leo Tolstoy, Carl von Clausewitz, and Henry Adams –Krakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 14grasped key concepts of complexity theory, but lacked the means of visualizing and verifying it that are available today. Murray Gell-Mann discusses the nature of empirical regularities, and their relationship to measures of complexity. Gell-Mann illustrates how apparently complex histories and patterns can sometimes be organized using simple models of growth and scaling. Geoffrey Harpham discusses the possible limitations and abuses of unified frameworks of explanation, using the history of philology as a case study. Unchecked, scientific trajectories in a social matrix can lead to unjustified inferences. David Krakauer introduces a range of concepts from non-linear dynamics, statistical physics and evolutionary biology, that he argues should be of use to all students of history. Using examples from traditional historicism, Krakauer shows how history often uses analogs of concepts and tools expressed quantitatively in the natural sciences. John McNeill explores parallels between cultural and biological evolution, exploring patterns of increasing cultural heterogeneity through time, and the role that specialist (pandas) and generalist (pigs) societies and states have played in explaining these patterns.Ken Pomeranz describes the ways in which naming historical phenomena influences how we then analyze them. Arguing that many of the classification schemes that are conventional among historians serve some other purposes well, but are not very conducive to seeking meaningful generalizations or engaging in dialogue with scientists, he suggests other approaches, while also giving reasons why they are far more likely to complement than displace currently popular taxonomies. Fred Spier, speaking as an historian, explores how big history might be brought within a reductive framework of physics, using the concept of free energy rate density, as a means of organizing major transitions, from the abiotic to the biotic and cultural domains. Peter Turchin explores the value of general quantitative theory in areas where prediction is limited, and comparative data and retrodiction need to be explored. The transformation of natural history into quantitative biology is used as possible precedent and model for a transformation of qualitative history.Geerat Vermeij considers a grand, economic theory of history, in which biology and culture might both be subsumed. Concepts of competition, feedback and power provide potential unifying historical concepts.Geoffrey West argues for quantitative approaches to history through a suitable choice of coarse-grained variables. West argues that is unlikely that we shall discern common patterns at the level of individuals, but if we allowKrakauer et al: Editors’ Column. Cliodynamics (2011) Vol. 2, Iss. 15 ourselves to study collective phenomena, such as urban systems, then we might make surprising new discoveries. No reader is likely to find all of these contributions persuasive, or perhaps even congenial. Nonetheless, we think that most will gain more from engaging with them in their current diversity than they would gain from any superficial consensus we could wring from them. Readers may think of some papers as introducing them to new tools, potentially useful for their current inquiries or for others they had previously deemed impossible. Others stand as arguments about what sorts of inquiries should be attempted; still others as preliminary reports from lines of inquiry (in various historical disciplines) that it would be good for a wider range of scholars to know about. Each of these, of course, bears on the others, at least indirectly: what we should ask, what tools we have for answering new and old questions, and what people have found by asking unusual questions or using unusual tools are obviously overlapping issues. The overlaps on display here are not nearly large enough to let us suggest a single, unified agenda for further work; they are, however, sufficiently numerous to suggest many places where more focused inter-disciplinary projects might take root and prove fruitful. Perhaps even more important, these efforts should give readers what the meetings they sprang from gave to its participants: a better sense of the range of conversations we might join, the opportunities and problems in those discussions, and some ways in which joining new conversations will give us new ways of analyzing our common past.。
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Felix Otto
models considered were of gradient-flow type and thus endowed with a variational interpretation: steepest descent in a multiscale energy landscape. The examples are • The branching of domains in uniaxial ferromagnets [1] (with R. Choksi and R. V. Kohn). Strongly uniaxial ferromagnets have only two favored magnetization directions (“up” and “down”). The width of the corresponding domains decreases towards a sample surface perpendicular to the favored axis. We rigorously establish the scaling of the energy in the sample dimensions in support of this behavior. To leading order, the micromagnetic model behaves like a three-dimensional analogue of the Kohn-M¨ uller [10] model for twin branching. • The period of cross-tie walls in ferromagnetic films [2] (with A. DeSimone, R. V. Kohn and S. M¨ uller). Cross-tie walls are transition layers between domains in ferromagnetic films. They display a periodic structure in the tangential direction. The experimentally observed scaling of the period in the material parameters is not well-understood [9]. In this paper, we present a combination of heuristic and rigorous analysis which reproduces the experimental scaling and thus identifies the relevant mechanism. • The rate of capillarity-driven spreading of a thin droplet [6] (with L. Giacomelli). Here, the starting point is the lubrication approximation. The scale invariant version of the model is ill-posed and has to be regularized near the contact line, e. g. through allowing finite slippage. In this paper, we rigorously derive a scaling law for the spreading of the droplet in an intermediate time regime. This scaling law depends only logarithmically on the length scale introduce agreement with a conjecture of de Gennes [5]. • The rate of coarsening in spinodal decomposition [11] (with R. V. Kohn). Spinodal decomposition is usually modelled by a Cahn-Hilliard equation. In the later stages, it is experimentally observed that the phase distribution coarsens in a statistically self-similar fashion. In this paper, we rigorously prove upper bounds for this coarsening process. The exponents are the ones heuristically expected and depend on whether the mobility is degenerate or non-degenerate: t1/4 resp. t1/3 . In [3], we predict a cross-over for almost degenerate mobility due to a change in the coarsening mechanism. • The first-order correction to the Lifshitz-Slyozov-Wagner theory for Ostwald ripening [7] (with A. H¨ onig and B. Niethammer). Ostwald ripening describes the late stage of spinodal decomposition in an off-critical mixture (volume fraction of one phase φ ≪ 1). The minority phase then consists of several particles immersed in a matrix of the majority phase. The particles are approximately spherical and don’t move—the Lifshitz–Slyozov—Wagner theory describes the evolution of the radii distribution. There is a major interest in identifying the next-order correction term in φ. We rigorously show that there is a cross-over in the correction term from φ1/3 to φ1/2 depending on the system size. Our method to rigorously analyze these scaling laws in a multiscale model is based on relating integral quantities (energies, average length scales, dissipation rates...). It is different from the more local method of matched asymptotic expan-
ICM 2002 · Vol. III · 1–3
Cross-over in Scaling Laws: A Simple Example from Micromagnetics
arXiv:math-ph/0305001v1 1 May 2003
Felix Otto∗
Abstract Scaling laws for characteristic length scales (in time or in the model parameters) are both experimentally robust and accessible for rigorous analysis. In multiscale situations cross–overs between different scaling laws are observed. We give a simple example from micromagnetics. In soft ferromagnetic films, the geometric character of a wall separating two magnetic domains depends on the film thickness. We identify this transition from a N´ eel wall to an Asymmetric Bloch wall by rigorously establishing a cross–over in the specific wall energy.
1. Introduction
Many continuum systems in materials science display pattern formation. These patterns are characterized by one or several length scales. The scaling of these characteristic lengths in the material parameters and/or in time are usually an experimentally robust feature. These scaling laws, and their characterizing exponents, are of interest to theoretical physics since they express a certain universality. At the same time, scaling laws (rather than more detailed features) are ameanable to heuristic and rigorous analysis and thus are a good test for the model and a challenge for mathematics. Scaling laws and their exponents reflect a scale invariance. In a multiscale model, these scale invariances are broken and only approximately valid in certain parameter and/or time regimes. The cross-over between two scaling laws reflects a change in the dominant physical mechanisms. In studying cross-overs, theoretical analysis may have an advantage over numerical simulation which has to explore many parameter decades and thus has to cope with widely separated length scales. Together with various collaborators, the author has analyzed scaling laws and their cross-overs in both static (variational) and dynamic models. The dynamic