Differences between regular and random order of updates in damage spreading simulations

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Collective dynamics of 'small-world' networks

Nature © Macmillan Publishers Ltd 19988typically slower than ϳ1km s −1)might differ signiﬁcantly from what is assumed by current modelling efforts 27.The expected equation-of-state differences among small bodies (ice versus rock,for instance)presents another dimension of study;having recently adapted our code for massively parallel architectures (K.M.Olson and E.A,manuscript in preparation),we are now ready to perform a more comprehensive analysis.The exploratory simulations presented here suggest that when a young,non-porous asteroid (if such exist)suffers extensive impact damage,the resulting fracture pattern largely deﬁnes the asteroid’s response to future impacts.The stochastic nature of collisions implies that small asteroid interiors may be as diverse as their shapes and spin states.Detailed numerical simulations of impacts,using accurate shape models and rheologies,could shed light on how asteroid collisional response depends on internal conﬁguration and shape,and hence on how planetesimals evolve.Detailed simulations are also required before one can predict the quantitative effects of nuclear explosions on Earth-crossing comets and asteroids,either for hazard mitigation 28through disruption and deﬂection,or for resource exploitation 29.Such predictions would require detailed reconnaissance concerning the composition andinternal structure of the targeted object.ⅪReceived 4February;accepted 18March 1998.1.Asphaug,E.&Melosh,H.J.The Stickney impact of Phobos:A dynamical model.Icarus 101,144–164(1993).2.Asphaug,E.et al .Mechanical and geological effects of impact cratering on Ida.Icarus 120,158–184(1996).3.Nolan,M.C.,Asphaug,E.,Melosh,H.J.&Greenberg,R.Impact craters on asteroids:Does strength orgravity control their size?Icarus 124,359–371(1996).4.Love,S.J.&Ahrens,T.J.Catastrophic impacts on gravity dominated asteroids.Icarus 124,141–155(1996).5.Melosh,H.J.&Ryan,E.V.Asteroids:Shattered but not dispersed.Icarus 129,562–564(1997).6.Housen,K.R.,Schmidt,R.M.&Holsapple,K.A.Crater ejecta scaling laws:Fundamental forms basedon dimensional analysis.J.Geophys.Res.88,2485–2499(1983).7.Holsapple,K.A.&Schmidt,R.M.Point source solutions and coupling parameters in crateringmechanics.J.Geophys.Res.92,6350–6376(1987).8.Housen,K.R.&Holsapple,K.A.On the fragmentation of asteroids and planetary satellites.Icarus 84,226–253(1990).9.Benz,W.&Asphaug,E.Simulations of brittle solids using smooth particle put.mun.87,253–265(1995).10.Asphaug,E.et al .Mechanical and geological effects of impact cratering on Ida.Icarus 120,158–184(1996).11.Hudson,R.S.&Ostro,S.J.Shape of asteroid 4769Castalia (1989PB)from inversion of radar images.Science 263,940–943(1994).12.Ostro,S.J.et al .Asteroid radar astrometry.Astron.J.102,1490–1502(1991).13.Ahrens,T.J.&O’Keefe,J.D.in Impact and Explosion Cratering (eds Roddy,D.J.,Pepin,R.O.&Merrill,R.B.)639–656(Pergamon,New York,1977).14.Tillotson,J.H.Metallic equations of state for hypervelocity impact.(General Atomic Report GA-3216,San Diego,1962).15.Nakamura,A.&Fujiwara,A.Velocity distribution of fragments formed in a simulated collisionaldisruption.Icarus 92,132–146(1991).16.Benz,W.&Asphaug,E.Simulations of brittle solids using smooth particle put.mun.87,253–265(1995).17.Bottke,W.F.,Nolan,M.C.,Greenberg,R.&Kolvoord,R.A.Velocity distributions among collidingasteroids.Icarus 107,255–268(1994).18.Belton,M.J.S.et al .Galileo encounter with 951Gaspra—First pictures of an asteroid.Science 257,1647–1652(1992).19.Belton,M.J.S.et al .Galileo’s encounter with 243Ida:An overview of the imaging experiment.Icarus120,1–19(1996).20.Asphaug,E.&Melosh,H.J.The Stickney impact of Phobos:A dynamical model.Icarus 101,144–164(1993).21.Asphaug,E.et al .Mechanical and geological effects of impact cratering on Ida.Icarus 120,158–184(1996).22.Housen,K.R.,Schmidt,R.M.&Holsapple,K.A.Crater ejecta scaling laws:Fundamental forms basedon dimensional analysis.J.Geophys.Res.88,2485–2499(1983).23.Veverka,J.et al .NEAR’s ﬂyby of 253Mathilde:Images of a C asteroid.Science 278,2109–2112(1997).24.Asphaug,E.et al .Impact evolution of icy regoliths.Lunar Planet.Sci.Conf.(Abstr.)XXVIII,63–64(1997).25.Love,S.G.,Ho¨rz,F.&Brownlee,D.E.Target porosity effects in impact cratering and collisional disruption.Icarus 105,216–224(1993).26.Fujiwara,A.,Cerroni,P .,Davis,D.R.,Ryan,E.V.&DiMartino,M.in Asteroids II (eds Binzel,R.P .,Gehrels,T.&Matthews,A.S.)240–265(Univ.Arizona Press,Tucson,1989).27.Davis,D.R.&Farinella,P.Collisional evolution of Edgeworth-Kuiper Belt objects.Icarus 125,50–60(1997).28.Ahrens,T.J.&Harris,A.W.Deﬂection and fragmentation of near-Earth asteroids.Nature 360,429–433(1992).29.Resources of Near-Earth Space (eds Lewis,J.S.,Matthews,M.S.&Guerrieri,M.L.)(Univ.ArizonaPress,Tucson,1993).Acknowledgements.This work was supported by NASA’s Planetary Geology and Geophysics Program.Correspondence and requests for materials should be addressed to E.A.(e-mail:asphaug@).letters to nature440NATURE |VOL 393|4JUNE 1998Collective dynamics of ‘small-world’networksDuncan J.Watts *&Steven H.StrogatzDepartment of Theoretical and Applied Mechanics,Kimball Hall,Cornell University,Ithaca,New York 14853,USA.........................................................................................................................Networks of coupled dynamical systems have been used to model biological oscillators 1–4,Josephson junction arrays 5,6,excitable media 7,neural networks 8–10,spatial games 11,genetic control networks 12and many other self-organizing systems.Ordinarily,the connection topology is assumed to be either completely regular or completely random.But many biological,technological and social networks lie somewhere between these two extremes.Here we explore simple models of networks that can be tuned through this middle ground:regular networks ‘rewired’to intro-duce increasing amounts of disorder.We ﬁnd that these systems can be highly clustered,like regular lattices,yet have small characteristic path lengths,like random graphs.We call them ‘small-world’networks,by analogy with the small-world phenomenon 13,14(popularly known as six degrees of separation 15).The neural network of the worm Caenorhabditis elegans ,the power grid of the western United States,and the collaboration graph of ﬁlm actors are shown to be small-world networks.Models of dynamical systems with small-world coupling display enhanced signal-propagation speed,computational power,and synchronizability.In particular,infectious diseases spread more easily in small-world networks than in regular lattices.To interpolate between regular and random networks,we con-sider the following random rewiring procedure (Fig.1).Starting from a ring lattice with n vertices and k edges per vertex,we rewire each edge at random with probability p .This construction allows us to ‘tune’the graph between regularity (p ¼0)and disorder (p ¼1),and thereby to probe the intermediate region 0Ͻp Ͻ1,about which little is known.We quantify the structural properties of these graphs by their characteristic path length L (p )and clustering coefﬁcient C (p ),as deﬁned in Fig.2legend.Here L (p )measures the typical separation between two vertices in the graph (a global property),whereas C (p )measures the cliquishness of a typical neighbourhood (a local property).The networks of interest to us have many vertices with sparse connections,but not so sparse that the graph is in danger of becoming disconnected.Speciﬁcally,we require n q k q ln ðn Þq 1,where k q ln ðn Þguarantees that a random graph will be connected 16.In this regime,we ﬁnd that L ϳn =2k q 1and C ϳ3=4as p →0,while L ϷL random ϳln ðn Þ=ln ðk Þand C ϷC random ϳk =n p 1as p →1.Thus the regular lattice at p ¼0is a highly clustered,large world where L grows linearly with n ,whereas the random network at p ¼1is a poorly clustered,small world where L grows only logarithmically with n .These limiting cases might lead one to suspect that large C is always associated with large L ,and small C with small L .On the contrary,Fig.2reveals that there is a broad interval of p over which L (p )is almost as small as L random yet C ðp Þq C random .These small-world networks result from the immediate drop in L (p )caused by the introduction of a few long-range edges.Such ‘short cuts’connect vertices that would otherwise be much farther apart than L random .For small p ,each short cut has a highly nonlinear effect on L ,contracting the distance not just between the pair of vertices that it connects,but between their immediate neighbourhoods,neighbourhoods of neighbourhoods and so on.By contrast,an edge*Present address:Paul zarsfeld Center for the Social Sciences,Columbia University,812SIPA Building,420W118St,New York,New York 10027,USA.Nature © Macmillan Publishers Ltd 19988letters to natureNATURE |VOL 393|4JUNE 1998441removed from a clustered neighbourhood to make a short cut has,at most,a linear effect on C ;hence C (p )remains practically unchanged for small p even though L (p )drops rapidly.The important implica-tion here is that at the local level (as reﬂected by C (p )),the transition to a small world is almost undetectable.To check the robustness of these results,we have tested many different types of initial regular graphs,as well as different algorithms for random rewiring,and all give qualitatively similar results.The only requirement is that the rewired edges must typically connect vertices that would otherwise be much farther apart than L random .The idealized construction above reveals the key role of short cuts.It suggests that the small-world phenomenon might be common in sparse networks with many vertices,as even a tiny fraction of short cuts would sufﬁce.To test this idea,we have computed L and C for the collaboration graph of actors in feature ﬁlms (generated from data available at ),the electrical power grid of the western United States,and the neural network of the nematode worm C.elegans 17.All three graphs are of scientiﬁc interest.The graph of ﬁlm actors is a surrogate for a social network 18,with the advantage of being much more easily speciﬁed.It is also akin to the graph of mathematical collaborations centred,traditionally,on P.Erdo¨s (partial data available at /ϳgrossman/erdoshp.html).The graph of the power grid is relevant to the efﬁciency and robustness of power networks 19.And C.elegans is the sole example of a completely mapped neural network.Table 1shows that all three graphs are small-world networks.These examples were not hand-picked;they were chosen because of their inherent interest and because complete wiring diagrams were available.Thus the small-world phenomenon is not merely a curiosity of social networks 13,14nor an artefact of an idealizedmodel—it is probably generic for many large,sparse networks found in nature.We now investigate the functional signiﬁcance of small-world connectivity for dynamical systems.Our test case is a deliberately simpliﬁed model for the spread of an infectious disease.The population structure is modelled by the family of graphs described in Fig.1.At time t ¼0,a single infective individual is introduced into an otherwise healthy population.Infective individuals are removed permanently (by immunity or death)after a period of sickness that lasts one unit of dimensionless time.During this time,each infective individual can infect each of its healthy neighbours with probability r .On subsequent time steps,the disease spreads along the edges of the graph until it either infects the entire population,or it dies out,having infected some fraction of the population in theprocess.p = 0p = 1Regular Small-worldRandomFigure 1Random rewiring procedure for interpolating between a regular ring lattice and a random network,without altering the number of vertices or edges in the graph.We start with a ring of n vertices,each connected to its k nearest neighbours by undirected edges.(For clarity,n ¼20and k ¼4in the schematic examples shown here,but much larger n and k are used in the rest of this Letter.)We choose a vertex and the edge that connects it to its nearest neighbour in a clockwise sense.With probability p ,we reconnect this edge to a vertex chosen uniformly at random over the entire ring,with duplicate edges forbidden;other-wise we leave the edge in place.We repeat this process by moving clockwise around the ring,considering each vertex in turn until one lap is completed.Next,we consider the edges that connect vertices to their second-nearest neighbours clockwise.As before,we randomly rewire each of these edges with probability p ,and continue this process,circulating around the ring and proceeding outward to more distant neighbours after each lap,until each edge in the original lattice has been considered once.(As there are nk /2edges in the entire graph,the rewiring process stops after k /2laps.)Three realizations of this process are shown,for different values of p .For p ¼0,the original ring is unchanged;as p increases,the graph becomes increasingly disordered until for p ¼1,all edges are rewired randomly.One of our main results is that for intermediate values of p ,the graph is a small-world network:highly clustered like a regular graph,yet with small characteristic path length,like a random graph.(See Fig.2.)T able 1Empirical examples of small-world networksL actual L random C actual C random.............................................................................................................................................................................Film actors 3.65 2.990.790.00027Power grid 18.712.40.0800.005C.elegans 2.65 2.250.280.05.............................................................................................................................................................................Characteristic path length L and clustering coefﬁcient C for three real networks,compared to random graphs with the same number of vertices (n )and average number of edges per vertex (k ).(Actors:n ¼225;226,k ¼61.Power grid:n ¼4;941,k ¼2:67.C.elegans :n ¼282,k ¼14.)The graphs are deﬁned as follows.Two actors are joined by an edge if they have acted in a ﬁlm together.We restrict attention to the giant connected component 16of this graph,which includes ϳ90%of all actors listed in the Internet Movie Database (available at ),as of April 1997.For the power grid,vertices represent generators,transformers and substations,and edges represent high-voltage transmission lines between them.For C.elegans ,an edge joins two neurons if they are connected by either a synapse or a gap junction.We treat all edges as undirected and unweighted,and all vertices as identical,recognizing that these are crude approximations.All three networks show the small-world phenomenon:L ՌL random but C q C random.00.20.40.60.810.00010.0010.010.11pFigure 2Characteristic path length L (p )and clustering coefﬁcient C (p )for the family of randomly rewired graphs described in Fig.1.Here L is deﬁned as the number of edges in the shortest path between two vertices,averaged over all pairs of vertices.The clustering coefﬁcient C (p )is deﬁned as follows.Suppose that a vertex v has k v neighbours;then at most k v ðk v Ϫ1Þ=2edges can exist between them (this occurs when every neighbour of v is connected to every other neighbour of v ).Let C v denote the fraction of these allowable edges that actually exist.Deﬁne C as the average of C v over all v .For friendship networks,these statistics have intuitive meanings:L is the average number of friendships in the shortest chain connecting two people;C v reﬂects the extent to which friends of v are also friends of each other;and thus C measures the cliquishness of a typical friendship circle.The data shown in the ﬁgure are averages over 20random realizations of the rewiring process described in Fig.1,and have been normalized by the values L (0),C (0)for a regular lattice.All the graphs have n ¼1;000vertices and an average degree of k ¼10edges per vertex.We note that a logarithmic horizontal scale has been used to resolve the rapid drop in L (p ),corresponding to the onset of the small-world phenomenon.During this drop,C (p )remains almost constant at its value for the regular lattice,indicating that the transition to a small world is almost undetectable at the local level.Nature © Macmillan Publishers Ltd 19988letters to nature442NATURE |VOL 393|4JUNE 1998Two results emerge.First,the critical infectiousness r half ,at which the disease infects half the population,decreases rapidly for small p (Fig.3a).Second,for a disease that is sufﬁciently infectious to infect the entire population regardless of its structure,the time T (p )required for global infection resembles the L (p )curve (Fig.3b).Thus,infectious diseases are predicted to spread much more easily and quickly in a small world;the alarming and less obvious point is how few short cuts are needed to make the world small.Our model differs in some signiﬁcant ways from other network models of disease spreading 20–24.All the models indicate that net-work structure inﬂuences the speed and extent of disease transmis-sion,but our model illuminates the dynamics as an explicit function of structure (Fig.3),rather than for a few particular topologies,such as random graphs,stars and chains 20–23.In the work closest to ours,Kretschmar and Morris 24have shown that increases in the number of concurrent partnerships can signiﬁcantly accelerate the propaga-tion of a sexually-transmitted disease that spreads along the edges of a graph.All their graphs are disconnected because they ﬁx the average number of partners per person at k ¼1.An increase in the number of concurrent partnerships causes faster spreading by increasing the number of vertices in the graph’s largest connected component.In contrast,all our graphs are connected;hence the predicted changes in the spreading dynamics are due to more subtle structural features than changes in connectedness.Moreover,changes in the number of concurrent partners are obvious to an individual,whereas transitions leading to a smaller world are not.We have also examined the effect of small-world connectivity on three other dynamical systems.In each case,the elements were coupled according to the family of graphs described in Fig.1.(1)For cellular automata charged with the computational task of density classiﬁcation 25,we ﬁnd that a simple ‘majority-rule’running on a small-world graph can outperform all known human and genetic algorithm-generated rules running on a ring lattice.(2)For the iterated,multi-player ‘Prisoner’s dilemma’11played on a graph,we ﬁnd that as the fraction of short cuts increases,cooperation is less likely to emerge in a population of players using a generalized ‘tit-for-tat’26strategy.The likelihood of cooperative strategies evolving out of an initial cooperative/non-cooperative mix also decreases with increasing p .(3)Small-world networks of coupled phase oscillators synchronize almost as readily as in the mean-ﬁeld model 2,despite having orders of magnitude fewer edges.This result may be relevant to the observed synchronization of widely separated neurons in the visual cortex 27if,as seems plausible,the brain has a small-world architecture.We hope that our work will stimulate further studies of small-world networks.Their distinctive combination of high clustering with short characteristic path length cannot be captured by traditional approximations such as those based on regular lattices or random graphs.Although small-world architecture has not received much attention,we suggest that it will probably turn out to be widespread in biological,social and man-made systems,oftenwith important dynamical consequences.ⅪReceived 27November 1997;accepted 6April 1998.1.Winfree,A.T.The Geometry of Biological Time (Springer,New Y ork,1980).2.Kuramoto,Y.Chemical Oscillations,Waves,and Turbulence (Springer,Berlin,1984).3.Strogatz,S.H.&Stewart,I.Coupled oscillators and biological synchronization.Sci.Am.269(6),102–109(1993).4.Bressloff,P .C.,Coombes,S.&De Souza,B.Dynamics of a ring of pulse-coupled oscillators:a group theoretic approach.Phys.Rev.Lett.79,2791–2794(1997).5.Braiman,Y.,Lindner,J.F.&Ditto,W.L.Taming spatiotemporal chaos with disorder.Nature 378,465–467(1995).6.Wiesenfeld,K.New results on frequency-locking dynamics of disordered Josephson arrays.Physica B 222,315–319(1996).7.Gerhardt,M.,Schuster,H.&Tyson,J.J.A cellular automaton model of excitable media including curvature and dispersion.Science 247,1563–1566(1990).8.Collins,J.J.,Chow,C.C.&Imhoff,T.T.Stochastic resonance without tuning.Nature 376,236–238(1995).9.Hopﬁeld,J.J.&Herz,A.V.M.Rapid local synchronization of action potentials:Toward computation with coupled integrate-and-ﬁre neurons.Proc.Natl A 92,6655–6662(1995).10.Abbott,L.F.&van Vreeswijk,C.Asynchronous states in neural networks of pulse-coupled oscillators.Phys.Rev.E 48(2),1483–1490(1993).11.Nowak,M.A.&May,R.M.Evolutionary games and spatial chaos.Nature 359,826–829(1992).12.Kauffman,S.A.Metabolic stability and epigenesis in randomly constructed genetic nets.J.Theor.Biol.22,437–467(1969).gram,S.The small world problem.Psychol.Today 2,60–67(1967).14.Kochen,M.(ed.)The Small World (Ablex,Norwood,NJ,1989).15.Guare,J.Six Degrees of Separation:A Play (Vintage Books,New Y ork,1990).16.Bollaba´s,B.Random Graphs (Academic,London,1985).17.Achacoso,T.B.&Yamamoto,W.S.AY’s Neuroanatomy of C.elegans for Computation (CRC Press,BocaRaton,FL,1992).18.Wasserman,S.&Faust,K.Social Network Analysis:Methods and Applications (Cambridge Univ.Press,1994).19.Phadke,A.G.&Thorp,puter Relaying for Power Systems (Wiley,New Y ork,1988).20.Sattenspiel,L.&Simon,C.P .The spread and persistence of infectious diseases in structured populations.Math.Biosci.90,341–366(1988).21.Longini,I.M.Jr A mathematical model for predicting the geographic spread of new infectious agents.Math.Biosci.90,367–383(1988).22.Hess,G.Disease in metapopulation models:implications for conservation.Ecology 77,1617–1632(1996).23.Blythe,S.P .,Castillo-Chavez,C.&Palmer,J.S.T oward a uniﬁed theory of sexual mixing and pair formation.Math.Biosci.107,379–405(1991).24.Kretschmar,M.&Morris,M.Measures of concurrency in networks and the spread of infectious disease.Math.Biosci.133,165–195(1996).25.Das,R.,Mitchell,M.&Crutchﬁeld,J.P .in Parallel Problem Solving from Nature (eds Davido,Y.,Schwefel,H.-P.&Ma¨nner,R.)344–353(Lecture Notes in Computer Science 866,Springer,Berlin,1994).26.Axelrod,R.The Evolution of Cooperation (Basic Books,New Y ork,1984).27.Gray,C.M.,Ko¨nig,P .,Engel,A.K.&Singer,W.Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reﬂects global stimulus properties.Nature 338,334–337(1989).Acknowledgements.We thank B.Tjaden for providing the ﬁlm actor data,and J.Thorp and K.Bae for the Western States Power Grid data.This work was supported by the US National Science Foundation (Division of Mathematical Sciences).Correspondence and requests for materials should be addressed to D.J.W.(e-mail:djw24@).0.150.20.250.30.350.00010.0010.010.11rhalfpaFigure 3Simulation results for a simple model of disease spreading.The community structure is given by one realization of the family of randomly rewired graphs used in Fig.1.a ,Critical infectiousness r half ,at which the disease infects half the population,decreases with p .b ,The time T (p )required for a maximally infectious disease (r ¼1)to spread throughout the entire population has essen-tially the same functional form as the characteristic path length L (p ).Even if only a few per cent of the edges in the original lattice are randomly rewired,the time to global infection is nearly as short as for a random graph.0.20.40.60.810.00010.0010.010.11pb。

抽样方案的种类包括什么

抽样方案的种类包括什么抽样方案的种类包括什么摘要：抽样是统计学中的一项重要方法，用于从总体中选择一部分样本进行研究和分析。

抽样方案的选择和设计对于研究结果的准确性和可靠性具有决定性的影响。

本文将介绍抽样方案的种类，包括简单随机抽样、系统抽样、整群抽样、分层抽样、多阶段抽样和方便抽样，并对其特点和应用进行详细阐述。

一、简单随机抽样简单随机抽样是最基本的抽样方法，是通过随机抽取每个样本的概率相等，且相互独立的方法。

该方法的优点是样本选择的公平性和随机性，能够较好地代表总体的特征。

然而，由于随机性的特点，样本容易出现偏差，因此需要在实际应用中进行适当的校正和控制。

二、系统抽样系统抽样是按照一定的规则和顺序从总体中抽取样本的方法。

该方法的优点是简单、快捷，能够保持总体的一定特征，并且可以避免简单随机抽样中可能出现的偏差。

然而，如果总体中存在周期性或规律性的特征，系统抽样可能导致样本偏差。

三、整群抽样整群抽样是将总体划分为若干个互不重叠的群体，然后从每个群体中选择部分群体进行抽样的方法。

该方法的优点是能够更好地反映总体的特征，并且减少样本选择的复杂性。

然而，由于群体内的个体可能存在差异，整群抽样可能导致样本的偏差。

四、分层抽样分层抽样是将总体划分为若干个相互独立的层次，然后从每个层次中选择部分样本进行抽样的方法。

该方法的优点是能够在样本选择中考虑到不同层次的差异，增加样本的多样性，并且可以更好地反映总体的特征。

然而，分层抽样需要事先知道总体的分层特征，否则可能导致样本的偏差。

五、多阶段抽样多阶段抽样是将总体分为多个阶段，然后在每个阶段中选择部分样本进行抽样的方法。

该方法的优点是能够逐步缩小样本范围，减少样本选择的复杂性，并且节约时间和成本。

然而，多阶段抽样可能导致样本的聚集性和偏差，需要在设计中合理考虑和控制。

六、方便抽样方便抽样是基于研究者的便利性和容易获得的样本进行抽样的方法。

该方法的优点是简单、快捷，适用于一些初步研究或实践中的问题。

市场调查方法(英文版)第十五章

15–14
EXHIBIT 15.2 Independent Samples t-Test Results
© 2007 Thomson/South-Western. All rights reserved.
15–15
What Is ANOVA?
• Analysis of Variance (ANOVA)
➢ An analysis involving the investigation of the effects of one treatment variable on an interval-scaled dependent variable
➢ A hypothesis-testing technique to determine whether statistically significant differences in means occur between two or more groups.
❖ Behavior, characteristics, beliefs, opinions, emotions, or attitudes
• Bivariate Tests of Differences
➢ Involve only two variables: a variable that acts like a dependent variable and a variable that acts as a classification variable.
❖ Differences in mean scores between groups or in comparing how two groups’ scores are distributed across possible response categories.

计量经济学导论CH13习题答案

CHAPTER 13TEACHING NOTESWhile this chapter falls under “Advanced Topics,” most of this chapter requires no more sophistication than the previous chapters. (In fact, I would argue that, with the possible exception of Section 13.5, this material is easier than some of the time series chapters.)Pooling two or more independent cross sections is a straightforward extension of cross-sectional methods. Nothing new needs to be done in stating assumptions, except possibly mentioning that random sampling in each time period is sufficient. The practically important issue is allowing for different intercepts, and possibly different slopes, across time.The natural experiment material and extensions of the difference-in-differences estimator is widely applicable and, with the aid of the examples, easy to understand.Two years of panel data are often available, in which case differencing across time is a simple way of removing g unobserved heterogeneity. If you have covered Chapter 9, you might compare this with a regression in levels using the second year of data, but where a lagged dependent variable is included. (The second approach only requires collecting information on the dependent variable in a previous year.) These often give similar answers. Two years of panel data, collected before and after a policy change, can be very powerful for policy analysis. Having more than two periods of panel data causes slight complications in that the errors in the differenced equation may be serially correlated. (However, the traditional assumption that the errors in the original equation are serially uncorrelated is not always a good one. In other words, it is not always more appropriate to used fixed effects, as in Chapter 14, than first differencing.) With large N and relatively small T, a simple way to account for possible serial correlation after differencing is to compute standard errors that are robust to arbitrary serial correlation and heteroskedasticity. Econometrics packages that do cluster analysis (such as Stata) often allow this by specifying each cross-sectional unit as its own cluster.108SOLUTIONS TO PROBLEMS13.1 Without changes in the averages of any explanatory variables, the average fertility rate fellby .545 between 1972 and 1984; this is simply the coefficient on y84. To account for theincrease in average education levels, we obtain an additional effect: –.128(13.3 – 12.2) ≈–.141. So the drop in average fertility if the average education level increased by 1.1 is .545+ .141 = .686, or roughly two-thirds of a child per woman.13.2 The first equation omits the 1981 year dummy variable, y81, and so does not allow anyappreciation in nominal housing prices over the three year period in the absence of an incinerator. The interaction term in this case is simply picking up the fact that even homes that are near the incinerator site have appreciated in value over the three years. This equation suffers from omitted variable bias.The second equation omits the dummy variable for being near the incinerator site, nearinc,which means it does not allow for systematic differences in homes near and far from the sitebefore the site was built. If, as seems to be the case, the incinerator was located closer to lessvaluable homes, then omitting nearinc attributes lower housing prices too much to theincinerator effect. Again, we have an omitted variable problem. This is why equation (13.9) (or,even better, the equation that adds a full set of controls), is preferred.13.3 We do not have repeated observations on the same cross-sectional units in each time period,and so it makes no sense to look for pairs to difference. For example, in Example 13.1, it is veryunlikely that the same woman appears in more than one year, as new random samples areobtained in each year. In Example 13.3, some houses may appear in the sample for both 1978and 1981, but the overlap is usually too small to do a true panel data analysis.β, but only13.4 The sign of β1 does not affect the direction of bias in the OLS estimator of1whether we underestimate or overestimate the effect of interest. If we write ∆crmrte i = δ0 +β1∆unem i + ∆u i, where ∆u i and ∆unem i are negatively correlated, then there is a downward biasin the OLS estimator of β1. Because β1 > 0, we will tend to underestimate the effect of unemployment on crime.13.5 No, we cannot include age as an explanatory variable in the original model. Each person inthe panel data set is exactly two years older on January 31, 1992 than on January 31, 1990. This means that ∆age i = 2 for all i. But the equation we would estimate is of the form∆saving i = δ0 + β1∆age i +…,where δ0 is the coefficient the year dummy for 1992 in the original model. As we know, whenwe have an intercept in the model we cannot include an explanatory variable that is constant across i; this violates Assumption MLR.3. Intuitively, since age changes by the same amount for everyone, we cannot distinguish the effect of age from the aggregate time effect.10913.6 (i) Let FL be a binary variable equal to one if a person lives in Florida, and zero otherwise. Let y90 be a year dummy variable for 1990. Then, from equation (13.10), we have the linear probability modelarrest = β0 + δ0y90 + β1FL + δ1y90⋅FL + u.The effect of the law is measured by δ1, which is the change in the probability of drunk driving arrest due to the new law in Florida. Including y90 allows for aggregate trends in drunk driving arrests that would affect both states; including FL allows for systematic differences between Florida and Georgia in either drunk driving behavior or law enforcement.(ii) It could be that the populations of drivers in the two states change in different ways over time. For example, age, race, or gender distributions may have changed. The levels of education across the two states may have changed. As these factors might affect whether someone is arrested for drunk driving, it could be important to control for them. At a minimum, there is the possibility of obtaining a more precise estimator of δ1 by reducing the error variance. Essentially, any explanatory variable that affects arrest can be used for this purpose. (See Section 6.3 for discussion.)SOLUTIONS TO COMPUTER EXERCISES13.7 (i) The F statistic (with 4 and 1,111 df) is about 1.16 and p-value ≈ .328, which shows that the living environment variables are jointly insignificant.(ii) The F statistic (with 3 and 1,111 df) is about 3.01 and p-value ≈ .029, and so the region dummy variables are jointly significant at the 5% level.(iii) After obtaining the OLS residuals, ˆu, from estimating the model in Table 13.1, we run the regression 2ˆu on y74, y76, …, y84 using all 1,129 observations. The null hypothesis of homoskedasticity is H0: γ1 = 0, γ2= 0, … , γ6 = 0. So we just use the usual F statistic for joint significance of the year dummies. The R-squared is about .0153 and F ≈ 2.90; with 6 and 1,122 df, the p-value is about .0082. So there is evidence of heteroskedasticity that is a function of time at the 1% significance level. This suggests that, at a minimum, we should compute heteroskedasticity-robust standard errors, t statistics, and F statistics. We could also use weighted least squares (although the form of heteroskedasticity used here may not be sufficient; it does not depend on educ, age, and so on).(iv) Adding y74⋅educ, , y84⋅educ allows the relationship between fertility and education to be different in each year; remember, the coefficient on the interaction gets added to the coefficient on educ to get the slope for the appropriate year. When these interaction terms are added to the equation, R2≈ .137. The F statistic for joint significance (with 6 and 1,105 df) is about 1.48 with p-value ≈ .18. Thus, the interactions are not jointly significant at even the 10% level. This is a bit misleading, however. An abbreviated equation (which just shows the coefficients on the terms involving educ) is110111kids= -8.48 - .023 educ + - .056 y74⋅educ - .092 y76⋅educ(3.13) (.054) (.073) (.071) - .152 y78⋅educ - .098 y80⋅educ - .139 y82⋅educ - .176 y84⋅educ .(.075) (.070) (.068) (.070)Three of the interaction terms, y78⋅educ , y82⋅educ , and y84⋅educ are statistically significant at the 5% level against a two-sided alternative, with the p -value on the latter being about .012. The coefficients are large in magnitude as well. The coefficient on educ – which is for the base year, 1972 – is small and insignificant, suggesting little if any relationship between fertility andeducation in the early seventies. The estimates above are consistent with fertility becoming more linked to education as the years pass. The F statistic is insignificant because we are testing some insignificant coefficients along with some significant ones.13.8 (i) The coefficient on y85 is roughly the proportionate change in wage for a male (female = 0) with zero years of education (educ = 0). This is not especially useful since we are not interested in people with no education.(ii) What we want to estimate is θ0 = δ0 + 12δ1; this is the change in the intercept for a male with 12 years of education, where we also hold other factors fixed. If we write δ0 = θ0 - 12δ1, plug this into (13.1), and rearrange, we getlog(wage ) = β0 + θ0y85 + β1educ + δ1y85⋅(educ – 12) + β2exper + β3exper 2 + β4union + β5female + δ5y85⋅female + u .Therefore, we simply replace y85⋅educ with y85⋅(educ – 12), and then the coefficient andstandard error we want is on y85. These turn out to be 0ˆθ = .339 and se(0ˆθ) = .034. Roughly, the nominal increase in wage is 33.9%, and the 95% confidence interval is 33.9 ± 1.96(3.4), or about 27.2% to 40.6%. (Because the proportionate change is large, we could use equation (7.10), which implies the point estimate 40.4%; but obtaining the standard error of this estimate is harder.)(iii) Only the coefficient on y85 differs from equation (13.2). The new coefficient is about –.383 (se ≈ .124). This shows that real wages have fallen over the seven year period, although less so for the more educated. For example, the proportionate change for a male with 12 years of education is –.383 + .0185(12) = -.161, or a fall of about 16.1%. For a male with 20 years of education there has been almost no change [–.383 + .0185(20) = –.013].(iv) The R -squared when log(rwage ) is the dependent variable is .356, as compared with .426 when log(wage ) is the dependent variable. If the SSRs from the regressions are the same, but the R -squareds are not, then the total sum of squares must be different. This is the case, as the dependent variables in the two equations are different.(v) In 1978, about 30.6% of workers in the sample belonged to a union. In 1985, only about 18% belonged to a union. Therefore, over the seven-year period, there was a notable fall in union membership.(vi) When y85⋅union is added to the equation, its coefficient and standard error are about -.00040 (se ≈ .06104). This is practically very small and the t statistic is almost zero. There has been no change in the union wage premium over time.(vii) Parts (v) and (vi) are not at odds. They imply that while the economic return to union membership has not changed (assuming we think we have estimated a causal effect), the fraction of people reaping those benefits has fallen.13.9 (i) Other things equal, homes farther from the incinerator should be worth more, so δ1 > 0. If β1 > 0, then the incinerator was located farther away from more expensive homes.(ii) The estimated equation islog()price= 8.06 -.011 y81+ .317 log(dist) + .048 y81⋅log(dist)(0.51) (.805) (.052) (.082)n = 321, R2 = .396, 2R = .390.ˆδ = .048 is the expected sign, it is not statistically significant (t statistic ≈ .59).While1(iii) When we add the list of housing characteristics to the regression, the coefficient ony81⋅log(dist) becomes .062 (se = .050). So the estimated effect is larger – the elasticity of price with respect to dist is .062 after the incinerator site was chosen – but its t statistic is only 1.24. The p-value for the one-sided alternative H1: δ1 > 0 is about .108, which is close to being significant at the 10% level.13.10 (i) In addition to male and married, we add the variables head, neck, upextr, trunk, lowback, lowextr, and occdis for injury type, and manuf and construc for industry. The coefficient on afchnge⋅highearn becomes .231 (se ≈ .070), and so the estimated effect and t statistic are now larger than when we omitted the control variables. The estimate .231 implies a substantial response of durat to the change in the cap for high-earnings workers.(ii) The R-squared is about .041, which means we are explaining only a 4.1% of the variation in log(durat). This means that there are some very important factors that affect log(durat) that we are not controlling for. While this means that predicting log(durat) would be very difficultˆδ: it could still for a particular individual, it does not mean that there is anything biased about1be an unbiased estimator of the causal effect of changing the earnings cap for workers’ compensation.(iii) The estimated equation using the Michigan data is112durat= 1.413 + .097 afchnge+ .169 highearn+ .192 afchnge⋅highearn log()(0.057) (.085) (.106) (.154)n = 1,524, R2 = .012.The estimate of δ1, .192, is remarkably close to the estimate obtained for Kentucky (.191). However, the standard error for the Michigan estimate is much higher (.154 compared with .069). The estimate for Michigan is not statistically significant at even the 10% level against δ1 > 0. Even though we have over 1,500 observations, we cannot get a very precise estimate. (For Kentucky, we have over 5,600 observations.)13.11 (i) Using pooled OLS we obtainrent= -.569 + .262 d90+ .041 log(pop) + .571 log(avginc) + .0050 pctstu log()(.535) (.035) (.023) (.053) (.0010) n = 128, R2 = .861.The positive and very significant coefficient on d90 simply means that, other things in the equation fixed, nominal rents grew by over 26% over the 10 year period. The coefficient on pctstu means that a one percentage point increase in pctstu increases rent by half a percent (.5%). The t statistic of five shows that, at least based on the usual analysis, pctstu is very statistically significant.(ii) The standard errors from part (i) are not valid, unless we thing a i does not really appear in the equation. If a i is in the error term, the errors across the two time periods for each city are positively correlated, and this invalidates the usual OLS standard errors and t statistics.(iii) The equation estimated in differences islog()∆= .386 + .072 ∆log(pop) + .310 log(avginc) + .0112 ∆pctsturent(.037) (.088) (.066) (.0041)n = 64, R2 = .322.Interestingly, the effect of pctstu is over twice as large as we estimated in the pooled OLS equation. Now, a one percentage point increase in pctstu is estimated to increase rental rates by about 1.1%. Not surprisingly, we obtain a much less precise estimate when we difference (although the OLS standard errors from part (i) are likely to be much too small because of the positive serial correlation in the errors within each city). While we have differenced away a i, there may be other unobservables that change over time and are correlated with ∆pctstu.(iv) The heteroskedasticity-robust standard error on ∆pctstu is about .0028, which is actually much smaller than the usual OLS standard error. This only makes pctstu even more significant (robust t statistic ≈ 4). Note that serial correlation is no longer an issue because we have no time component in the first-differenced equation.11311413.12 (i) You may use an econometrics software package that directly tests restrictions such as H 0: β1 = β2 after estimating the unrestricted model in (13.22). But, as we have seen many times, we can simply rewrite the equation to test this using any regression software. Write the differenced equation as∆log(crime ) = δ0 + β1∆clrprc -1 + β2∆clrprc -2 + ∆u .Following the hint, we define θ1 = β1 - β2, and then write β1 = θ1 + β2. Plugging this into the differenced equation and rearranging gives∆log(crime ) = δ0 + θ1∆clrprc -1 + β2(∆clrprc -1 + ∆clrprc -2) + ∆u .Estimating this equation by OLS gives 1ˆθ= .0091, se(1ˆθ) = .0085. The t statistic for H 0: β1 = β2 is .0091/.0085 ≈ 1.07, which is not statistically significant.(ii) With β1 = β2 the equation becomes (without the i subscript)∆log(crime ) = δ0 + β1(∆clrprc -1 + ∆clrprc -2) + ∆u= δ0 + δ1[(∆clrprc -1 + ∆clrprc -2)/2] + ∆u ,where δ1 = 2β1. But (∆clrprc -1 + ∆clrprc -2)/2 = ∆avgclr .(iii) The estimated equation islog()crime ∆ = .099 - .0167 ∆avgclr(.063) (.0051)n = 53, R 2 = .175, 2R = .159.Since we did not reject the hypothesis in part (i), we would be justified in using the simplermodel with avgclr . Based on adjusted R -squared, we have a slightly worse fit with the restriction imposed. But this is a minor consideration. Ideally, we could get more data to determine whether the fairly different unconstrained estimates of β1 and β2 in equation (13.22) reveal true differences in β1 and β2.13.13 (i) Pooling across semesters and using OLS givestrmgpa = -1.75 -.058 spring+ .00170 sat- .0087 hsperc(0.35) (.048) (.00015) (.0010)+ .350 female- .254 black- .023 white- .035 frstsem(.052) (.123) (.117) (.076)- .00034 tothrs + 1.048 crsgpa- .027 season(.00073) (0.104) (.049)n = 732, R2 = .478, 2R = .470.The coefficient on season implies that, other things fixed, an athlete’s term GPA is about .027 points lower when his/her sport is in season. On a four point scale, this a modest effect (although it accumulates over four years of athletic eligibility). However, the estimate is not statistically significant (t statistic ≈-.55).(ii) The quick answer is that if omitted ability is correlated with season then, as we know form Chapters 3 and 5, OLS is biased and inconsistent. The fact that we are pooling across two semesters does not change that basic point.If we think harder, the direction of the bias is not clear, and this is where pooling across semesters plays a role. First, suppose we used only the fall term, when football is in season. Then the error term and season would be negatively correlated, which produces a downward bias in the OLS estimator of βseason. Because βseason is hypothesized to be negative, an OLS regression using only the fall data produces a downward biased estimator. [When just the fall data are used, ˆβ = -.116 (se = .084), which is in the direction of more bias.] However, if we use just the seasonspring semester, the bias is in the opposite direction because ability and season would be positive correlated (more academically able athletes are in season in the spring). In fact, using just theβ = .00089 (se = .06480), which is practically and statistically equal spring semester gives ˆseasonto zero. When we pool the two semesters we cannot, with a much more detailed analysis, determine which bias will dominate.(iii) The variables sat, hsperc, female, black, and white all drop out because they do not vary by semester. The intercept in the first-differenced equation is the intercept for the spring. We have∆= -.237 + .019 ∆frstsem+ .012 ∆tothrs+ 1.136 ∆crsgpa- .065 seasontrmgpa(.206) (.069) (.014) (0.119) (.043) n = 366, R2 = .208, 2R = .199.Interestingly, the in-season effect is larger now: term GPA is estimated to be about .065 points lower in a semester that the sport is in-season. The t statistic is about –1.51, which gives a one-sided p-value of about .065.115(iv) One possibility is a measure of course load. If some fraction of student-athletes take a lighter load during the season (for those sports that have a true season), then term GPAs may tend to be higher, other things equal. This would bias the results away from finding an effect of season on term GPA.13.14 (i) The estimated equation using differences is∆= -2.56 - 1.29 ∆log(inexp) - .599 ∆log(chexp) + .156 ∆incshrvote(0.63) (1.38) (.711) (.064)n = 157, R2 = .244, 2R = .229.Only ∆incshr is statistically significant at the 5% level (t statistic ≈ 2.44, p-value ≈ .016). The other two independent variables have t statistics less than one in absolute value.(ii) The F statistic (with 2 and 153 df) is about 1.51 with p-value ≈ .224. Therefore,∆log(inexp) and ∆log(chexp) are jointly insignificant at even the 20% level.(iii) The simple regression equation is∆= -2.68 + .218 ∆incshrvote(0.63) (.032)n = 157, R2 = .229, 2R = .224.This equation implies t hat a 10 percentage point increase in the incumbent’s share of total spending increases the percent of the incumbent’s vote by about 2.2 percentage points.(iv) Using the 33 elections with repeat challengers we obtain∆= -2.25 + .092 ∆incshrvote(1.00) (.085)n = 33, R2 = .037, 2R = .006.The estimated effect is notably smaller and, not surprisingly, the standard error is much larger than in part (iii). While the direction of the effect is the same, it is not statistically significant (p-value ≈ .14 against a one-sided alternative).13.15 (i) When we add the changes of the nine log wage variables to equation (13.33) we obtain116117 log()crmrte ∆ = .020 - .111 d83 - .037 d84 - .0006 d85 + .031 d86 + .039 d87(.021) (.027) (.025) (.0241) (.025) (.025)- .323 ∆log(prbarr ) - .240 ∆log(prbconv ) - .169 ∆log(prbpris )(.030) (.018) (.026)- .016 ∆log(avgsen ) + .398 ∆log(polpc ) - .044 ∆log(wcon )(.022) (.027) (.030)+ .025 ∆log(wtuc ) - .029 ∆log(wtrd ) + .0091 ∆log(wfir )(0.14) (.031) (.0212)+ .022 ∆log(wser ) - .140 ∆log(wmfg ) - .017 ∆log(wfed )(.014) (.102) (.172)- .052 ∆log(wsta ) - .031 ∆log(wloc ) (.096) (.102) n = 540, R 2 = .445, 2R = .424.The coefficients on the criminal justice variables change very modestly, and the statistical significance of each variable is also essentially unaffected.(ii) Since some signs are positive and others are negative, they cannot all really have the expected sign. For example, why is the coefficient on the wage for transportation, utilities, and communications (wtuc ) positive and marginally significant (t statistic ≈ 1.79)? Higher manufacturing wages lead to lower crime, as we might expect, but, while the estimated coefficient is by far the largest in magnitude, it is not statistically different from zero (tstatistic ≈ –1.37). The F test for joint significance of the wage variables, with 9 and 529 df , yields F ≈ 1.25 and p -value ≈ .26.13.16 (i) The estimated equation using the 1987 to 1988 and 1988 to 1989 changes, where we include a year dummy for 1989 in addition to an overall intercept, isˆhrsemp ∆ = –.740 + 5.42 d89 + 32.60 ∆grant + 2.00 ∆grant -1 + .744 ∆log(employ ) (1.942) (2.65) (2.97) (5.55) (4.868)n = 251, R 2 = .476, 2R = .467.There are 124 firms with both years of data and three firms with only one year of data used, for a total of 127 firms; 30 firms in the sample have missing information in both years and are not used at all. If we had information for all 157 firms, we would have 314 total observations in estimating the equation.(ii) The coefficient on grant – more precisely, on ∆grant in the differenced equation – means that if a firm received a grant for the current year, it trained each worker an average of 32.6 hoursmore than it would have otherwise. This is a practically large effect, and the t statistic is very large.(iii) Since a grant last year was used to pay for training last year, it is perhaps not surprising that the grant does not carry over into more training this year. It would if inertia played a role in training workers.(iv) The coefficient on the employees variable is very small: a 10% increase in employ increases hours per employee by only .074. [Recall:∆≈ (.744/100)(%∆employ).] Thishrsempis very small, and the t statistic is also rather small.13.17. (i) Take changes as usual, holding the other variables fixed: ∆math4it = β1∆log(rexpp it) = (β1/100)⋅[ 100⋅∆log(rexpp it)] ≈ (β1/100)⋅( %∆rexpp it). So, if %∆rexpp it = 10, then ∆math4it= (β1/100)⋅(10) = β1/10.(ii) The equation, estimated by pooled OLS in first differences (except for the year dummies), is4∆ = 5.95 + .52 y94 + 6.81 y95- 5.23 y96- 8.49 y97 + 8.97 y98math(.52) (.73) (.78) (.73) (.72) (.72)- 3.45 ∆log(rexpp) + .635 ∆log(enroll) + .025 ∆lunch(2.76) (1.029) (.055)n = 3,300, R2 = .208.Taken literally, the spending coefficient implies that a 10% increase in real spending per pupil decreases the math4 pass rate by about 3.45/10 ≈ .35 percentage points.(iii) When we add the lagged spending change, and drop another year, we get4∆ = 6.16 + 5.70 y95- 6.80 y96- 8.99 y97 +8.45 y98math(.55) (.77) (.79) (.74) (.74)- 1.41 ∆log(rexpp) + 11.04 ∆log(rexpp-1) + 2.14 ∆log(enroll)(3.04) (2.79) (1.18)+ .073 ∆lunch(.061)n = 2,750, R2 = .238.The contemporaneous spending variable, while still having a negative coefficient, is not at all statistically significant. The coefficient on the lagged spending variable is very statistically significant, and implies that a 10% increase in spending last year increases the math4 pass rate118119 by about 1.1 percentage points. Given the timing of the tests, a lagged effect is not surprising. In Michigan, the fourth grade math test is given in January, and so if preparation for the test begins a full year in advance, spending when the students are in third grade would at least partly matter.(iv) The heteroskedasticity-robust standard error for log() ˆrexpp β∆is about 4.28, which reducesthe significance of ∆log(rexpp ) even further. The heteroskedasticity-robust standard error of 1log() ˆrexpp β-∆is about 4.38, which substantially lowers the t statistic. Still, ∆log(rexpp -1) is statistically significant at just over the 1% significance level against a two-sided alternative.(v) The fully robust standard error for log() ˆrexpp β∆is about 4.94, which even further reducesthe t statistic for ∆log(rexpp ). The fully robust standard error for 1log() ˆrexpp β-∆is about 5.13,which gives ∆log(rexpp -1) a t statistic of about 2.15. The two-sided p -value is about .032.(vi) We can use four years of data for this test. Doing a pooled OLS regression of ,1垐 on it i t r r -,using years 1995, 1996, 1997, and 1998 gives ˆρ= -.423 (se = .019), which is strong negative serial correlation.(vii) Th e fully robust “F ” test for ∆log(enroll ) and ∆lunch , reported by Stata 7.0, is .93. With 2 and 549 df , this translates into p -value = .40. So we would be justified in dropping these variables, but they are not doing any harm.。

雅思阅读机经真题解析-Novice and Expert

雅思阅读机经真题解析-Novice and Expert小站独家，雅思阅读机经真题解析。

一切患有雅思阅读刷题强迫症的烤鸭，请看这里。

小站精心整理了一批雅思阅读机经真题。

如果你的剑桥雅思阅读已是烂熟于心，那么这一系列的雅思阅读机经真题真的很适合你，搭配上绝对原创的讲解，还有全文的中文翻译，这等阅读大餐，还等什么!You should spend about 20 minutes on Question 14-26 which are based on Reading Passage below.Becoming an ExpertExpertise is commitment coupled with creativity. Specifically, it is the commitment of time, energy, and resources to a relatively narrow field of study and the creative energy necessary to generate new knowledge in that field. It takes a considerable amount of time and regular exposure to a large number of cases to become an expert.AAn individual enters a field of study as a novice. The novice needs to learn the guiding principles and rules of a given task in order to perform that task. Concurrently, the novice needs to be exposed to specific cases, or instances, that test the boundaries of such heuristics. Generally, a novice will find a mentor to guide her through the process. A fairly simple example would be someone learning to play chess. The novice chess player seeks a mentor to teach her the object of the game, the number of spaces, the names of the pieces, the function of each piece, how each piece is moved, and the necessary conditions for winning or losing the game.BIn time, and with much practice, the novice begins to recognize patterns of behavior within cases and. thus, becomes a journeyman. With more practice and exposure to increasingly complex cases, the journeyman finds patterns not only within cases but also between cases. More importantly, the journeyman learns that these patterns often repeat themselves over time. The journeyman still maintains regular contact with a mentor to solve specific problems and learn more complex strategies. Returning to the example of the chess player, the individual begins to learn patterns of opening moves, offensive and defensive game-playing strategies, and patterns of victory and defeat.CWhen a journeyman starts to make and test hypotheses about future behavior based on past experiences, she begins the next transition. Once she creatively generates knowledge, rather than simply matching superficial patterns, she becomes an expert. At this point, she is confident in her knowledge and no longer needs a mentor as a guide—she becomes responsible for her own knowledge. In the chess example, once a journeyman begins competing against experts, makes predictions based on patterns, and tests those predictions against actual behavior, she is generating new knowledgeand a deeper understanding of the game. She is creating her own cases rather than relying on the cases of others.DThe chess example is a rather short description of an apprenticeship model. Apprenticeship may seem like a restrictive 18th century mode of education, but it is still a standard method of training for many complex tasks. Academic doctoral programs are based on an apprenticeship model, as are fields like law, music, engineering, and medicine. Graduate students enter fields of study, find mentors, and begin the long process of becoming independent experts and generating new knowledge in their respective domains.EPsychologists and cognitive scientists agree that the time it takes to become an expert depends on the complexity of the task and the number of cases, or patterns, to which an individual is exposed. The more complex the task, the longer it takes to build expertise, or, more accurately, the longer it takes to experience and store a large number of cases or patterns.FThe Power of ExpertiseAn expert perceives meaningful patterns in her domain better than non-experts. Where a novice perceives random or disconnected data points, an expert connects regular patterns within and between cases. This ability to identify patterns is not an innate perceptual skill; rather it reflects the organization of knowledge after exposure to and experience with thousands of cases. Experts have a deeper understanding of their domains than novices do, and utilize higher-order principles to solve problems. A novice, for example, might group objects together by color or size, whereas an expert would group the same objects according to their function or utility. Experts comprehend the meaning of data and weigh variables with different criteria within their domains better than novices. Experts recognize variables that have the largest influence on a particular problem and focus their attention on those variables.GExperts have better domain-specific short-term and long-term memory than novices do. Moreover, experts perform tasks in their domains faster than novices and commit fewer errors while problem solving. Interestingly, experts go about solving problems differently than novices. Experts spend more time thinking about a problem to fully understand it at the beginning of a task than do novices, who immediately seek to find a solution. Experts use their knowledge of previous cases as context for creating mental models to solve given problems.HBetter at self-monitoring than novices, experts are more aware of instances where they have committed errors or failed to understand a problem. Experts check their solutions more often thannovices and recognize when they are missing information necessary for solving a problem. Experts are aware of the limits of their domain knowledge and apply their domain's heuristics to solve problems that fall outside of their experience base.IThe Paradox of ExpertiseThe strengths of expertise can also be weaknesses. Although one would expect experts to be good forecasters, they are not particularly good at making predictions about the future. Since the 1930s, researchers have been testing the ability of experts to make forecasts. The performance of experts has been tested against actuarial tables to determine if they are better at making predictions than simple statistical models. Seventy years later, with more than two hundred experiments in different domains, it is clear that the answer is no. If supplied with an equal amount of data about a particular case, an actuarial table is as good, or better, than an expert at making calls about the future. Even if an expert is given more specific case information than is available to the statistical model, the expert does not tend to outperform the actuarial table.JTheorists and researchers differ when trying to explain why experts are less accurate forecasters than statistical models. Some have argued that experts, like all humans, are inconsistent when using mental models to make predictions. A number of researchers point to human biases to explain unreliable expert predictions. During the last 30 years, researchers have categorized, experimented, and theorized about the cognitive aspects of forecasting. Despite such efforts, the literature shows little consensus regarding the causes or manifestations of human bias.Questions 1-5Complete the flow chartChoose No More Than Three Words from the Reading Passage for each answer. Write your answers in boxes 1-5on your answer sheet.From a novice to an expertNovice:↓need to study 1 under the guidance of a23↓start to identify 4 for cases within or between study more 5 ways of doing thingsExpert:create new knowledgeperform task independentlyQuestions 6-10Do the following statements agree with the information given in Reading Passage 1?In boxes 6-10 on your answer sheet, writeTRUE if the statement is trueFALSE if the statement is falseNOT GIVEN if the information is not given in the passage6. Novices and experts use the same system of knowledge to comprehend and classify objects.7. The focus of novices' training is necessarily on long term memory8. When working out the problems, novices want to solve them straight away.9. When handling problems, experts are always more efficient than novices in their fields.10. Expert tend to review more than novices on cases when flaws or limit on understanding took place.Questions 11-13Complete the following summary of the paragraphs of Reading Passage, using No More Than Two Words from the Reading Passage for each answer. Write your answers in boxes 11-13 on your answer sheet.While experts outperform novices and machines in pattern recognition and problem solving, expert predictions of future behavior or events are seldom as accurate as simple actuarial tables. Why? Some have tried to explain that experts differ when using cognitive 11 to forecast. Researchers believe it is due to 12 . However attempting endeavor of finding answers did not yet produce 13 .文章题目：Novice and Expert篇章结构体裁论说文题目新手与专家结构A 新手进入业界的首要任务B 新手积累经验之后的任务C 新手如何向专家过渡D 学徒关系训练法的重要性E 决定成为专家时间的因素F 专家更善于觉察并聚焦对于特定稳定有最大影响的变脸G 专家与新手的区别H 专家相对新手更擅长自我检测I 专家在预测未来方面不如数据统计J 专家预测未来逊于统计模型的原因试题分析Question 1-13题目类型：Question 1-5 Complete the flow chartQuestion 6-10 TRUE, FALSE, NOT GIVENQuestion 11-13 Answer the questions below题号定位词文中对应点题目解析1A novice , requires to studyA段第二句本题可以根据线索词novice定位在A-C段，由requires to study可以判断A段第二句中needs to learn为同义词。

学术英语(综合)U7教师用书

Unit 7 MathematicsI Teaching ObjectivesAfter learning Unit 7, students (Ss) are expected to develop the following academic skills and knowledge:II Teaching Activities and ResourcesReadingText ALead-inTeaching StepsPut Ss into pairs and ask them to do the task in Lead-in. Then choose several Ss to share their answers with the whole class.Answer Keys (Suggested Answers)•Analyzing statistics collected from questionnaires•Conducting experiments and analyzing the data•Purchasing financial productsText AnalysisTeaching Steps1.OverviewLet Ss preview Text A before class. An alternative plan is to allocate some time for Ss to read Text A quickly in class. Then invite several Ss to summarize the main idea.2.In-Depth Analysis1)Show Ss the following words and invite them to share their ownunderstandings with the class. Provide additional information in Supplementary Information when necessary.•Fibonacci sequence•decimal place•Stephen Baker and The Numerati•Acronym and Initialism2)Explain some important language points in Language Support to Ss.3)Discuss with Ss the nature and predictive function of mathematics in theauthor’s eyes by doing Task 1 in Critical reading and thinking.4)Ask Ss to work in pairs on some of the questions about mathematics bydoing Task 2 in Critical reading and thinking. Call on some Ss to report their answers to the class.Supplementary Information1.Fibonacci sequenceFibonacci sequence is often observed in the geometry of plants such as flowersand fruit with regard to their recurrent structures and forms. For instance, Fibonacci sequence is used to study and indicate the arrangement of leaves, branches, flowers or seeds in plants, highlighting the existence of regular patterns.Fibonacci sequence is also closely related to the Golden Ratio (approximately noted as 1.168), which not only frequently occurs in nature, but is widely used to achieve aesthetic perfection in artworks, such as sculptures and paintings.2.Stephen Baker and The NumeratiStephen Baker is an American journalist, non-fiction author and novelist who often explores themes concerning data and technology. The Numerati is a non-fiction book written by Stephen Baker. In this book, Baker discusses the increasing role that data-mining plays in politics, business, law enforcement, etc. on the basis of interviews with the numerati, which refers to people who are developing and using technologies to analyze and characterize our everyday actions. The book shows that data-mining can be used to predict outcomes and influence human behavior.3.Acronym and InitialismAcronym and Initialism are two types of abbreviation. Acronyms are pronounced as a whole word (e.g. NASA) while Initialisms are pronounced one letter at a time(e.g. FBI). In this text, an example of Acronym is ASCII, and CCTV is a typicalexample of Initialism.Language Support1.What if those strings of numbers are records of the things you’ve bought, placesyou’ve traveled to, websites you’ve visited, parties you’ve voted for?(Para. 3)此处作者为了引起读者注意，营造交互感，使用了偏对话体的文风。

2023北京高三一模英语汇编：阅读理解C篇

2023北京高三一模英语汇编阅读理解C篇一、阅读理解（2023·北京东城·统考一模）Many people have participated into lots of virtual meetings these years. Some research shows this adjustment might not impact workplace productivity to any great degree. A new study, though, suggests otherwise.In the study, 602 participants were randomly paired and asked to come up with creative uses for a product. They were also randomly selected to work together either in person or virtually. The pairs were then ranked by assessing their total number of ideas, as well as those concepts’ degree of novelty, and asked to submit their best idea. Among the groups, virtual pairs came up with significantly fewer ideas, suggesting that something about face-to-face interaction generates more creative ideas. The findings could stiffen employers’ resolve to urge or require their employees to come back to the office.“We ran this experiment based on feedback from companies that it was harder to innovate with remote workers,” said lead researcher Melanie Brucks. “Unlike other forms of virtual communication, like phone calls or e-mail, videoconferencing copies the in-person experience quite well, so I was surprised when we found meaningful differences between in-person and video interaction for idea generation. ”When random objects were placed in both the virtual and physical rooms, the virtual pairs of participants spent more time looking directly at each other rather than letting their look wander about the room and taking in the entire scene. Eyeing one’s whole environment and noticing the random objects were associated with increased idea generation. On platforms, the screen occupies our interactions. Our look wavers less. “Looking away might come across as rude, ” said Brucks,“so we have to look at the screen because that is the defined context of the interaction, the same way we wouldn’t walk to another room while talking to someone in person. ”Like most educators, Brucks has primarily taught virtually in the past three years, and she did notice some benefits of the approach as well. Her students were more likely to take turns speaking and her shyer students spoke up more often, rid of the anxiety that comes from addressing a large classroom. Brucks found that one solution to improving virtual idea generation might be to simply turn off the camera, for her students felt “freer” and more creative when asked to do so. And this may be sound advice for the workplace.Virtual teamwork can’t replace face-to-face teamwork. Idea selection proficiency (能力) is only valuable if you have strong options to select from, and face-to-face teams are the best means to generate winning options. Perhaps the workplace will find a compromise—a sweet spot in the middle that balances working from both home and office.1．What does the underlined word “stiffen” in Paragraph 2 most probably mean?A．Shake.B．Revise.C．Challenge.D．Strengthen.2．At first, lead researcher Melanie Brucks might think that ________.A．videoconferencing can’t compare with in-person communicationB．participants should make eye contact in an online meetingC．the feedback from companies seems questionableD．creative ideas may emerge from casual thoughts3．What can we learn about Brucks’ class?A．Her students relieved anxiety by speaking up.B．Her students progressed in focusing attention.C．Her students took advantage of virtual learning.D．Her students displayed talent for public speaking. 4．Which of the following would be the best title for the passage?A．Interacting Virtually Impacts Working Participation B．Maintaining Teamwork Improves Idea Generation C．Grouping Randomly Increases Productivity D．Brainstorming Online Limits Creativity （2023·北京西城·统考一模）Imagine a simple blood test that could flag most kinds of cancers at the earliest, most curable stage. Liquid biopsies could, in theory, detect a tumor (肿瘤) well before it could be found by touch, symptoms or imaging. Blood tests could avoid the need for surgeons to cut tissue samples and make it possible to reveal cancer hiding in places needles and scalpels cannot safely reach. They could also determine what type of cancer is taking root to help doctors decide what treatment might work best to destroy it.Liquid biopsies are not yet in hand, because it is hard to find definitive cancer signals in a tube of blood, but progress in recent years has been impressive. Last year the journal Science published the first big prospective study of a liquid biopsy for DNA and proteins from multiple types of cancers. Though far from perfect, the blood test called CancerSEEK found 26 tumors that had not been discovered with conventional screenings.Liquid biopsies can rely on a variety of biomarkers in addition to tumor DNA and proteins, such as free-floating cancer cells themselves. But what makes the search difficult, Ana Robles, a cancer biologist of the National Cancer Institute, explains, is that “if you have an early-stage cancer or certain types of cancer, there might not be a lot of tumor DNA,” and tests might miss it. The ideal blood test will be both very specific and very sensitive so that even tiny tumors can be found. To tackle this challenge, CancerSEEK looks for cancer-specific mutations (突变) on 16 genes, and for eight proteins that are linked to cancer and for which there are highly sensitive tests.Simple detection is not the only goal. An ideal liquid biopsy will also determine the likely location of the cancer so that it can be treated. “Mutations are often shared among different kinds of cancer, so if you find them in blood, you don’t know if that mutation is coming from a stomach cancer or lung cancer,” says Anirban Maitra, a cancer scientist at the Anderson Cancer Center. To solve that problem, some newer liquid biopsies look for changes in gene expression. Such changes, Maitra notes, are “more organ-specific”.On the nearer horizon are liquid biopsies to help people already diagnosed with cancer. Last year the government approved the first two such tests, which scan for tumor DNA so doctors can select mutation-targeted drugs. Scientists are working on blood tests to detect the first signs of cancer recurrence (复发) in patients who have completed treatment. This work is moving fast, but does it save lives?That is the question companies such as Thrive and Grail must answer for their broadly ambitious screening tests. “These companies have to prove that they can detect early cancer and, more important, that the early detection can have an impact on cancer survival,” Maitra observes.5．According to the passage, liquid biopsies are expected toA．flag cancer and determine the treatmentB．detect cancer signals from a sample of bloodC．take images of tumors and prevent potential cancersD．show types of cancer by measuring the amount of proteins6．What can we learn from the passage?A．Signs of cancer recurrence are not detectable.B．Different kinds of cancer have different gene mutations.C．Biomarkers are much more reliable than tumor DNA and proteins.D．Organ-specific cancers will be identified through changes in gene expression.7．The author is mostly concerned about whether .A．liquid biopsies can discover tumors conventional screenings can’t findB．liquid biopsies can improve the application of mutation-targeted drugsC．liquid biopsies can help save the lives of those with cancerD．liquid biopsies can be developed for cancer prevention（2023·北京海淀·统考一模）To a chef, the sounds of lip smacking, slurping and sallwing are the highest form of fltery (恭维). But to someone with a certain type of misophonia (恐音症), these same sounds can be torturous. Brain scans are now helping scientists start to understand why.People with misophonia experience strong discomfort, annoyance or disgust when they hear particular triggers. These can include chewing, swallowing, slurping, throat clearing, coughing and even audible breathing. Researchers previously thought this reaction might be caused by the brain overactively processing certain sounds. Now, however, a new study published in Journal of Neuroscience has linked some forms of misophonia to heightened “mirroring” behavior in the brain: those affected feel distress while their brains act as if they were imitating the triggering mouth movements."This is the first breakthrough in misophonia research in 25 years, " says psychologist Jennifer J. Brout, who directs the International Misophonia Research Network and was not involved in the new study.The research team, led by Newcastle University neuroscientist Sukhbinder Kumar, analyzed brain activity in people with and without misophonia when they were at rest and while they listened to sounds. These included misophonia triggers (such as chewing), generally unpleasant sounds (like a crying baby), and neutral sounds. The brain's auditory (听觉的) cortex, which processes sound, reacted similarly in subjects with and without misophonia. But in both the resting state and listening trials, people with misophonia showed stronger connections between the auditory cortex and brain regions that control movements of the face, mouth and throat, while the controlled group didn't. Kumar found this connection became most active in participants with misophonia when they heard triggers specific to the condition.“Just by listening to the sound, they activate the motor cortex more strongly. So in a way it was as if they were doing the action themselves,” Kumar says. Some mirroring is typical in most humans when witnessing others' actions; the researchers do not yet know why an excessive (过分的) mirroring response might cause such a negative reaction, and hope to address that in future research. “Possibilities include a sense of loss of control, invasion of personal space, or interference with current goals and actions, " the study authors write.Fatima Husain, an llinois University professor of speech and hearing science, who was not involved in the study, says potential misophonia therapies could build on the new findings by counseling patients about handling unconscious motor responses to triggering sounds- not just coping with the sounds themselves. If this works, sheadds, one should expect to see reduced connected activity between the auditory and motor cortices.8. It can be learnt from the new study that .A. misophonia sufferers can't help imitating the triggersB. people with misophonia are more likely to flatter chefsC. the brains of people with misophonia overreact to sounds stronglyD. misophonia sufferers tend to have similar annoying activities in their brains9. Compared with people without misophonia, people with misophonia .A. suffer less severely at the resting stateB. own markedly different brain structuresC. react more negatively at a mirroring responseD. lose control of their facial movements easily10. What might be the significance of the study?A. Improving speech and hearing science.B. Developing a treatment for misophonia.C. Drawing people's attention to misophonia.D. Promoting human brain structure research.（2023·北京朝阳·统考一模）Clown fish live their adult lives in the protective arms of sea anemones, the small brightly colored sea animals attached onto rocks to house clown fish. Between birth and adulthood, however, the fish have to complete a treacherous journey. After hatching, they swim out to the open sea to finish developing. After maturing, the young fish swim back, during which they have to avoid a “wall of mouths” by sensing the unfriendly smells. With ocean acidification, a trend that is occurring worldwide, scientists began to wonder what might happen to fish’s sense of smell.My team put 300 recently hatched clown fish in our lab. When we introduced a friendly fish odor (气味), they did not react. But when we introduced an enemy odor, they swam away. We then repeated the experiment with 300 new hatchlings from the same parents in the more acidic water-a level we can expect by the year 2100 if current trends continue. When we introduced friendly and unfriendly smells at the same time, the fish seemed unable to make up their minds, spending equal time swimming toward one smell and the other. They could sense chemical signals but couldn’t recognize the meaning of them.It is always tricky to say that behaviors seen in a lab would also be seen in the wild. So we went to a sandy lake near one of the Great Barrier Reef’s northern islands to test how wild-caught damselfish would react to enemy smells after exposing them to acidic water. In a tank, about half of them held in water with acidity expected by 2050 were attracted to the unfriendly odor and half were not, yet not one held in water anticipated by 2100 avoided being attracted to the enemy odor. We then let the marked damselfish loose in the lake. The fish once held in the most acidic water swam farther away from their protective home. Can fish adapt? Most studies have habituated fish to lifted acidic conditions over a few days or months-an extremely short length of time. The animals are not given a realistic opportunity to adapt. Yet some scientists thought that fish might escape the anger of ocean acidification, in part because early research done in the 1980s showed that certain animals had an astonishing ability to regulate their internal chemistry to survive acidified water. But maintaining normal functions such as avoiding danger is a different challenge.At a minimum, confusion could place yet another stressor on fish already challenged by rising watertemperatures, overfishing, etc. Further, if many ocean creatures start to behave strangely, entire food webs and ecosystems could come crashing down. Although the science is still new, the results appear to be lining up: ocean acidification is messing with fish’s minds.11．What does the underlined word “treacherous” in Paragraph 1 probably mean?A．Risky.B．Hurried.C．Mysterious.D．Helpless.12．What can we learn about the fish in the acidic water?A．They lost their senses to chemical signals.B．They were less likely to respond to threats.C．Their behavior in the lab disappeared in the wild.D．They tended to seek the protection from their home.13．What can be inferred from the passage?A．The author’s study confirms previous findings.B．Fish’s adaptation to acidic water is a matter of time.C．Different fishes behave differently to acidity change.D．The chances of restoring fish’s minds are yet to be seen.14．Which of the following would be the best title for the passage?A．What Do Different Stressors Do to Ocean Creatures?B．What Does Ocean Acidity Mean to Ocean Creatures?C．How Does Ocean Acidification Destroy the Ecosystem?D．How Do Ocean Creatures Adapt to Ocean Acidification?（2023·北京丰台·统考一模）Many people would answer the question of what makes us human by insisting that we are cultural beings. There is no doubt that we are. But one definition of culture is the totality of traditions acquired in a community by social learning from other individuals, and many animal species have traditions. Can we then say that some animals are cultural beings too?One approach to study culture in animals is the so-called Method of Exclusion (排除), in which scientists investigate behavioral variations across populations of one species. In a famous study, scientists learned that chimpanzee (黑猩猩) behaviors were socially passed on as they were present at some sites but not at others, despite having same ecological settings. For example, chimpanzees in Tai National Park in Ivory Coast are well-known for their nut-cracking skills. Chimpanzees in Gombe national part in Tanzania, on the other hand, do not crack nuts, although nuts exist in their environment too.However, when applying the Method of Exclusion, one has to be very careful. There are other factors that could also explain the pattern of behavioral evaluation. For example, some of the chimpanzee techniques scientists evaluated occur in only one of the three subspecies. So it’s quite possible that these behaviors also have an innate component. This would mean that one chimpanzee subspecies uses a new technique not out of cultural tradition, but because the behavior is fixed to specific genes. Another factor that has to be excluded is of course the environment Chimpanzees in Mahale do not fish algae (水藻), simply because algae does not exist there.But when we exclude all the variations that can be explained by genes or environment, we still find that animals do show cultural variations. Does that mean there is no real difference between them and us after all? Not exactly: There is a fundamental difference between human and animal culture. Only humans can build culturally on what generations before us have learned. This is called “cumulative culture”. We don’t have to keep reinventing the wheel. This is called the “ratchet (棘轮) effect”. Like a ratchet that can be turned forward but not back, people’s cultural techniques evolve.It is likely that behaviors we see today in chimpanzee cultures could be invented over and over again by individual animals themselves. In contrast, a child born today would not be able to invent a computer without the knowledge of many past generations.15．Why does the author mention the example of the chimpanzees in two parks in Paragraph 2?A．To prove that culture does exist in animals.B．To justify the uniqueness of the research method.C．To compare how chimpanzees behave in different parks.D．To stress the importance of environment in studying culture.16．What does the underlined word “innate” in Paragraph 3 probably mean?A．Advanced.B．Inborn.C．Adaptive.D．Intelligent.17．What can we learn from the passage?A．Cumulative culture is what sets humans apart from animals.B．Culure in animals is as worthy to be valued as human culture.C．Animals don’t have the ability to invent behaviors in a community.D．The “ratchet effect” decides if humans can build on past experiences.（2023·北京石景山·Recently, a new discovery has been made in the field of biology that challenges our previous understanding of the origins of life on Earth. For decades, scientists believed that life emerged in a warm, shallow pond, where a series of chemical reactions led to the creation of the first living cells. However, a team of researchers has now uncovered evidence that suggests life may have actually begun in a quite different environment.The discovery was made in a remote part of the ocean, where a team of scientists was exploring a hydrothermal vent (深海热泉) system located deep beneath the surface. Hydrothermal vents release superheated water and gases from the Earth’s mantle (地幔). They create an extreme environment that is unfavourable to most forms of life, but home to a variety of unique organisms.As the scientists collected samples of the vent’s mineral-rich liquid, they noticed something strange. They discovered that it contained a complex network of organic molecules (分子), including amino acids (氨基酸) and other building blocks of life. This was surprising, as hydrothermal vents were previously thought to be empty of life-supporting chemicals. However, the most striking discovery was yet to come. Analysis of the organic molecules revealed that they were not simply the product of chemical reactions in the vent, but rather had been produced by living organisms. This served as evidence of a lively ecosystem that was completely independent of sunlight and the surface world.This discovery has significant implications for our understanding of the origins of life on Earth. The warmpond hypothesis (假说), which has long been the dominant theory, suggests that life emerged in a relatively warm and damp environment. However, the new evidence from the hydrothermal vent system suggests that life may have actually begun in an extreme environment, where survival was only possible through the development of original biochemical pathways. The discovery also raises new questions about the potential for life to exist on other celestial bodies (天体). Hydrothermal vents have been identified on Saturn’s moon Enceladus and Jupiter’s moon Europa. The presence of organic molecules at these sites suggests that unique forms of life may exist.While this discovery is still in its early stages, it is already beginning to reshape our understanding of the origins of life and the potential for life on other planets. It is a testament to the power of scientific exploration and the incredible complexity of life on Earth. As we continue to explore the depths of the ocean and the vast expanse of space, who knows what other surprises and discoveries lie in store.18．What did the scientists newly discover in the vent’s liquid?A．Superheated gases.B．A variety of minerals.C．Life-supporting chemicals.D．Complex forms of life.19．What can we learn from the passage?A．The warm pond hypothesis proves to be right.B．Organic molecules lead to chemical reactions.C．Forms of life were identified in the vents on other planets.D．Original biochemical pathways can occur in severe conditions.20．What is conveyed in the last paragraph?A．Researches help us push the boundaries of technology.B．Scientists have addressed the limitations of the research.C．D．Researchers have confirmed the origins and complexity of life.（2023·北京房山·统考一模）Vast underwater meadows (草甸) of gently waving sea grass cover hundreds of miles up and down the West Coast. These blue-green fields perform a variety of important services. They protect the shoreline from erosion, clear pollutants from the water and provide habitats for all kinds of marine animals.New research suggests sea grass meadows may also mitigate a serious consequence of greenhouse gas emissions: the steady acidification of ocean waters. The study published in the journal Global Change Biology finds that sea grass forests can raise pH levels in coastal waters. As they perform photosynthesis (光合作用), they remove carbon dioxide from the water, counteracting the acidifying effect of the gas.“I think we are all very excited about it,” said lead study author Aurora Ricart, a scientist at the Bigelow Laboratory for Marine Sciences.Ocean acidification is a side effect of rising carbon dioxide levels in the atmosphere. Some of this CO2 dissolves out of the air and into the sea, causing a chemical reaction that lowers the water’s pH. Scientists sometimes refer to it as global warming’s “evil twin”—an invisible companion to climate change.Ocean acidification can have harmful effects on marine organisms like shellfish and coral by preventing them from properly forming the hard shells they need to survive. It’s a threat both to natural ecosystems and to shellfish fisheries around the world. The study presents a natural way to address the problem.Researchers analyzed six years of data from sea grass meadows spanning more than 600 miles off the California coastline. It focused on the common eelgrass, one of the most widespread sea grass species on the West Coast. The authors claim it’s the largest, most comprehensive study of its kind.According to the study, sea grass ecosystems can raise pH levels by more than 0.1 unit, equivalent to about a 30% decrease in acidity. The effect isn’t constant. It comes in waves and is influenced by temperature, daylight, ocean currents and other factors that affect water chemistry and sea grass photosynthesis rates. But the tempering influence on acidification can be lasting, sometimes persisting for up to three weeks at a time. The study also shows that pH is higher in sea grass ecosystems, compared to nearby areas with no sea grass, about 65% of the time.The study didn’t investigate the effects of higher pH on marine organisms — that’s a question for future research. But there’s reason to believe these meadows may have a positive influence on shellfish and other ocean animals.21．What can we learn from this passage?A．Sea grass forests can lower pH levels of coastal waters.B．Shellfish and corals are not affected by ocean acidification.C．Sea grass meadows can help remove pollutants from the sea water.D．The effects of higher pH on marine organisms were investigated in this study.22．What does the underlined word “mitigate” in Paragraph 2 probably mean?A．Relieve.B．Present.C．Cause.D．Predict.23．Paragraph 7 tells us the research ________.A．findings B．process C．questions D．reflection24．Why does the author write this passage?A．To illustrate the serious situation of climate change.B．To present the living conditions of the underwater meadows.C．To emphasize the importance of research on marine pollution.D．To introduce a natural way to solve the problem of ocean acidification.（2023·北京延庆·统考一模）Making use of the wind, the water or, for more than half of all plant species, animals, plants disperse (散播) seeds far and wide. Frugivores — animals such as gibbons that feed on the fleshy fruits of plants — eat and then excrete (排泄) seeds away from the original tree. The African savanna elephant can carry seeds up to a record-breaking distance of 65 kilometres. This ability to shift geographical ranges will be crucial to plants when it comes to surviving climate change. However, just like all gibbon species, the African savanna elephant is endangered, its population down by 60 percent over the past 50 years.Researchers in Denmark and the USA have published a new study into how the loss of seed-dispersing animals could affect the resilience (恢复力) of forests and other natural ecosystems. According to their research, this loss has already reduced the ability of plants to move in pace with climate change by 60 percent, and in some areas by as much as 95 percent.Evan Fricke, lead author of the study, explains that in order to reach these results, they pulled together existing data from all previous studies and used machine learning to develop models that could estimate the seed dispersal potential of any animal, even ones that are now extinct.The researchers found that, historically, the decline of seed-dispersing animals has had the greatest influence on plants across the temperate (温带的) regions of North and South America, Europe and southern Australia. “Our temperate ecosystems have lost a lot of the natural seed-dispersal function that they would have had.” explains Fricke, referring to large mammals that were once widespread in these regions.Nevertheless, the poor conservation status of many seed-dispersing tropical animals puts plants in regions such as Southeast Asia and Madagascar most at risk today. Without the preservation of such animals, global seed dispersal could decline by a further 15 percent. “The direct implication of this decline is that many plant species will be unable to keep pace with a changing climate,” says Fricke. “That means the potential loss not only of plant biodiversity but of the ecosystem functions that those plants provide.”As wildlife is lost, plants can no longer adapt and survive and forests become less sustainable, which reduces the amount of carbon they can store. They also lose their ability to support wildlife. Whole ecosystems are disrupted. The conclusion, Fricke says, is clear: we must conserve currently endangered species and restore the populations of important seed dispersers. “Independent of climate change, rewilding has the potential to benefit our ecosystems, but in a changing climate, it has the added benefit of increasing the climate resilience of those ecosystems,” he says.25．The author mentions the African savanna elephant in Paragraph 1 is to ________.A．highlight the problem B．predict the endingC．express an opinion D．provide a solution26．What does Fricke conclude from the study?A．plants disperse seeds by way of animals excreting them.B．rewilding can promote the climate resilience of our ecosystems.C．seed-dispersing animals could hardly affect the natural ecosystems.D．the loss of seed-dispersing animals has little influence on temperate regions.27．Which would be the best title of the passage?A．The Resilience of EcosystemsB．The Conservation of Seed-dispersing AnimalsC．Animals That Spread Seeds Are Essential — And Under ThreatD．Animals That Spread Seeds Are Endangered — And Well Protected。

有关双减政策的优秀英语范文

有关双减政策的英语作文1The "Double Reduction" policy has brought significant positive changes to students' learning and lives. It is a remarkable initiative that has had a profound impact on the educational landscape.Firstly, students now have more time to pursue their hobbies and interests. They can engage in various activities such as painting, music, and sports, which not only enriches their lives but also helps them develop diverse skills and talents. For example, my classmate Tom used to struggle with a heavy study load but now he has ample time to practice playing the guitar and has even joined the school band.Secondly, the policy allows students to participate in outdoor activities. Spending time in nature, breathing fresh air, and exercising helps improve their physical and mental health. This has made students more energetic and enthusiastic in their daily lives.Moreover, the "Double Reduction" policy has promoted educational equity. It has reduced the reliance on excessive after-school tutoring, making quality educational resources more accessible to a wider range of students. This means that children from different backgrounds have an equal chance to receive a good education and develop their potential.In conclusion, the "Double Reduction" policy has been a boon forstudents, offering them a better balance between study and life, and fostering a more comprehensive and healthy development.2The double reduction policy has brought significant changes to family education. In the past, parents were often highly stressed and anxious about their children's academic performances, spending most of their time and energy supervising and pushing their children to study. However, with the implementation of this policy, a remarkable transformation has occurred.For instance, parents now have more quality time to spend with their children. They can engage in various activities together, such as going for walks in the park, playing board games at home, or having heart-to-heart conversations. This increased interaction not only strengthens the parent-child bond but also allows parents to better understand their children's thoughts and feelings.Another positive change is that parents have become less obsessed with their children's grades. Instead, they are placing greater emphasis on the physical and mental health of their children. They encourage children to participate in sports, hobbies, and social activities, which helps children develop a more balanced and healthy personality.In conclusion, the double reduction policy has had a profound and positive impact on family education. It has allowed families to focus more on nurturing well-rounded and happy children, rather than solely onacademic achievements. This shift is crucial for the healthy growth and development of children in the long run.3The Double Reduction Policy has brought about significant changes to school education. It places new demands on both teachers and schools. Teachers are now required to optimize their teaching methods to enhance the efficiency of the classroom. For instance, they can employ various interactive and engaging teaching strategies to ensure that students grasp the key points effectively.Moreover, schools need to organize more colorful and diverse club activities to meet the diverse needs of students. This can include art clubs, sports clubs, and science clubs. These activities not only enrich students' school life but also help them develop their interests and talents.For example, our school has set up a robotics club, where students can learn programming and mechanical knowledge. They can also participate in competitions to showcase their skills and creativity. Such activities provide students with practical opportunities and cultivate their comprehensive abilities.In conclusion, the Double Reduction Policy is a positive step forward. It forces schools and teachers to think innovatively and provide a better educational environment for students. Only in this way can students truly enjoy the process of learning and grow into all-round individuals.The Double Reduction Policy has brought about significant changes and advancements in the education sector. It has compelled educational training institutions to transform and focus more on quality-oriented education. For instance, many such institutions have shifted their emphasis from purely academic tutoring to developing students' comprehensive skills like art, music, and sports. This transformation not only enriches students' extracurricular lives but also helps them cultivate diverse talents.Moreover, the policy encourages educational innovation and promotes the integration of online and offline education. With the advancement of technology, online education has become increasingly popular. It allows students to access quality educational resources anytime and anywhere, breaking the limitations of time and space. At the same time, offline education provides face-to-face interaction and practical experiences, which are irreplaceable. The combination of the two forms creates a more flexible and efficient learning environment.In conclusion, the Double Reduction Policy is a powerful driving force for the development of the education industry. It has forced educational institutions to adapt and innovate, ultimately benefiting students and promoting the overall improvement of the education system.The "Double Reduction" policy, aiming to alleviate the excessive academic burden on students, has brought significant changes to the education landscape. However, its implementation is not without challenges. One of the main concerns is how to ensure the effective implementation of the policy. For instance, some educational institutions might be tempted to covertly continue the old practices of excessive tutoring and homework. To address this, strict supervision and penalties should be imposed. Regular inspections and random checks can help maintain the integrity of the policy.Another issue is the anxiety of some parents. They worry that with reduced academic pressure, their children might fall behind. To alleviate these concerns, schools and the government need to provide more comprehensive and transparent information about the benefits of the policy. They could organize parent meetings and workshops to explain how students can develop better under a more balanced educational environment.Moreover, it is essential to enhance the quality of school education. Teachers should be provided with sufficient training and resources to design engaging and effective lessons. Extra-curricular activities should also be diversified and improved to cultivate students' comprehensive abilities.In conclusion, while the "Double Reduction" policy holds great promise, addressing these challenges through concerted efforts from all stakeholders is crucial for its successful implementation and the well-being of students.。

《中外教育交流英语》段落翻译

UNIT 1 CHOOSING AN IDEAL UNIVERSITY1.在选择学校时，为什么会把费用放在第一位呢？因为费用是留学的客观条件之一。

大部分家庭都会把费用问题作为首要筛选条件，从中选择符合自身条件的国家，适合的城市，乃至学校。

Why is the admission considered as the first priority in the selection of the school? Because the tuition is one of the essential conditions when studying abroad. Most of the families would put financial issue as the primary consideration, which helps them to choose the right country, city or even school.2.托福、雅思的考试分数也是择校的重要条件之一。

很多学生先准备成绩，后申请大学。

对于大部分学生而言，获得良好的分数也是必要条件之一。

The grade of TOEFL or IELTS is also one of the important premises. Before applying schools, many student have already got the English grade. Therefore a better grade on English is one of the essential conditions for most students.3.在美国，比较热门的专业有以下几个：统计，金融，市场营销，土木工程，航天科技，生物科学等。

如果考虑到职业规划的问题，这些专业是很不错的选择。

In US, the popular majors are statistics, finance, marketing, architecture, astronautics and bio-technology. If you are thinking about your career plan, those specialties are recommended.UNIT 2 APPLYING TO THE TARGETED UNIVERSITY 1.研究生阶段的学习通常是为了获得一个硕士或博士学位，而一些大学也提供研究生文凭课程。

供应商合规性管理加强对供应商的合规审计

供应商合规性管理加强对供应商的合规审计随着全球化经济的发展，供应链管理越来越受到重视。

在供应链管理中，供应商是一个关键角色，他们向企业提供原材料、零部件和服务。

然而，由于供应商的规模、地理位置和文化背景的差异，他们的合规性可能面临着一系列的挑战。

因此，加强对供应商的合规审计成为了供应商合规性管理的重要方面。

供应商合规审计是指通过评估和监控供应商的活动，确保其符合公司的政策、法规和行业标准。

这不仅有助于减少风险，保护公司声誉，还能促进业务伙伴之间的合作和信任。

在进行供应商合规审计时，有几个关键方面需要关注。

首先，合规政策。

企业应该制定明确的合规政策，明确规定供应商应遵循的法规、行业标准和道德要求。

该政策应与供应商签订合同时进行明确的沟通，确保供应商清楚了解他们需要履行的义务。

其次，供应商评估。

企业应建立一个供应商评估体系，评估供应商的合规性、质量控制、环境和社会责任等方面。

这个体系应包括定期评估的过程和标准，以及评估结果的记录和追踪。

第三，合规审核。

企业可以定期或随机进行供应商的合规审核。

这些审核可以由内部审计团队或第三方机构进行。

合规审核可以涵盖供应商的财务记录、劳工关系、环境政策和安全措施等方面。

任何不符合合规要求的问题都应及时解决。

第四，供应商合作。

合规审计不仅仅是一种监督和惩罚机制，也是一种与供应商合作的机会。

企业应该与供应商建立长期合作关系，共同制定供应链的合规标准，并提供培训和支持，帮助供应商提高合规水平。

最后，持续改进。

合规审计是一个持续改进的过程。

企业应该根据合规审计的结果，及时进行调整和改进，进一步提高供应商合规性管理的效力。

同时，企业还应通过跟踪和共享供应商的合规数据，为其他企业提供有关供应商合规的参考和经验。

总之，供应商合规性管理加强对供应商的合规审计是一个关键的战略决策。

通过合规审计，企业可以减少风险，保护声誉，增强供应链的可持续性。

目标是建立一个合规性强、透明开放的供应链网络，为企业的可持续发展提供稳定的支持。

《统计学基础(英文版·第7版)》题库答案les7e_ptb_01

Chapter 1Introduction to Statistics1.1An Overview of Statistics1Distinguish Between a Population and a SampleMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Determine whether the data set is a population or a sample.1)The age of every fourth person entering a department storeA)sample B)population2)The age of each employee at a local grocery storeA)population B)sampleSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Identify the population and the sample.3)A survey of 1212 American households found that 52% of the households own a computer.4)When 1348 American households were surveyed, it was found that 57% of them owned two cars.5)A survey of 2625 elementary school children found that 28% of the children could be classified as obese. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.Use the Venn diagram to identify the population and the sample.6)A)Population: Magazine subscribers; Sample; Magazine subscribers who renew their subscriptionB)Population: Magazine subscribers who renew their subscription; Sample: Magazine subscribers2Distinguish Between a Parameter and a StatisticSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Determine whether the numerical value is a parameter or a statistic. Explain your reasoning.7)A recent survey by the alumni of a major university indicated that the average salary of 10,500 of its 200,000graduates was $130,000.8)The average salary of all assembly-line employees at a certain car manufacturer is $44,000.9)A survey of 1040 students was taken from a university with 19,000students.3Distinguish Between Descriptive Statistics and Inferential StatisticsMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Identify whether the statement describes inferential statistics or descriptive statistics.10)The average age of the students in a statistics class is 20years.A)descriptive statistics B)inferential statistics11)The chances of winning the California Lottery are one chance in twenty-two million.A)inferential statistics B)descriptive statistics12)There is a relationship between smoking cigarettes and getting emphysema.A)inferential statistics B)descriptive statistics13)From past figures, it is predicted that 31% of the registered voters in California will vote in the June primary.A)inferential statistics B)descriptive statistics14)Based on previous clients, a marriage counselor concludes that the majority of marriages that begin withcohabitation before marriage will result in divorce.A)inferential statistics B)descriptive statistics4ConceptsSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Provide an appropriate response.15)Explain the difference between a sample and a population.16)If you had to do a statistical study, would you use a sample or a population? Why?1.2Data Classification1Distinguish Between Qualitative and Quantitative DataMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Determine whether the data are qualitative or quantitative.1)the colors of automobiles on a used car lotA)qualitative B)quantitative2) the number of complaint letters received by the United States Postal Service in a given dayA)quantitative B)qualitative3)the number of seats in a movie theaterA)quantitative B)qualitative4)the numbers on the shirts of a girlʹs soccer teamA)qualitative B)quantitative2Classify Data with Respect to the Four Levels of MeasurementMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Identify the data setʹs level of measurement.5)hair color of women on a high school tennis teamA)nominal B)ordinal C)interval D)ratio6)numbers on the shirts of a girlʹs soccer teamA)nominal B)ordinal C)interval D)ratio7)ages of students in a statistic classA)ratio B)ordinal C)interval D)nominal8)temperatures of 26 selected refrigeratorsA)interval B)ordinal C)nominal D)ratio9)number of milligrams of tar in 30 cigarettesA)ratio B)ordinal C)interval D)nominal10)number of pages in your statistics bookA)ratio B)ordinal C)interval D)nominal11)marriage status (married, single, or divorced) of the faculty at the University of ColoradoA)nominal B)ordinal C)interval D)ratio12)list of 1240 social security numbersA)nominal B)ordinal C)interval D)ratio13)the ratings of a movie ranging from ʺpoorʺ to ʺgoodʺ to ʺexcellentʺA)ordinal B)nominal C)interval D)ratio14)the final grades (A, B, C, D, and F) for students in a statistics classA)ordinal B)nominal C)interval D)ratio15)the annual salaries for all teachers in CaliforniaA)ratio B)ordinal C)interval D)nominal16)list of zip codes for ChicagoA)nominal B)ordinal C)interval D)ratio17)the nationalities listed in a recent survey (for example, Asian, European, or Hispanic).A)nominal B)ordinal C)interval D)ratio18)the amounts of fat (in grams) in 37 cookiesA)ratio B)ordinal C)interval D)nominal19)the years the summer Olympics were held in the United StatesA)interval B)ordinal C)nominal D)ratio20)numbers of touchdowns scored by a major university in five randomly selected games53134A)ratio B)ordinal C)interval D)nominal21)the average daily temperatures (in degrees Fahrenheit) on five randomly selected days2123213027A)interval B)nominal C)ordinal D)ratio22)manuscripts rated ʺacceptableʺ or ʺunacceptableʺA)ordinal B)nominal C)ratio D)interval23)the lengths (in minutes) of the top ten movies with respect to ticket sales in 2007A)ratio B)nominal C)ordinal D)interval24)the data listed on the horizontal axis in the graphA)nominal B)interval C)ordinal D)ratio25)the data listed on the horizontal axis in the graphA)ratio B)nominal C)ordinal D)interval3ConceptsSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Provide an appropriate response.26)Explain the differences between the interval and ratio levels of measurement.27)Explain why data expressed with the Celsius temperature scale is at the interval level of measurement ratherthan the ratio level.1.3Data Collection and Experimental Design1Decide Whether Study is Observational Study or ExperimentMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.Determine whether the study is an observational study or an experiment.1)A medical researcher obtains a sample of adults suffering from diabetes. She randomly assigns 89people to atreatment group and 89 to a placebo group. The treatment group receives a medication over a period of threemonths and the placebo group receives a placebo over the same time frame. At the end of three months thepatientsʹ symptoms are evaluated.A)experiment B)observational study2)A poll is conducted in which professional musicians are asked their ages.A)observational study B)experiment3)A pollster obtains a sample of students and asks them how they will vote on an upcoming referendum.A)observational study B)experiment4)The personnel director at a large company would like to determine whether the company cafeteria is widelyused by employees. She calls each employee and asks them whether they usually bring their own lunch, eat at the company cafeteria, or go out for lunch.A)observational study B)experiment5)A scientist was studying the effects of a new fertilizer on crop yield. She randomly assigned half of the plots ona farm to group one and the remaining plots to group two. On the plots in group one, the new fertilizer wasused for a year. On the plots in group two, the old fertilizer was used. At the end of the year the average cropyield for the plots in group one was compared with the average crop yield for the plots in group two.A)experiment B)observational study6)A researcher obtained a random sample of 100 smokers and a random sample of 100 nonsmokers. Afterinterviewing all 200 participants in the study, the researcher compared the rate of depression among thesmokers with the rate of depression among nonsmokers.A)observational study B)experimentSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Identify the experimental units and treatments used in the experiment.7)A medical researcher center wants to test the effectiveness of a new diabetes medication. The companyidentifies 98 adults suffering from a similar form of diabetes. The subjects are randomly assigned to twogroups. One group is given a medication and the other is given a placebo that looks exactly like the medication.After three months, the subjectsʹ symptoms are studied and compared.2Identify a Biased SampleSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Provide an appropriate response.8)Explain what bias there is in a study done entirely online.9)A report sponsored by the California Citrus Commission stated that cholesterol levels can be lowered bydrinking at least one glass of a citrus product each day. Determine if the report is biased.10)A local newspaper ran a survey by asking, ʺDo you support the deployment of a weapon that could killmillions of innocent people?ʺ Determine whether the survey question is biased.3Identify Sampling TechniquesMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.Identify the sampling technique used.11)Thirty-five sophomores, 30 juniors and 33seniors are randomly selected from 281 sophomores, 242juniors and529 seniors at a certain high school.A)stratified B)random C)cluster D)convenience E)systematic12)Every fifth person boarding a plane is searched thoroughly.A)systematic B)random C)cluster D)convenience E)stratified13)At a local community college, five statistics classes are randomly selected out of 20 and all of the students fromeach class are interviewed.A)cluster B)random C)convenience D)systematic E)stratified14)A researcher randomly selects and interviews fifty male and fifty female teachers.A)stratified B)random C)cluster D)convenience E)systematic15)A researcher for an airline interviews all of the passengers on five randomly selected flights.A)cluster B)random C)convenience D)systematic E)stratified16)A community college student interviews everyone in a particular statistics class to determine the percentage ofstudents that own a car.A)convenience B)random C)cluster D)systematic E)stratified17)Based on 12,500 responses from 36,500questionnaires sent to its alumni, a major university estimated that theannual salary of its alumni was $116,000 per year.A)random B)stratified C)cluster D)convenience E)systematic18)In a recent television survey, participants were asked to answer ʺyesʺ or ʺnoʺ to the question ʺAre you in favorof the death penalty?ʺ Six thousand five hundred responded ʺyesʺ while 4700 responded ʺnoʺ. There was afifty-cent charge for the call.A)convenience B)random C)cluster D)stratified E)systematic19)A lobbyist for a major airspace firm assigns a number to each legislator and then uses a computer to randomlygenerate ten numbers. The lobbyist contacts the legislators corresponding to these numbers.A)random B)convenience C)cluster D)stratified E)systematic20)To ensure customer satisfaction, every 20th phone call received by customer service will be monitored.A)systematic B)random C)cluster D)stratified E)convenience21)A market researcher randomly selects 400 drivers under 45years of age and 100 drivers over 45years of age.A)stratified B)random C)cluster D)convenience E)systematic22)To avoid working late, the quality control manager inspects the last 60 items produced that day.A)convenience B)random C)cluster D)stratified E)systematic23)The names of 70 contestants are written on 70 cards. The cards are placed in a bag, and three names are pickedfrom the bag.A)random B)stratified C)cluster D)convenience E)systematic24)A researcher randomly selected 70 of the nationʹs middle schools and interviewed all of the teachers at eachschool.A)cluster B)random C)stratified D)convenience E)systematicSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Provide an appropriate response.25)After a hurricane, a disaster area is divided into 200 equal grids. Thirty of the grids are selected and everyoccupied household in the grid is interviewed to help focus relief efforts. Select the numbers of the first fivegrids that belong to the cluster sample.163487693890169513925588771015092097915726)There are 750 incoming freshmen attending a university this fall. A researcher wishes to send questionnaires toa sample of 30 of them to complete regarding their drinking habits. Select the numbers of the first fivefreshmen who belong to the simple random sample.163487693890169513925588771015092097915727)A college employs 85 faculty members. Without replacement, select the numbers of the five members who willserve on the tenure committee next year.163487693890169513925588771015092097915728)Of the 5000 outpatients released from a local hospital in the past year, one hundred were contacted and askedtheir opinion on the care they received. Select the first five patients who belong to the simple random sample.1634876938901695139255887710150920979157MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Determine whether you would take a census or use a sampling. If you would use a sampling, decide what sampling technique you would use.29)The average age of the 80 residents of an assisted living center.A)census B)random sampling C)stratified sampling D)cluster sampling 4ConceptsSHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.Provide an appropriate response.30)Explain the differences between cluster sampling and stratified sampling.31)Explain the difference between a census and a sampling and describe the advantages and disadvantages ofeach.Chapter 1Introduction to StatisticsAnswer Key1.1An Overview of Statistics1Distinguish Between a Population and a Sample1)A2)A3)population: collection of all American households; sample: collection of 1212American households surveyed4)population: collection of all American households; sample: collection of 1348American households surveyed5)population: elementary school children; sample: collection of 2625 elementary school children surveyed.6)A2Distinguish Between a Parameter and a Statistic7)It describes a statistic because the number $130,000is based on a subset of the population.8)It describes a parameter because the $44,000is based on all the workers at the car manufacturer.9)It describes a statistic because the number 1040is based on a subset of the population.3Distinguish Between Descriptive Statistics and Inferential Statistics10)A11)A12)A13)A14)A4Concepts15)A population is the collection of all outcomes, responses, measurements, or counts that are of interest.. A sample is asubset of a population.16)A sample would be used. It is usually impractical to obtain all the population data.1.2Data Classification1Distinguish Between Qualitative and Quantitative Data1)A2)A3)A4)A2Classify Data with Respect to the Four Levels of Measurement5)A6)A7)A8)A9)A10)A11)A12)A13)A14)A15)A16)A17)A18)A19)A20)A21)A22)A23)A24)A25)A3Concepts26)Data at the ratio level are similar to data at the interval level, but with the added property that a zero entry is aninherent zero (implies ʺnoneʺ). Also, for data at the ratio level a ratio of two data values can be formed so that one data value can be expressed as a multiple of another.27)Such data is at the interval level rather than the ratio level because the temperature of 0°C does not represent acondition where no heat is present, so it is not an inherent zero as required by the ratio level. Also, ratios of twotemperatures cannot be formed so that one data value is expressed as a multiple of the other. The temperature 2°C is not twice as warm as 1°C.1.3Data Collection and Experimental Design1Decide Whether Study is Observational Study or Experiment1)A2)A3)A4)A5)A6)A7)The experimental units are the 98 adults in the study. The treatment is the new diabetes medication.2Identify a Biased Sample8)The study may be biased because it is limited to people with computers.9)A report sponsored by the citrus industry is much more likely to reach conclusions favorable to the industry.10)The wording of the question is biased, as it tends to encourage negative responses.3Identify Sampling Techniques11)A12)A13)A14)A15)A16)A17)A18)A19)A20)A21)A22)A23)A24)A25)163, 169, 15, 92, 9726)163, 487, 693, 169, 51327)16, 34, 69, 38, 1328)1634, 3890, 1695, 1392, 150929)A4Concepts30)In stratified sampling, members of the population are divided into two or more subsets, or strata, that share a similarcharacteristic. A sample is then randomly selected from each of the strata. A stratified sample has members from each segment of the population. In cluster sampling, the population is divided into naturally occurring subgroups, each having similar characteristics. All of the members in one or more (but not all) of the clusters are then selected. In a cluster sample, care must be taken to ensure that all clusters have similar characteristics.31)A census is a count or measure of an entire population, while a sampling is a count or measure of part of a population.A census provides complete information but is often expensive, difficult, and time consuming to perform especially ifthe population is large. A sampling is less expensive and time consuming, however appropriate sampling techniques must be used to ensure that unbiased data are collected and that the sample is representative of the population. Even with the best sampling methods, sampling error can occur.。

随机分组英语作文

随机分组英语作文全文共3篇示例，供读者参考篇1Random grouping is a common practice in various educational settings, from classrooms to workshops. This method of dividing students or participants into groups without any specific criteria can have both advantages and disadvantages.One of the main advantages of random grouping is that it can help foster a sense of inclusivity and fairness. By taking away the possibility of bias or favoritism in the group formation process, random grouping ensures that every individual has an equal chance of working with different peers. This can be especially important in diverse environments where different backgrounds and skill levels may be present. Random grouping also encourages students to interact with a wider range of peers, leading to increased socialization and collaboration skills.Another benefit of random grouping is that it can help break up cliques and encourage new relationships to form. In a classroom or workshop where students tend to stick to theirfamiliar group of friends, random grouping can mix things up and create opportunities for students to work with people they may not have interacted with otherwise. This can lead to a more inclusive and supportive learning environment where everyone feels like they have a role to play.However, random grouping also has its drawbacks. One of the main disadvantages is that it can result in uneven distribution of skills and abilities within groups. Without considering factors such as academic performance or prior knowledge, random grouping may lead to some groups being more capable or successful than others. This can create frustration and resentment among group members, especially if they feel like they are being held back by their peers.Additionally, random grouping may not always result in the most effective or efficient teams. When individuals with vastly different working styles or communication preferences are placed together randomly, it can be challenging for the group to find common ground and work effectively towards a shared goal. This can lead to conflicts and misunderstandings that hinder the group's progress.In conclusion, while random grouping can have its benefits in promoting inclusivity and breaking up cliques, it is importantto consider the potential drawbacks and limitations of this method. Educators and facilitators should be mindful of the challenges that may arise from random grouping and strive to create balanced and effective groups that can support the learning and development of all individuals involved. By striking a balance between randomness and consideration of individual differences, random grouping can be a valuable tool for promoting collaboration and socialization in educational settings.篇2Random Grouping in English ClassIn today's dynamic and changing world, the traditional method of teaching English has also evolved to adapt to the needs of modern learners. One of the techniques that has gained popularity in recent years is random grouping in English class. This method involves dividing students into groups randomly for various activities and tasks, providing them with the opportunity to work and interact with different classmates.There are several benefits to using random grouping in English class. Firstly, it promotes diversity and inclusivity in the classroom. By mixing students from different backgrounds,cultures, and learning styles, random grouping encourages collaboration and understanding among diverse groups of learners. This not only enhances students' communication skills but also fosters a sense of empathy and respect for others.Secondly, random grouping helps to improve students' social skills. Working with different classmates on a regular basis allows students to develop important social skills such as teamwork, leadership, and conflict resolution. They learn how to communicate effectively with others, listen to different perspectives, and compromise when necessary. These skills are essential for success in the workplace and in life in general.Another benefit of random grouping in English class is that it promotes creativity and innovation. When students are exposed to different ideas and perspectives from their classmates, they are more likely to think outside the box and come up with innovative solutions to problems. This can lead to more engaging and meaningful learning experiences for students, as they are encouraged to explore new ways of thinking and expressing themselves.Furthermore, random grouping in English class can also help to improve academic performance. Research has shown that students who work in diverse groups tend to perform betteracademically compared to those who work in homogenous groups. This is because diverse groups often bring a wider range of skills, knowledge, and perspectives to the table, which can lead to more comprehensive and well-rounded solutions to problems.Despite the many benefits of random grouping in English class, there are also some challenges that teachers may face when implementing this method. For example, some students may feel uncomfortable working with classmates they are not familiar with, which can lead to resistance or lack of participation. Teachers must create a supportive and inclusive classroom environment to help students overcome their reservations and feel more at ease working with different groups.Additionally, random grouping may not always be suitable for every learning activity or task. Some activities may require students to work with specific partners or in predetermined groups to achieve the learning objectives effectively. Teachers need to assess the nature of the task and the needs of the students before deciding whether random grouping is the most appropriate strategy to use.In conclusion, random grouping in English class is a valuable teaching technique that can benefit students in many ways. Bypromoting diversity, social skills, creativity, and academic performance, random grouping provides students with the opportunity to learn and grow in a dynamic and inclusive learning environment. Teachers play a crucial role in creating a supportive and engaging classroom atmosphere that encourages students to collaborate and excel in their English studies. By embracing random grouping as a teaching strategy, educators can help students develop the skills and confidence they need to succeed in an increasingly interconnected and diverse world.篇3Random grouping is a common practice in many educational settings, especially in language learning classes. It is a method of dividing students into groups based on chance rather than any predetermined criteria. This can be a rewarding experience for students as it promotes cooperation, communication, and diversity among classmates.One of the main benefits of random grouping is that it helps students break out of their comfort zones and interact with different individuals. This can be particularly beneficial in language learning classes where students may be more inclined to speak with their friends or those who speak the same nativelanguage. Random grouping forces students to communicate with classmates they may not have spoken to before, encouraging them to use the target language and expand their vocabulary.Furthermore, random grouping promotes diversity within the classroom. By mixing students from different backgrounds, cultures, and language levels together, it creates a rich learning environment where students can learn from each other's experiences and perspectives. This diversity can lead to more engaging discussions, as students bring a variety of viewpoints to the table.Random grouping also fosters teamwork and collaboration among students. Working with different classmates on group projects or activities can help students develop important skills such as communication, problem-solving, and leadership. It also encourages students to support and help each other, creating a sense of camaraderie and community within the classroom.However, random grouping does come with its challenges. Some students may feel uncomfortable or anxious about working with classmates they are not familiar with. To address this, teachers can create a supportive and inclusive environment where students feel respected and valued. Teachers can alsoprovide guidance and support to help students navigate group dynamics and communicate effectively with their peers.In conclusion, random grouping can be a valuable strategy in language learning classes. By promoting cooperation, communication, and diversity among students, it can enhance the learning experience and help students develop important skills for the future. As teachers, we should embrace random grouping as a way to create a dynamic and engaging classroom environment where all students have the opportunity to learn and grow.。

学校的规矩和家里的规矩,英语作文

学校的规矩和家里的规矩,英语作文全文共3篇示例，供读者参考篇1School Rules vs Home RulesAs a student, I live in two different worlds with different sets of rules – the world of school and the world of home. While some of the rules overlap, there are also many distinct differences between what is expected of me at school versus at home. Let me take you through some of the key contrasts.At school, the rules are extensive, formal, and apply equally to all students. We have a thick student handbook that covers everything from dress codes to attendance policies to rules about using phones and electronics. The dress code, for example, prohibits certain types of clothing like anything showing too much skin, ripped jeans, pajamas, or clothing with offensive language or images. At home, my parents are a lot more relaxed about what I wear around the house as long as it's decent.The attendance policies at school are also very strict. If you miss too many days, even with an excuse note from a parent, you canface consequences like detention or being disqualified fromextracurricular activities. My parents obviously want me to go to school, but if I'm legitimately sick or we have a family obligation, they don't penalize me for missing a day like the school does.Using phones is another area where the school rules are much tighter. Phones have to be off and put away during class time unless we are specifically using them for an educational purpose approved by the teacher. At home, my parents don't really limit my phone usage as long as I'm being responsible, getting my homework and chores done, and not staying on it late into the night.In terms of behavior, school rules prohibit anything disruptive, disrespectful, or dangerous like fighting, bullying, plagiarizing, cheating on tests, skipping class, and damaging property. Violations result in disciplinary actions from teachers and administrators. While my parents obviously don't condone those types of behaviors either, the consequences at home would be different, like losing phone/gaming privileges or being grounded.The school also has a lot of protocols in place for safety and emergency situations. We have regular fire drills, lockdown drills, severe weather drills, and procedures for things like medical emergencies, bomb threats, or confronting an intruder. Myparents have their own emergency plans for our home as well, but they aren't as formalized or practiced as the school's protocols.In general, I feel like the school rules are more rigid, impersonal, and one-size-fits-all compared to the rules and expectations at home. At school, breaking rules results in very clear, predetermined consequences laid out in the handbook. At home, my parents' rules tend to be more tailored to me as an individual, take more context and circumstances into account, and the consequences vary based on the situation.That's not to say that one environment has better or worse rules than the other – they just stem from different contexts, purposes, and authority figures. The school needs to maintain order, safety, and an environment conducive to learning for hundreds or thousands of students. My parents篇2School Rules vs Home RulesBeing a student means having to follow a lot of rules - at school and at home. Rules are supposed to keep things running smoothly and keep people safe, but sometimes they can feel really restrictive and unfair. There are a lot of similarities but alsosome big differences between the rules at school and the rules at home.At school, the rules are really strict and clearly laid out in the student handbook. You have to follow a dress code, only wear certain types of clothing and shoes. You can't have any visible tattoos or crazy hair colors or styles. You have to be at school on time every day and go to all your classes. If you are late or skip class, you get in trouble. There are also a ton of rules about behavior - no running in the halls, no cursing or bullying, no fighting, no using phones during class, and so on. Break any of those rules and you could get detention or even suspension.The school rules apply to everyone equally, whether you are a straight-A student or a troublemaker. The punishments are also pretty standardized - if you get caught skipping class, you get the same punishment as anyone else who skips class. It doesn't matter if you had a good excuse or not. The rules are strict but they are at least consistent and fair across the board.At home, the rules can be a lot more relaxed in some ways but also strangely strict in other ways. My parents aren't too worried about what clothes I wear or what my hair looks like, as long as it's not something completely inappropriate or offensive. The curfew rules about what time I have to be home vary a lotbased on my parents' moods and whether they feel like I've been behaving well lately. If I get in trouble at school, I'm sure to get disciplined twice as hard when I get home.The punishments at home also aren't really standardized. If I forget to do my chores one day, I might just get a warning. But if I forget a few days in a row, I could get grounded for a week or longer. It really depends on how strict my parents are feeling in that moment and how angry I've made them. The same offense can get vastly different punishments depending on the circumstances.That's one of the biggest differences - school rules have to be enforced consistently due to liability and fairness concerns. But at home, my parents can really make up the rules as they go based on their personal expectations and preferences for me. They have a lot more leeway to make exceptions or goeasier/harder on me at their discretion.Another difference is that at school, once you serve your detention or suspension, your record gets wiped clean. You don't remain in trouble or keep getting punished further for the same offense. But at home, my parents like to keep bringing up past mistakes over and over if I do something else wrong. Theconsequences seem to just keep carrying over and piling up at home much more than at school.At the same time, home rules can be a lot more relaxed and personal than school rules in some ways. At school, the rules are designed with safety and learning as the top priorities. You can't do anything that could be even remotely dangerous or disruptive to the classroom environment. But at home, as long as I'm not doing anything truly harmful, my parents will be more relaxed about things like watching too much TV, spending too much time online, or staying up past curfew occasionally.My parents also make some exceptions around things that are important to me personally. For example, at school I have to put my phone away completely during class. But at home, my parents will occasionally allow me to spend more time on the phone if I've had a really big assignment to work on. They realize that giving me a little personal flexibility can help relieve my stress as a student.That personal flexibility is a big difference compared to the rigidity of school rules and how they are applied to everyone equally without much room for exceptions based on circumstances. The staff and teachers have to be impartial and fair across thousands of students. My parents only have to worryabout a handful of us kids, so they can be more subjective about the rules.I guess the bottom line is that school rules make more sense for an institutional environment with a huge number of people. Having clear, standardized rules and consequences helps maintain order and avoids allegations of discrimination or unfair treatment. The rules have to be enforced impartially and consistently by the staff.But at home, my parents can afford to be a bit more relaxed and subjective with the rules because our household is much smaller and more personal. They get to decide what's most important based on our specific family values and what they think is best for nurturing my personal growth. As long as the basic expectations are clear, they can pick and choose which rules to strictly enforce and which ones to be more flexible about based on individual circumstances.I have a love/hate relationship with both sets of rules. The school rules can feel really oppressive and make me feel like I'm being treated like a little kid with no freedom. But I also understand that having those clear standards is important for keeping a huge school environment under control. The home rules drive me crazy because they can feel so inconsistent andunfair, with my parents constantly shifting the goal posts and dishing out random punishments based on their moods. But I'm also grateful for the personal flexibility my parents give me compared to the rigidity of school policies.I guess dealingwith different rules and different rule-makers is just one of the challenges of being a student trying to get through each day. We have to follow the comprehensive school rules during classroom hours, then come home and follow a different, more subjective set of rules in the evenings. We're constantly switching between different expectations and models of discipline. No wonder students are so stressed out all the time! But understanding the reasons behind the differences in rules at school and home helps give me a little more patience in navigating both worlds. Now if only I could get my parents and teachers on the same page...篇3The Rules of School vs. The Rules at HomeAs a student, I live in two very different worlds with their own distinct sets of rules and expectations. At school, there is a clear hierarchy and structure with many regulations that must be followed. However, at home with my family, the rules are oftenmore relaxed and informal. While there are some similarities, the contrasts between these two environments are quite stark.School is essentially a small society with its own laws and governance. From the moment I step through the doors each morning, I'm bound by numerous policies. There are strict dress codes that prohibit certain types of clothing deemed inappropriate or distracting. My high school doesn't allow ripped jeans, short skirts or shorts, or tops that are too revealing. We also can't have any clothing with offensive language or imagery. While these rules around attire may seem overly restrictive to some, they are intended to maintain a focused, respectful learning environment.The regulations don't stop there. Every classroom has its own set of rules about things like when we can use our phones, policies around tardiness and bathroom breaks, how we should behave and treat each other, and even guidance on how we should arrange our desks and workspace. Teachers implement different standards in their rooms and we have to be aware of and follow each instructor's particular policies. It can certainly get confusing juggling all the varying expectations.Beyond the classroom, there are overarching school rules that govern our conduct in hallways, the cafeteria, gymnasium,library, and all common areas. These spaces have guidelines around things like how we should walk and talk, restrictions on eating or drinking, policies about displaying affection or physical contact, and rules prohibiting running, horseplay, or causing any disturbances. Essentially, there are scores of rules that dictate just about every aspect of our behavior and movement while at school.Violating any of these rules can result in punishments ranging from warnings to detentions to suspensions or even expulsion for more serious offenses. The administration takes rule infractions seriously as they aim to provide a safe, structured environment conducive to learning. Therefore, we are constantly reminded of the need to be aware of and follow the myriad regulations.In stark contrast, my home life is far more relaxed and informal when it comes to rules and governance. My parents set some basic guidelines, primarily around things like curfews, screen time limits, household chores, and treating family members with respect. However, their enforcement and the consequences for violations are usually less rigid than at school.For example, while my school has policies dictating the types of clothing we can wear, at home I'm free to dress how I wantwithin reason. My parents just ask that I avoid anything too revealing or with profanity or offensive imagery when we have guests over. However, for just being around the house with family, they are much more relaxed about my wardrobe choices compared to the strict dress codes at school.The same goes for things like using my phone or watching TV. At school, those activities are heavily restricted. But at home, while my parents set some screen time limits, they are more lenient and flexible, recognizing the phone and TV are a part of modern life and teen social interaction. As long as I'm getting my homework and chores done in a timely manner, they don't crack down as harshly on those activities compared to the school environment.Even rules around things like curfews, household responsibilities, and common courtesy tend to be more loosely interpreted at home versus school. My parents are certainly stricter about me being home at a reasonable hour on school nights and having core chores like cleaning my room and taking out the trash done consistently. But they are more easy-going when it comes to things like minor tardiness on a weekend curfew or occasionally skipping a chore if I'm overwhelmed with school commitments.At the end of the day, my parents seem to value keeping some basic guidelines in place around major responsibilities and respecting others, but they allow much more freedom and autonomy within those broader principles compared to the rigidity of school rules. They recognize I'm becoming a young adult and need to start taking more ownership over my conduct and choices without the constant, heavy hand of strict policies and consequences looming over me at all times.Of course, there are some areas where the rules at home could be considered stricter than school policies. For example, my parents tend to be more diligent about me keeping up with household chores and responsibilities compared to school. They also crack down harder on any sort of rude, disrespectful behavior towards family members versus how such conduct might be handled in the classroom.Additionally, there is less tolerance at home for letting my performance slide in key areas like getting good grades or doing my homework. At school, teachers tend to focus more on daily habits and behaviors rather than the end result as long as I'm putting in decent effort along the way. If I do poorly on an assignment or test, teachers are usually happy as long as I tried my best and am staying engaged. But my parents put muchmore emphasis on the outcome. If my grades start to slip, they will come down harder enforcing more stringent rules around things like screen time or social activities until my performance improves.So while school is a domain governed by countless rigid policies and procedures around every aspect of student life, the home front is more of a principled approach with broader guidelines but more leeway and flexibility in how they are implemented and enforced. It's really two contrasting worlds that I have to deftly navigate on a daily basis.There are certainly pros and cons to the differing rule sets in these environments. The school's multitude of regulations can sometimes feel overly controlling and restrictive. It often feels like my entire life is micromanaged under a constant cloud of monitoring and harsh penalties for even slight missteps. This can breed resentment and motivate some students to rebel or disengage.However, I also recognize that schools need to maintain strict policies to create an environment conducive to learning and prepare us for the professional world after graduation where we will have to follow workplace rules and procedures. The clear boundaries, consistency, and accountability help instill disciplineand respect for authority. These are important values that will serve us well in our future careers.In contrast, the more relaxed guidelines and flexibility at home helps build personal responsibility, autonomy, andself-governance over time. Rather than harsh punishments for minor transgressions, my parents allow me to learn through natural consequences and mistakes. This approach helps develop intrinsic motivation, resilience, and emotional maturity that the school's external discipline system doesn't necessarily cultivate as effectively.So while school and home governance models differ vastly, there are merits to both systems. The restrictions and oversight at school enforce important life skills around discipline, respect, accountability, and following rules that will benefit us professionally down the road. Meanwhile, the autonomy and trust afforded at home helps build critical emotional intelligence and independence that is also essential for becoming a healthy, well-adjusted adult.As I continue to navigate these two contrasting spheres of rules and expectations, I'm learning how to adapt my behavior and mindset based on my current environment. I recognize that schools need their comprehensive rule books to maintain orderand facilitate an effective learning atmosphere. And I understand my parents allow more freedom at home as a way to foster responsibility and prepare me for independence after high school.While the differences between the governing styles can sometimes be jarring as I cycle between the domains of school and home each day, I'm thankful for the opportunity to experience both approaches. The enforcement of rules and boundaries mixed with increasing autonomy over time is helping shape me into a disciplined yet self-directed individual ready to thrive as an adult. These divergent worlds may feel at odds, but they are complementary in building a balanced foundation for future success.。

超实用高考英语复习阅读理解：优秀传统文化 (教师版)

优秀传统文化距离高考还有一段时间，不少有经验的老师都会提醒考生，愈是临近高考，能否咬紧牙关、学会自我调节，态度是否主动积极，安排是否科学合理，能不能保持良好的心态、以饱满的情绪迎接挑战，其效果往往大不一样。

以下是本人从事10多年教学经验总结出的以下学习资料，希望可以帮助大家提高答题的正确率，希望对你有所帮助，有志者事竟成！养成良好的答题习惯，是决定高考英语成败的决定性因素之一。

做题前，要认真阅读题目要求、题干和选项，并对答案内容作出合理预测;答题时，切忌跟着感觉走，最好按照题目序号来做，不会的或存在疑问的，要做好标记，要善于发现，找到题目的题眼所在，规范答题，书写工整;答题完毕时，要认真检查，查漏补缺，纠正错误。

总之，在最后的复习阶段，学生们不要加大练习量。

在这个时候，学生要尽快找到适合自己的答题方式，最重要的是以平常心去面对考试。

英语最后的复习要树立信心，考试的时候遇到难题要想“别人也难”，遇到容易的则要想“细心审题”。

越到最后，考生越要回归基础，单词最好再梳理一遍，这样有利于提高阅读理解的效率。

另附高考复习方法和考前30天冲刺复习方法。

【江苏省常州市2022-2023学年高三上学期期末考试】The Chinese written language originated very early. However, people began to like the art of writing, and pay attention to the creator’s thinking and spirit since the period between the Late Han Dynasty and Wei Jin Dynasty. The Chinese calligraphy fonts(字体) gradually evolved from oracle, Chinese bronze inscriptions and silk manuscripts to the following five main schools:21．The difference between Seal Script and Official Script lies in ______.A．the difficulty to write B．their function in useC．who often wrote them D．how cursive they are22．Which school is compared to people walking according to the text?A．Official Script. B．Running Hand.C．Cursive Hand. D．Regular Script.23．If you want to practice both Regular Script and Running Hand, who is a better choice to choose based on this article?A．Ouyang Xun. B．Yan Zhenqing.C．Zhang Xu. D．Wang Xizhi.【答案】21．A 22．B 23．B【文章大意】这是一篇说明文。

北京海淀区中国人民大学附属中学2024年高三第二学期自主学习能力测试英语试题含解析

北京海淀区中国人民大学附属中学2024年高三第二学期自主学习能力测试英语试题注意事项1．考生要认真填写考场号和座位序号。

2．试题所有答案必须填涂或书写在答题卡上，在试卷上作答无效。

第一部分必须用2B 铅笔作答；第二部分必须用黑色字迹的签字笔作答。

3．考试结束后，考生须将试卷和答题卡放在桌面上，待监考员收回。

第一部分（共20小题，每小题1.5分，满分30分）1．Never before ________ the famous museum was just a stone's thro away from their school, so out ________. A．had they known; went all they B．they had known; went all theyC．had they known; they all went D．they had known; they all went2．__________ 62% of all luxury consumer sales in Europe in 2015, shoppers from the Chinese mainland spent $7.2 billion overseas during the Spring Festival.A．Accounting for B．Adding toC．Consisting of D．Making up3．A survey of the opinions of experts _________ that three hours of outdoor exercise a week ________ good for one’s health.A．show; are B．shows; isC．show; is D．shows; are4．--- How about ______ plan to open a branch?--- I should say it was ______ failure.A．a; a B．a; / C．the; / D．the; a5．The main issue at the APEC meeting was a climate-change plan _____ by Australia’s Howard an d backed by Bush. A．put out B．put offC．put away D．put forward6．My mother ______ angry with me, but I couldn’t shut my mouth before I shouted at her!A．wasn’t B．wouldn’t beC．wouldn’t have been D．hadn’t been7．—What do you think of teaching, Bob?—I find it fun and challenging. It is a job ________ you are doing something serious and interesting.A．which B．whereC．when D．that8．--Tom, your foreign teacher speaks Chinese fluently!--Oh, she has lived in China for six years; otherwise she such good Chinese.A．didn’t speak B．would not have spokenC．would not speak D．hadn’t spoken9．________ and open to various interpretations, the terms of the new proposal need improving based on the interests of both sides.A．Accessible B．ApparentC．Ambiguous D．Aggressive10．After college, he was employed in a middle school and there ever since.A．would worked B．had workedC．worked D．has worked11．________enough money, the young man was unable to buy his girlfriend expensive jewelry.A．Not to save B．Not savingC．Not having saved D．Not saved12．_____ at the differences between her culture and theirs, Annie wanted to return home.A．Confusing B．ConfusedC．Having confused D．To confuse13．I like such houses with beautiful gardens in front, but I don’t have enough money to buy .A．it B．one C．that D．this14．In the library you can use your own computer to connect to Wi-Fi specially _____ for readers.A．preparing B．to prepareC．prepared D．prepare15．—You look sleepy today.—__________ not to miss the flight, I didn't dare to close my eyes the whole night.A．Reminded B．Being remindedC．Reminding D．Having reminded16．The infrastructure of a country is ________ makes everything run well, including things like transport, irrigation, electricity and schools.A．which B．thatC．where D．what17．If you want to improve your figure and health, the most effective thing to do is to show up at the gym every time you ________ be there.A．can B．willC．may D．shall18．I need to be more mature and ready to ______ my mistakes.A．hold on to B．cut off C．make up for D．take up19．Lisa wouldn’t ________ the job any more. She had a big argument with her boss and resigned.A．come up with B．keep up withC．make up with D．put up with20．The deal，next week，will allow Charney to make a great fortune in the stock market. A．completed B．being completedC．to be completed D．having been completed第二部分阅读理解（满分40分）阅读下列短文，从每题所给的A、B、C、D四个选项中，选出最佳选项。

抽样调查的方案设计有哪些类型

抽样调查的方案设计有哪些类型抽样调查的方案设计有哪些类型摘要：抽样调查是研究人员在进行社会调查或市场调研时常用的一种方法。

在方案设计阶段，研究人员需要选择合适的抽样类型来确保样本具有代表性，并准确反映整体人群的特征。

本文将介绍六种常见的抽样调查方案设计类型，并详细阐述每种类型的特点、适用场景及具体步骤，旨在帮助研究人员选择最合适的抽样调查方案。

一、简单随机抽样：简单随机抽样是最基本、最常见的抽样方法之一。

在这种设计下，每个个体被选中的概率是相等且独立的。

简单随机抽样适用于人口总体分布均匀且个体间没有明显差异的调查对象。

具体步骤包括：确定样本量、生成每个个体的随机数字、按照随机数字对个体排序、选择前n个个体作为样本。

二、系统抽样：系统抽样是指根据某种规则从总体中选择个体作为样本。

这种方法的优势在于操作简便，适用于总体分布有规律且统计特征均匀的情况。

具体步骤包括：确定样本量、计算抽样间距、随机选择一个起始点、按照设定间距选择样本。

三、分层抽样：分层抽样是将总体分为若干个层次，然后在每个层次内进行抽样。

这种抽样方法可以保证每个层次都有足够的样本量，从而更好地反映总体的特点。

具体步骤包括：确定各层次的样本量、计算每个层次的抽样比例、随机选择每个层次中的样本。

四、整群抽样：整群抽样是将总体分为若干个群体，然后随机选择部分群体进行调查。

这种抽样方法适用于群体内差异较大，而群体间差异较小的情况。

具体步骤包括：确定群体数量、计算每个群体的抽样比例、随机选择群体、在选中群体中进行全员调查或抽取样本。

五、整体抽样：整体抽样是指直接对总体中的每个个体进行调查，适用于总体规模较小且操作难度较低的情况。

这种抽样方法能够提供总体的详尽信息，但样本量通常较大。

具体步骤包括：确定样本量、逐个调查总体中的个体。

六、多阶段抽样：多阶段抽样是将总体分为若干个阶段，每个阶段依次进行抽样。

这种抽样方法适用于总体分布复杂、样本采集难度较大的情况。

截面与面板数据分析英文课件 (5)

v Problem: its effects on duration
influenced: high-income worker control group (low) and treatment group (high)
v Meyer, Viscusi and Durbin (1995)
INJURY.RAW log(durat); fchnge; highearn; age; gender; marital status; industry; type of injury
The pattern of coef. on the year dummies The change of the coef. of the key variable over time policy analysis
Example 13.1 Has the pattern of
women’s fertility Changed?
v Interpretations
base year: 1972 education: .128(4)=.512. turning point of age
v heteroskedasticity of error term over time? B-P test; WLS v educ? interaction effects (P. 13.7, IV) Has the effect of
dxi must have some variation across i. (13.17) satisfies the homoskedasticity assumption.
是同分布的（not identically distributed.） Different intercept and slopes Policy analysis