详细说明简要说明：详细介绍：日立电梯配件：广州粤立网址： E-mail：gzyueli@ 原厂产品！质量保证！厚道经营！长期合作！日立扶梯类：1、新款（错齿）左右黄边条2、新款（错齿）后边条3、新款（错齿）前边条4、旧款（平齿）左右黄边条5、旧款（平齿）后边条6、旧款（平齿）前边条7、日立梳齿板左8、日立梳齿板中9、日立梳齿板右10、扶手带耐磨条（2米/条）11、扶手带驱动轮（141）12、扶手从动轮13、158驱动总成（带齿轮）14、70*70白色托滚轮15、扶手带出入口安全开关16、梯级滚轮安全开关17、扶手带入口保护胶18、端部铝框架（弯头）19、日立扶梯锁（全套）20、机械防逆转装置21、20齿或21齿轮22、双排轮（24齿和29齿）23、前梯级滚轮24、后梯级滚轮25、急停按钮26、进口扶梯三角皮带27、日立扶梯制动器DHL-D1428、7.5KW扶梯主机29、梯级（30\35度带黄边）30、扶手带驱动链条31、扶手带32、日立接触器H2033、中间内盖板L=150034、中间内盖板L=300035、各式扶梯灯具36、1000型（长度1000）的梯级30度日立电梯控制板类：1、DMC-12、DMD-13、SCLBV1.0(小B板）4、NPH-SCLA（A板）5、SCLC外呼板(C板）6、MTB-CLVL通讯板7、MTB-KIN主板8、MTB-HLNF串行通讯板9、RIO板-22个继电器10、IOSB继电器板11、B89外显板12、GVF外显板13、INV-BDCC驱动板14、INV-SDC门机板15、INV-MPU主板16、INV-FIO付板17、分频板FDB-0218、MCUB主板19、FMT主板20、MPU421、FB-SDC22、FB-FIO23、MIVB24、CMPU25、MPV26、ICBD27、EXT-MPU28、MIGV板门机板29、ACR30、HAD31、HPMC32、ADS33、模块PM300RSD06034、UA2-CMI35、编码器TS5246N478日立直梯类：1、DL-PO2按钮（NPX）2、SL-TC大方按钮3、RL1-M带旁灯按钮4、AK2026不配字片5、外呼面板（树脂）单梯6、外呼面板（树脂）并联7、外呼面板不锈钢8、操纵箱不锈钢面板9、外呼箱（整套）树脂10、外呼透镜11、日立原厂语音报站板12、通用型语音报站板13、对讲机主机DJ-A14、对讲机分机15、对讲机电源16、大方灯罩615*61517、小方灯罩290*29018、高响度蜂鸣器6/12/24V19、日立报站钟-55V20、绳头楔套装配WS-12\1021、门锁DK-RSL22、门锁DK-RN4L23、操纵箱锁24、泊梯锁（基站锁）25、付门锁TKB-1A26、缓冲开关TKB-1B27、LX口-21限位\极限开关28、LX口-22安全钳\涨紧开关29、相序保护开关XJ5(A)30、安全触板开关LX29-6\731、光电开关TL-G3D32、7.5KW直梯电机机33、客梯门电机YSMB71-434、货梯门电机TOG-MS-335、横流风扇GF-971236、横流风扇FB-9B37、货梯圆风扇38、编码器100-1024C/T-C2-12V39、门机编码器HES-0512-2MHC40、门机编码器TS60Z6N15141、制动线圈-ZC系列42、油封TKL-进口43、刹车皮TKL44、门地坎-铝合金45、门上坎46、门挂板-OP=800\90047、大货梯门挂板48、钢丝绳组件-OP=800\90049、YP门挂轮50、YP梯动触头大铜头51、YP梯动触头静铜头52、眼镜仔53、差动变压器54、变压器TBK-10055、变压器TBK-60056、变压器TBK-30057、涡流传感器WX02658、H50接触器AC110V59、接触器SC-5-1AC60、接触器SC-4-1AC61、接触器SC-N3AC62、位置检测器RM-YA363、平层感应器YG-3 DC110V64、光电开关NDS-3UAX65、底坑检修箱66、相序继电器WJD67、曳引轮614*12*568、曳引轮514*12*569、轿顶导向轮70、限速器反绳轮71、涨紧反绳轮72、限速器张紧装置（整套）73、导轨接油盒-8K\13K74、方油杯75、井道灯76、日立新款滑块77、8K/13K靴衬78、滚动导靴滚轮79、主机防震胶80、轿底防震胶81、日立光幕MS23382、日立WJD相序继电器83、日立旋转编码器N12284、VG7的15KW的变频器85、日立NPH-2-SCLB V1.0板86、日立UAX梯的轿内数显87、日立GVF2轿内数显88、RM-YA3平层感应器89、门机板INV-SDC990、三角锁胆91、称重感应器DW-1。
Package‘tcftt’October14,2022Type PackageTitle Two-Sample Tests for Skewed DataVersion0.1.0Author Huaiyu Zhang,Haiyan WangMaintainer Huaiyu Zhang<*************************>Description The classical two-sample t-test works well for the normally distributed data or data with large sample size.The tcfu()and tt()tests implemented in this package providebetter type-I-error control with more accurate power when testing the equality of two-samplemeans for skewed populations having unequal variances.These tests are especially usefulwhen the sample sizes are moderate.The tcfu()uses the Cornish-Fisher expansion to achievea better approximation to the true percentiles.The tt()provides transformations of the Welch'st-statistic so that the sampling distribution become more symmetric.For more technical details, please refer to Zhang(2019)</2097/40235>.License GPL-2Encoding UTF-8LazyData trueRoxygenNote7.1.0Depends R(>=3.1.0)Imports statsNeedsCompilation noRepository CRANDate/Publication2020-07-2314:50:02UTCR topics documented:adjust_power (2)boot_test (3)pauc (3)tcftt (4)tcfu (5)tt (6)t_cornish_fisher (7)t_edgeworth (8)12adjust_power Index10 adjust_power Adjusting power to assure actual size is within significance levelDescriptionIt is common to use Monte Carlo experiments to evaluate the performance of hypothesis tests and compare the empirical power among competing tests.High power is desirable but difficulty arises when the actual sizes of competing tests are not comparable.A possible way of tackling this issue is to adjust the empirical power according to the actual size.This function incorporates three types of power adjustment methods.Usageadjust_power(size,power,method="ZW")Argumentssize the empirical size of a test.power the empirical power of a test.method the power adjustment method.’ZW’is the method proposed by Zhang and Wang(2020),’CYS’is the method proposed by Cavus et al.(2019),and’probit’is the"method1:probit analysis"in Lloyd(2005).Valuethe power value after adjustment.ReferencesLloyd,C.J.(2005).Estimating test power adjusted for size.Journal of Statistical Computation and Simulation,75(11):921-933.Cavus,M.,Yazici,B.,&Sezer,A.(2019).Penalized power approach to compare the power of the tests when Type I error probabilities are munications in Statistics-Simulation and Computation,1-15.Zhang,H.and Wang,H.(2020).Transformation tests and their asymptotic power in two-sample comparisons Manuscript in review.Examplesadjust_power(size=0.06,power=0.8,method= ZW )adjust_power(size=0.06,power=0.8,method= CYS )adjust_power(size=0.06,power=0.8,method= probit )boot_test3 boot_test Bootstrap_t test for two-sample comparisonsDescriptionThis function provides bootstrap approximation to the sampling distribution of the the Welch’s t-statisticUsageboot_test(x1,x2,B=1000,alternative="greater")Argumentsx1thefirst sample.x2the second sample.B number of resampling rounds.Default value is1000.alternative the alternative hypothesis:"greater"for upper-tailed,"less"for lower-tailed,and "two.sided"for two-sided alternative.Valuethe p-value of the bootstrap_t test.Examplesx1<-rnorm(100,0,1)x2<-rnorm(100,0.5,2)boot_test(x1,x2)pauc Power-adjustment based on non-parametric estimation of the ROCcurveDescriptionIt is common to use Monte Carlo experiments to evaluate the performance of hypothesis tests and compare the empirical power among competing tests.High power is desirable but difficulty arises when the actual sizes of competing tests are not comparable.A possible way of tackling this issue is to adjust the empirical power according to the actual size.This function implements the"method 2:non-parametric estimation of the ROC curve"in Lloyd(2005).For more details,please refer to the paper.Usagepauc(stat_h0,stat_ha,target_range_lower,target_range_upper)4tcfttArgumentsstat_h0simulated test statistics under the null hypothesis.stat_ha simulated test statistics under the alternative hypothesis.target_range_lowerthe lower end of the size range.target_range_upperthe upper end of the size range.Valuethe adjusted power.ReferencesLloyd,C.J.(2005).Estimating test power adjusted for size.Journal of Statistical Computation andSimulation,75(11):921-933.Examplesstath0<-rnorm(100)statha<-rnorm(100,mean=1)pauc(stath0,statha,0.01,0.1)tcftt tcftt:Two-Sample Tests for Skewed DataDescriptionThe classical two-sample t-test works well for the normally distributed data or data with largesample size.The tcfu()and tt()tests implemented in this package provide better type I error con-trol with more accurate power when testing the equality of two-sample means for skewed popu-lations having unequal variances.The approximation is especially useful when the sample sizesare moderate.The tcfu()uses the Cornish-Fisher expansion to achieve a better approximation tothe true percentiles.The tt()provides transformations of the Welch’s t-statistic so that the sam-pling distribution become more symmetric.For more technical details,please refer to Zhang(2019)</2097/40235>.tcftt functionsThe function‘tcfu()‘implements the Cornish-Fisher based two-sample test(TCFU)and‘tt()‘im-plements the transformation based two-sample test(TT).The function‘t_edgeworth()‘provides theEdgeworth expansion for cumulative distribution function for the Welch’s t-statistic,and‘t_cornish_fisher()‘provides the Cornish-Fisher expansion for the percentiles.The functions‘adjust_power()‘and‘pauc()‘provide power adjustment for simulation studies so that the actual size of the tests arewithin the significance level.tcfu5 tcfu The TCFU testDescriptionThis test is suitable for testing the equality of two-sample means for the populations having unequal variances.When the populations are not normally distributed,this test can provide better type I error control and more accurate power than a large-sample t-test using normal approximation.The critical values of the test are computed based on the Cornish-Fisher expansion of the Welch’s t-statistic.The order of the Cornish-Fisher expansion is allowed to be0,1,or2.More details please refer to Zhang and Wang(2020).Usagetcfu(x1,x2,effectSize=0,alternative="greater",alpha=0.05,order=2)Argumentsx1thefirst sample.x2the second sample.effectSize the effect size of the test.The default value is0.alternative the alternative hypothesis:"greater"for upper-tailed,"less"for lower-tailed,and "two.sided"for two-sided alternative.alpha the significance level.The default value is0.05.order the order of the Cornish-Fisher expansion.Valuetest statistic,critical value,p-value,reject decision at the given significance level.ReferencesZhang,H.and Wang,H.(2020).Transformation tests and their asymptotic power in two-sample comparisons.Manuscript in review.Examplesx1<-rnorm(20,1,3)x2<-rnorm(21,2,3)tcfu(x1,x2,alternative= two.sided )6tt tt The transformation based testDescriptionThis test is suitable for testing the equality of two-sample means for the populations having unequal variances.When the populations are not normally distributed,the sampling distribution of the Welch’s t-statistic may be skewed.This test conducts transformations of the Welch’s t-statistic to make the sampling distribution more symmetric.For more details,please refer to Zhang and Wang (2020).Usagett(x1,x2,alternative="greater",effectSize=0,alpha=0.05,type=1)Argumentsx1thefirst sample.x2the second sample.alternative the alternative hypothesis:"greater"for upper-tailed,"less"for lower-tailed,and "two.sided"for two-sided alternative.effectSize the effect size of the test.The default value is0.alpha the significance level.The default value is0.05.type the type of transformation to be used.Possible choices are1to4.They cor-respond to the TT1to TT4in Zhang and Wang(2020).Which type providesthe best test depends on the relative skewness parameter A in Theorem2.2ofZhang and Wang(2020).In general,if A is greater than3,type=3is recom-mended.Otherwise,type=1or4is recommended.The type=2transformationmay be more conservative in some cases and more liberal in some other casesthan the type=1and4transformations.For more details,please refer to Zhangand Wang(2020).Valuetest statistic,critical value,p-value,reject decision at the given significance level.ReferencesZhang,H.and Wang,H.(2020).Transformation tests and their asymptotic power in two-sample comparisons Manuscript in review.t_cornish_fisher7Examplesx1<-rnorm(20,1,3)x2<-rnorm(21,2,3)tt(x1,x2,alternative= two.sided ,type=1)#Negative lognormal versus normal datan1=50;n2=33x1=-rlnorm(n1,meanlog=0,sdlog=sqrt(1))-0.3*sqrt((exp(1)-1)*exp(1))x2=rnorm(n2,-exp(1/2),0.5)tt(x1,x2,alternative= less ,type=1)tt(x1,x2,alternative= less ,type=2)tt(x1,x2,alternative= less ,type=3)tt(x1,x2,alternative= less ,type=4)#Lognormal versus normal datan1=50;n2=33x1=rlnorm(n1,meanlog=0,sdlog=sqrt(1))+0.3*sqrt((exp(1)-1)*exp(1))x2=rnorm(n2,exp(1/2),0.5)tt(x1,x2,alternative= greater ,type=1)tt(x1,x2,alternative= greater ,type=2)tt(x1,x2,alternative= greater ,type=3)tt(x1,x2,alternative= greater ,type=4)t_cornish_fisher Cornish-Fisher expansion for Welch’s t-statisticDescriptionThis function provides approximation for the quantile function of the sampling distribution of the Welch’s t-statistic using Cornish-Fisher expansion(up to second order).Usaget_cornish_fisher(p,order=2,n1,n2,mu1,mu2,sigma1,sigma2,gamma1,gamma2,tau1,tau2)Argumentsp a probability value.order the order of Cornish-Fisher expansion.Valid options are0,1,and2.If set to0,it reduces to a normal approximation and it returns the p-th percentile of standardnormal distribution.n1sample size for the sample from thefirst population.n2sample size for the sample from the second population.mu1mean of thefirst population.mu2mean of the second population.sigma1standard deviation of thefirst population.sigma2standard deviation of the second population.gamma1skewness of thefirst population.gamma2skewness of the second population.tau1kurtosis of thefirst population.tau2kurtosis of the second population.ValueCornish-Fisher expansion value evaluated at p.Examplest_cornish_fisher(0.9,order=2,n1=60,n2=30,mu1=0,mu2=0,sigma1=1,sigma2=0.5,gamma1=1,gamma2=0,tau1=6,tau2=0)t_cornish_fisher(0.3,order=1,n1=60,n2=30,mu1=0,mu2=0,sigma1=1,sigma2=0.5,gamma1=1,gamma2=0,tau1=6,tau2=0)t_edgeworth Edgeworth expansion for Welch’s t-statisticDescriptionThis function provides approximation for the cumulative distribution function of the sampling dis-tribution of the Welch’s t-statistic using Normal distribution,first order or second order Edgeworth expansion.Usaget_edgeworth(x,order=2,n1,n2,mu1,mu2,sigma1,sigma2,gamma1,gamma2,tau1,tau2)Argumentsx a real number.order the order of edgeworth expansion.Valid options are0,1,and2.If set to0, it reduces to approximation based on the central limit theorem and returns theCDF of standard normal distribution evaluated at x.n1sample size for the sample from thefirst population.n2sample size for the sample from the second population.mu1mean of thefirst population.mu2mean of the second population.sigma1standard deviation of thefirst population.sigma2standard deviation of the second population.gamma1skewness of thefirst population.gamma2skewness of the second population.tau1kurtosis of thefirst population.tau2kurtosis of the second population.ValueEdgeworth expansion evaluated at x.Examplest_edgeworth(1.96,order=2,n1=20,n2=30,mu1=0,mu2=0,sigma1=1,sigma2=0.5,gamma1=1,gamma2=0,tau1=6,tau2=0)Indexadjust_power,2boot_test,3pauc,3t_cornish_fisher,7t_edgeworth,8tcftt,4tcfu,5tt,610。
IntroductionWelcome to this support pack on assessment for learning tools in the classroom.This are a selection of assessment tools designed for Level 1 and below learners for use in the classroom by teachers who want to assess progress or want to help students to engage in self assessment.Almost every learning activity can present opportunities for assessment. The ideas contained in this pack can be used on a regular basis to support and inform teaching and learning and to help to assess a learners progress.Both teachers and students can use the feedback gained from the activities in this pack to improve learning. The kind of feedback which can be gather includes:•How students are learning•Their progress•The nature of their understanding•Any difficulties they are havingThese activities are intended to be a guide as to activities that can be done to assess learning in the classroom as well as motivate learners by helping them to identify their progress, any barriers to that progress and therefore start to become more independent during their learning.These can and should be adapted to the centres own learners and we encourage centres to do so.These activities represent some activities which could be used to assess learner progress. The activities are not exhaustive and are designed to compliment rather than replace a centres own usual practices.WHERE AM I NOW?What am i good at? What have I done well? What am I learning?What am I going to do next?What are my goals? What ways can I use to get better at this?What do i need to do? What people can support me?What other supports do i need? What ways can i use tohelp myself? WHERE AM I GOING?WHAT DO I NEED TO GET THERE?I am able tocomplete work I listened well I help others if I listen to others I raise my hand Green= Excellent Amber= OK Red= Have to improveunderstandI feel confident toRequest a Selfie If you are proudof your work, Complete theproud tokentake a lolly stick and clip these to your workand we will take a photo of you with yourworkPearson Proud TokenI am proud of this workbecause...........................................................................................................Pearson AirwaysAirwaysAirwaysBoarding CardLanding CardWhat I already know about this topic:What I discovered today:In this lesson I want to achieve:A question I want to know the answer to is:Did you achieve your goal? How?A question I still have about this learning is?Tell one person in the room what you like about theirwork.Write down the answer here.Ask one person in the room a question about their work. Write down the answer here.Give the person a positive suggestion on how they can improve their work.Write it down here.Person receiving feedback: ______________________________ Person giving feedback:__________________________________。
