相信许多留学生对数学代考都不陌生，国外许多大学都引进了网课的学习模式。网课学业有利有弊，学生不需要到固定的教室学习，只需要登录相应的网站研讨线上课程即可。但也正是其便利性，线上课程的数量往往比正常课程多得多。留学生课业深重，时刻名贵，既要学习知识，又要结束多种类型的课堂作业，physics作业代写，物理代写，论文写作等；网课考试很大程度增加了他们的负担。所以，您要是有这方面的困扰，不要犹疑，订购myassignments-help代考渠道的数学代考服务，价格合理，给你前所未有的学习体会。
我们的数学代考服务适用于那些对课程结束没有掌握，或许没有满足的时刻结束网课的同学。高度匹配专业科目，按需结束您的网课考试、数学代写需求。担保买卖支持，100%退款保证，免费赠送Turnitin检测报告。myassignments-help的Math作业代写服务，是你留学路上忠实可靠的小帮手！
统计代写|抽样调查作业代写sampling theory of survey代考|A Few Indirect Questioning Techniques Other than RRT’s
Certain general objections against the virtues of RRT’s are being sounded now-a-days from various quarters. RRT’s are hard to be clearly understood and correctly applied by average respondents, so for them face-to-face interviews are unavoidable; once RR’s are gathered they cannot be reproduced; complaints are common that RRT’s are tricky and are laughable leading to frequent disapprovals. Raghavarao and Federer (1979), Miller (1984), Miller, Cisim and Harrel (1986), Droitcour, Caspar, Hubbard, Parsley, Visscher and Ezzati (1991) are some of the proponents of an alternative indirect questioning technique called Item Count Technique. My collaborative involvement in the multifarious development of the subject is documented in the papers and monographs by Chaudhuri and Christofides $(2007,2008,2013)$, Chaudhuri (2011a), Shaw (2015,2016) and besides, Christofides (2015) has also added substantially. Other prominent contributors include Hussain, Shah and Shabbir (2012) and Tian, Tang, Wu and Liu (2014) among several others.
Briefly, Item Count Technique works as follows. Suppose $T$ stands for a stigmatizing or ‘tainted’ attribute and our object is to estimate in a finite population the proportion $\theta$ that bears it as is usual in RRT’s. But here two independent samples $s_1$ and $s_2$ say, are drawn from a population following a given design $p$ with probabilities and positive inclusion-probabilities $\pi_i$ for $i$ and for every pair $i, j(i \neq j)$ the value $\pi_{i j}, i, j \in U=(1, \cdots N)$. In the first sample a respondent is given a card on which $G(>1)$ innocuous items appear and the tainted attribute $T$ is added as the $(G+1)$ st item and the person is to report the number $K$ out of these $(G+1)$ items that apply to him/her. In the other independently drawn sample $s_2$ a person is presented a list of the above $G$ innocuous items and on request is to announce the number out of them that apply to him/her. Before the present author stepped in, the samples were taken according to the SRSWR method alone. But since 2007 as documented the samples were chosen with general unequal probabilities and essentially without replacement.
统计代写|抽样调查作业代写sampling theory of survey代考|Adaptive Sampling
As we understand, vide Chaudhuri $(2000,2015)$, adaptive sampling was introduced by Thompson (1990) and later further developed by Thompson (1992), Thompson and Seber (1996), Salehi and Seber (1997, 2002, 2013), Chaudhuri (2000, 2015) among others including Chaudhuri and Saha (2004), Chaudhuri, Bose and Dihidar (2005), Chaudhuri, Bose and Ghosh $(2004,2005)$ et al.
Suppose we intend to suitably estimate the numbers of people engaged in various types of small-scale industrial enterprises in the rural unorganized sector in certain under-developed or developing countries. This is necessary in assessing the magnitudes and values of gross and net domestic national products. In an Indian state many may earn their livelihoods as iron or goldsmiths or by manufacturing stone chips or metal screws in the villages as private craftsmen and craftswomen. Though in India there is a National Sample Survey Office (NSSO) every year conducting nationwide big surveys in both rural and urban areas in every Indian state or union territory (UT),
such surveys hardly capture such earners industry-wise in sufficient numbers. NSSO-sampled villages may not contain enough goldsmiths and so their contributions to GDP may be significantly underestimated.
A possible remedy is adaptive sampling with specific missions which may not be encouraged by a national survey at all. An adaptive sampling may provide a clue which we plan to discuss below.
As usual, let $U=(1, \cdots, i, \cdots N)$ be a finite population of units with $y$ values $y_i$ for respective $i$ in $U$. Also, let $s$ be a sample of units chosen with probability $p(s)$ according to a design $p$. But suppose that for most of the units $y_i$-values are zero or negligibly small but for certain unknown number of units in unknown locations forming unknowable clusters may be rather high-valued so that the population total $Y=\sum_1^N y_i$ may be quite sizeable in aggregated magnitudes. In a sample $s$ many of the $y_i$-values may be nil or negligible but a few may be adequately positive-valued. Then, it may be rational to suppose, or rather expect one or more neighboring units of such a positivevalued unit may be similarly valued but not in the sample. In such situations it may be worthwhile to extend the sample to cover them to capture useful units contributing to the sample-data helping to create a better estimation to render the estimator closer to the total. So, the following rationally profitable method starts developing.
Given a unit $i$ of $U$, a neighborhood is first uniquely defined. For example given a farmland, one to its left, one to its right, one above and one below it may be regarded as its four neighbors; the set of these five units including the initial unit $i$ itself may be called its neighborhood. When a sample $s$ is drawn, every sampled unit is to be scanned to note its value, doing nothing if it is nil or negligible-valued, but in case it is well-valued check the value of each of its neighbors, going no further if the neighbors are not well-valued but checking each of the neighbors for its value if it is well-valued and proceeding with this process and enhancing the sample and stopping as soon as the first time it is found that none of the units among the neighbors is well-valued. The set of units thus reached starting with a unit is called a cluster of that unit. Dropping from this cluster all the nil or negligible-valued units in it what is left out constitutes the network of the initial unit.
抽样调查代考
统计代写|抽样调查作业代写sampling theory of survey代考|A Few Indirect Questioning Techniques Other than RRT’s
现在，来自各个方面的某些反对 RRT 优点的普遍反对意见正在响起。RRT很难被普通受访者清楚地理解和正确应用，因此对他们来说面对面的访谈是不可避免的；一旦 RR 被收集起来，它们就不能被复制；人们普遍抱怨 RRT 很棘手，而且很可笑，导致经常遭到反对。Raghavarao 和 Federer (1979)、Miller (1984)、Miller、Cisim 和 Harrel (1986)、Droitcour、Caspar、Hubbard、Parsley、Visscher 和 Ezzati (1991) 是另一种间接提问技术（称为项目计数技术）的一些支持者. Chaudhuri 和 Christofides 的论文和专着中记录了我参与该主题的多方面发展(2007,2008,2013), Chaudhuri (2011a), Shaw (2015,2016) 以及 Christofides (2015) 也进行了大量补充。其他著名贡献者包括 Hussain、Shah 和 Shabbir (2012) 以及 Tian、Tang、Wu 和 Liu (2014) 等。
简而言之，项目计数技术的工作原理如下。认为吨代表污名化或“污染”属性，我们的目标是估计有限人口中的比例一世像在 RRT 中一样承受它。但是这里有两个独立样本秒1和秒2比方说，是从遵循给定设计的人群中抽取的p具有概率和正包含概率π一世为了一世对于每一对一世,j(一世≠j)价值π一世j,一世,j∈在=(1,⋯否). 在第一个样本中，给受访者一张卡片，上面写着G(>1)出现无害的物品和受污染的属性吨被添加为(G+1)st item and the person 是要报号的钾在这些之外(G+1)适用于他/她的项目。在另一个独立抽取的样本中秒2向一个人展示了上面的列表G无害的物品，并应要求公布其中适用于他/她的数量。在本作者介入之前，样本是单独根据 SRSWR 方法采集的。但自 2007 年以来，如文件所示，样本的选择概率一般不相等，而且基本上没有替换。
统计代写|抽样调查作业代写sampling theory of survey代考|Adaptive Sampling
据我们了解，vide Chaudhuri(2000,2015)，自适应采样由 Thompson（1990）引入，后来由 Thompson（1992）、Thompson 和 Seber（1996）、Salehi 和 Seber（1997、2002、2013）、Chaudhuri（2000、2015）等人进一步发展，包括 Chaudhuri 和 Saha (2004)，Chaudhuri、Bose 和 Dihidar (2005)，Chaudhuri、Bose 和 Ghosh(2004,2005)等。
假设我们打算适当估计某些欠发达国家或发展中国家农村无组织部门中从事各类小型工业企业的人数。这对于评估国内生产总值和净值的规模和价值是必要的。在印度的一个邦中，许多人可能以铁匠或金匠的身份谋生，或者通过在村庄中作为私人工匠和女工匠制造石屑或金属螺丝来谋生。尽管在印度，每年都有一个全国抽样调查办公室 (NSSO) 在印度每个邦或联邦领土 (UT) 的农村和城市地区进行全国性的大规模调查，
此类调查很难涵盖足够数量的此类行业收入者。NSSO 抽样的村庄可能没有足够的金匠，因此他们对 GDP 的贡献可能被大大低估了。
一种可能的补救措施是针对特定任务进行自适应抽样，这可能根本不会受到全国调查的鼓励。自适应采样可以提供我们计划在下面讨论的线索。
像往常一样，让在=(1,⋯,一世,⋯否)是一个有限的单位群体是价值观是一世对于各自的一世在在. 还有，让秒是按概率选择的单位样本p(秒)根据设计p. 但假设对于大多数单位是一世- 值是零或小到可以忽略不计，但对于某些未知位置的未知数量的单位，形成不可知的集群可能是相当高的值，因此人口总数是=∑1否是一世合计幅度可能相当大。在样品中秒许多的是一世- 值可能为零或可忽略不计，但少数可能具有足够的正值。然后，可以合理地假设，或者更确切地说，期望这样一个正值单元的一个或多个相邻单元可能具有相似的值，但不在样本中。在这种情况下，可能值得扩展样本以覆盖它们以捕获对样本数据有贡献的有用单位，从而帮助创建更好的估计以使估计量更接近总数。因此，以下合理盈利的方法开始发展。
给定一个单位一世的在，邻域首先被唯一定义。比如一块农田，左一右一上一下可以看作是它的四个邻居；这五个单元的集合，包括初始单元一世本身可以称为它的邻域。当样品秒被绘制，扫描每个采样单元以记录其值，如果它为零或值可忽略不计，则不执行任何操作，但如果它的值很高，请检查其每个邻居的值，如果邻居是价值不高，但检查每个邻居的价值，如果它价值很高，并继续这个过程并增强样本，并在第一次发现邻居中没有一个单位是好的时立即停止 -重视。以一个单元开始的单元集合称为该单元的簇。从这个集群中删除所有零值或可忽略值的单元，剩下的构成初始单元的网络。
myassignments-help数学代考价格说明
1、客户需提供物理代考的网址，相关账户，以及课程名称，Textbook等相关资料~客服会根据作业数量和持续时间给您定价~使收费透明，让您清楚的知道您的钱花在什么地方。
2、数学代写一般每篇报价约为600—1000rmb，费用根据持续时间、周作业量、成绩要求有所浮动(持续时间越长约便宜、周作业量越多约贵、成绩要求越高越贵)，报价后价格觉得合适，可以先付一周的款，我们帮你试做，满意后再继续，遇到Fail全额退款。
3、myassignments-help公司所有MATH作业代写服务支持付半款，全款，周付款，周付款一方面方便大家查阅自己的分数，一方面也方便大家资金周转，注意:每周固定周一时先预付下周的定金，不付定金不予继续做。物理代写一次性付清打9.5折。
Math作业代写、数学代写常见问题
留学生代写覆盖学科？
代写学科覆盖Math数学,经济代写，金融,计算机,生物信息，统计Statistics,Financial Engineering，Mathematical Finance，Quantitative Finance，Management Information Systems,Business Analytics,Data Science等。代写编程语言包括Python代写、Physics作业代写、物理代写、R语言代写、R代写、Matlab代写、C++代做、Java代做等。
数学作业代写会暴露客户的私密信息吗？
我们myassignments-help为了客户的信息泄露，采用的软件都是专业的防追踪的软件，保证安全隐私，绝对保密。您在我们平台订购的任何网课服务以及相关收费标准，都是公开透明，不存在任何针对性收费及差异化服务，我们随时欢迎选购的留学生朋友监督我们的服务，提出Math作业代写、数学代写修改建议。我们保障每一位客户的隐私安全。
留学生代写提供什么服务？
我们提供英语国家如美国、加拿大、英国、澳洲、新西兰、新加坡等华人留学生论文作业代写、物理代写、essay润色精修、课业辅导及网课代修代写、Quiz，Exam协助、期刊论文发表等学术服务，myassignments-help拥有的专业Math作业代写写手皆是精英学识修为精湛；实战经验丰富的学哥学姐！为你解决一切学术烦恼！
物理代考靠谱吗？
靠谱的数学代考听起来简单，但实际上不好甄别。我们能做到的靠谱，是把客户的网课当成自己的网课；把客户的作业当成自己的作业；并将这样的理念传达到全职写手和freelancer的日常培养中，坚决辞退糊弄、不守时、抄袭的写手！这就是我们要做的靠谱！
数学代考下单流程
提早与客服交流，处理你心中的顾虑。操作下单，上传你的数学代考/论文代写要求。专家结束论文，准时交给，在此过程中可与专家随时交流。后续互动批改
付款操作：我们数学代考服务正常多种支付方法，包含paypal，visa，mastercard，支付宝，union pay。下单后与专家直接互动。
售后服务：论文结束后保证完美经过turnitin查看，在线客服全天候在线为您服务。如果你觉得有需求批改的当地能够免费批改，直至您对论文满意为止。如果上交给教师后有需求批改的当地，只需求告诉您的批改要求或教师的comments，专家会据此批改。
保密服务：不需求提供真实的数学代考名字和电话号码，请提供其他牢靠的联系方法。我们有自己的工作准则，不会泄露您的个人信息。
myassignments-help擅长领域包含但不是全部:
myassignments-help服务请添加我们官网的客服或者微信/QQ，我们的服务覆盖：Assignment代写、Business商科代写、CS代考、Economics经济学代写、Essay代写、Finance金融代写、Math数学代写、report代写、R语言代考、Statistics统计学代写、物理代考、作业代写、加拿大代考、加拿大统计代写、北美代写、北美作业代写、北美统计代考、商科Essay代写、商科代考、数学代考、数学代写、数学作业代写、physics作业代写、物理代写、数据分析代写、新西兰代写、澳洲Essay代写、澳洲代写、澳洲作业代写、澳洲统计代写、澳洲金融代写、留学生课业指导、经济代写、统计代写、统计作业代写、美国Essay代写、美国代考、美国数学代写、美国统计代写、英国Essay代写、英国代考、英国作业代写、英国数学代写、英国统计代写、英国金融代写、论文代写、金融代考、金融作业代写。