如果你也在 怎样代写统计推断Statistical inference这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。统计推断statistics interference可用于机械零件的尺寸计算，确定所施加的载荷何时超过结构的强度，以及在许多其他情况下。这种类型的分析也可以用来估计故障的概率或故障的频率。
统计推断Statistical inference机械部件通常被设计为精确地配合在一起。例如，如果一个轴被设计成在一个孔中 “滑动配合”，轴必须比孔小一点。(传统的公差可能表明，所有的尺寸都在这些预定的公差之内。然而，对实际生产的过程能力研究可能显示出具有长尾巴的正态分布）。轴和孔的尺寸通常会形成具有一些平均值（算术平均值）和标准差的正态分布。
my-assignmentexpert™ 统计推断Statistical inference作业代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。my-assignmentexpert™， 最高质量的统计推断Statistical inference作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此统计推断Statistical inference作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。
想知道您作业确定的价格吗? 免费下单以相关学科的专家能了解具体的要求之后在1-3个小时就提出价格。专家的 报价比上列的价格能便宜好几倍。
my-assignmentexpert™ 为您的留学生涯保驾护航 在统计Statistics作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计推断Statistical inference代写服务。我们的专家在统计Statistics代写方面经验极为丰富，各种统计推断Statistical inference相关的作业也就用不着 说。
我们提供的统计推断Statistical inference及其相关学科的代写，服务范围广, 其中包括但不限于:
统计代写|统计推断作业代写Statistical inference代考|The Place of Statistics in the Social Sciences
It was over 70 years ago that W. H. Auden (1966, p. 225) issued this commandment in a Harvard commencement address; and, at least since J. W. Tankard (1984) quoted this Phi Beta Kappa poem in his book The Statistical Pioneers, few critics of statistics in the social sciences have been able to resist repeating it. Grounds for Auden’s concern were already evident: At around the same time, Maurice Kendall (1942), commenting on the growing influence of statisticians, observed that, “Beginning as a small sect concerned only with political economy, they have already overrun every branch of science with a rapidity of conquest rivaled only by Attila, Mohammed, and the Colorado beetle” (p. 69). Ironically, and remarkably, Kendall made his observation just before the explosive growth of statistics following World War II. With the advent of electronic computers and space-age grants to research and higher education, his comparison has long since faded into meaninglessness; with respect not only to their speed of conquest but equally to the breadth of their empire, the social scientists have put Attila to shame. In 1942, when Kendall was writing, only $29 \%$ of medical schools taught statistics, but by 1952 , that figure was $82 \%$ (Marks, 1997, p. 137, n. 5). And medicine was the field where conquest by statistics was most vigorously contested.
Beginning just before World War II, the new theory of statistical inference developed during the 1920 s and 1930 s transformed the social sciences and neighboring disciplines. Although, with respect to issues of application, I shall be focusing on psychology, the area of my training and experience, and secondarily on medicine, the fields of education, sociology, agriculture, and economics were similarly infected. Comparative experiments, designed to investigate questions of whether certain treatments had an “effect” or not, were now assumed to have, in statistical significance, an objective criterion for yes-or-no answers to those questions, and research in these fields, in its modern forms, mushroomed in the $1940 \mathrm{~s}$ and $1950 \mathrm{~s}$ partly as a result. A course in statistics, with an overwhelming emphasis on inferential over descriptive techniques, is today the one requirement across virtually all universities in these disciplines. Statistical inference has thus been regarded, almost universally for the last 70 years, as the backbone of research in the social sciences. Its monopoly is ominously secured, and attested, by the fact that it now crucially shapes the research questions that can be asked in these fields, and few practitioners can any longer conceive of alternatives.
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|Statistical Inference and its Misconceptions
With Oakes’s pretest out of the way, it is time to concretize the initial focus of this essay by providing an informal clarification of what is meant by statistical inference and taking a brief look at some of the criticisms. For simplicity I shall be speaking in this chapter only of significance testing; other techniques comprised by statistical inference (e.g., confidence intervals) I shall get to in due course.
In a typical significance test, we might want to determine whether our treatment had had an effect. Now it might be supposed that we could just look at our data and see whether there were a difference between our treatment and control data. But when psychologists speak of a treatment effect, they are talking about a difference that is unobservable in principle. We actually imagine our treatment and control data to have been randomly sampled from a hypothetical infinite population of such scores, and our statement of an effect pertains to a difference between these hypothetical populations. When we do a significance test, we start by assuming something we typically don’t believe for a moment-for example, that the treatment has no effect. The significance level we calculate is the probability of drawing two such different samples if there were no difference in the population means. If this probability lies beyond a conventional cut-off, we reject the null hypothesis of no effect.
So the first thing that is strange about the procedure is that it is wholly hypothetical. Except in survey research, we don’t take random samples, but that is the basis for our probability calculations, which otherwise have no meaning.
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|The Root of the Problem in the Dualistic Concept of Probability
The source of confusion over statistical inference is the concept of probability, on which statistical inference rests. It is evident from reflection on everyday usage that the concept of probability covers several aspects or meanings. On the one hand, it adverts to frequency of occurrence in some set or series of events. If we say “set,” we have the classical, or Laplacean, measure of probability from gambling as the ratio of the number of cases favorable to an event to the total number of equiprobable cases; if the definition is to avoid circularity, judgments of equiprobability must appeal to some principle of symmetry or indifference.
If we say “series,” we have the frequency definition of probability, in terms of a sequence of events, whether real or ideally constructed. The frequency definition is often confused with the classical, but the distinction is important. The frequency conception is regarded by its proponents as having the advantage, over the classical, of being empirical; actuarial probabilities based on mortality statistics are the cardinal example. The classical ratio, in contrast, is based on an a priori model (e.g., of a fair coin). Either definition presupposes a random process (rolling a die, deaths from the plague), with the probability of an event either assumed or estimated from data; calculations then concern probabilities of particular outcomes of the random process.
Probability, in addition to being conceived as a relative frequency, can also refer to a degree of expectation or belief. Here again the interpretations divide. The logical version takes probability to be a degree of rational belief, or the degree to which a body of evidence warrants a particular assertion. The personalist theory, also known as the subjectivist theory, purports to take probability as an individual’s degree of belief, though it imposes constraints, like additivity (The probabilities of a proposition and of its denial must add to 1.), on any system of beliefs, or belief coefficients, to make them behave like classical probabilities. Probability on these epistemic theories is not limited to random processes but pertains to uncertainty from whatever source. There are other interpretations, too; a more recent one takes probability to be a physical propensity of a device or set-up to display a certain outcome.
统计推断学代写
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|THE PLACE OF STATISTICS IN THE SOCIAL SCIENCES
70 多年前，WH Auden1966,p.225在哈佛的毕业典礼演讲中发布了这条诫命；而且，至少从 JW Tankard 开始1984在他的《统计先驱》一书中引用了这首 Phi Beta Kappa 诗，很少有社会科学统计评论家能够拒绝重复它。奥登担心的理由已经很明显：大约在同一时间，莫里斯·肯德尔1942在评论统计学家日益增长的影响力时，他观察到，“作为一个只关注政治经济学的小教派，他们已经以只有阿提拉、穆罕默德和科罗拉多甲虫可以匹敌的速度征服了科学的每一个分支”p.69. 具有讽刺意味的是，肯德尔在二战后统计数据爆炸式增长之前做出了他的观察。随着电子计算机的出现和太空时代对研究和高等教育的资助，他的比较早已变得毫无意义。社会科学家不仅在征服速度方面，而且在帝国的广度方面，都让阿提拉感到羞耻。1942 年，肯德尔写作时，只有29%的医学院教授统计学，但到 1952 年，这个数字是82% 米一种r到s,1997,p.137,n.5. 医学是统计学征服最激烈的领域。
就在第二次世界大战之前开始，在 1920 年代和 1930 年代发展起来的新的统计推断理论改变了社会科学和邻近学科。虽然，关于应用问题，我将重点关注心理学，我的培训和经验领域，其次是医学，但教育、社会学、农业和经济学领域也受到了同样的影响。旨在调查某些治疗是否具有“效果”的问题的比较实验，现在被假定在统计学上具有对这些问题的“是”或“否”答案的客观标准，并且在这些领域的研究中，它的现代形式，如雨后春笋般涌现1940 s和1950 s部分原因。统计学课程强调推论而非描述性技术，如今已成为这些学科几乎所有大学的一项要求。因此，在过去的 70 年中，统计推断几乎被普遍认为是社会科学研究的支柱。它的垄断得到了不祥的保证，并得到了证明，因为它现在对这些领域可以提出的研究问题至关重要，而且很少有从业者可以再设想替代方案。
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|STATISTICAL INFERENCE AND ITS MISCONCEPTIONS
随着 Oakes 的预先测试的结束，是时候通过非正式地澄清统计推断的含义并简要介绍一些批评来具体化本文的最初重点了。为简单起见，我将在本章中仅讨论显着性检验；统计推断所包含的其他技术和.G.,C这nF一世d和nC和一世n吨和rv一种一世s我会在适当的时候到达。
在典型的显着性检验中，我们可能想确定我们的治疗是否有效。现在可以假设我们可以只查看我们的数据，看看我们的处理数据和控制数据之间是否存在差异。但是当心理学家谈到治疗效果时，他们谈论的是原则上无法观察到的差异。我们实际上想象我们的治疗和控制数据是从这些分数的假设无限群体中随机抽样的，我们对效果的陈述与这些假设群体之间的差异有关。当我们进行显着性检验时，我们首先假设一些我们通常暂时不相信的事情——例如，治疗没有效果。我们计算的显着性水平是在总体均值没有差异的情况下抽取两个这样不同样本的概率。如果此概率超出常规截止值，我们拒绝无效的零假设。
所以这个程序的第一件奇怪的事情是它完全是假设的。除了在调查研究中，我们不随机抽样，但这是我们概率计算的基础，否则没有任何意义。
统计代写|统计推断作业代写STATISTICAL INFERENCE代考|THE ROOT OF THE PROBLEM IN THE DUALISTIC CONCEPT OF PROBABILITY
统计推断混淆的根源是概率的概念，统计推断依赖于此。从日常使用的反思中可以明显看出，概率的概念涵盖了几个方面或含义。一方面，它反映了某些事件或一系列事件的发生频率。如果我们说“集合”，我们有经典的或拉普拉斯式的赌博概率度量，即对事件有利的案例数量与等概率案例总数的比率；如果定义是为了避免循环，对等概率的判断必须诉诸某种对称或无差异原则。
如果我们说“系列”，我们就有概率的频率定义，根据一系列事件，无论是真实的还是理想构造的。频率定义经常与经典混淆，但区别很重要。频率概念被其支持者认为比经典概念具有经验性的优势。基于死亡率统计的精算概率就是主要的例子。相比之下，经典比率基于先验模型和.G.,这F一种F一种一世rC这一世n. 任何一个定义都预设了一个随机过程r这一世一世一世nG一种d一世和,d和一种吨HsFr这米吨H和p一世一种G你和，从数据中假设或估计事件的概率；然后计算涉及随机过程的特定结果的概率。
概率除了被认为是相对频率外，还可以指某种程度的期望或信念。在这里，解释又出现了分歧。逻辑版本将概率视为某种程度的理性信念，或证据主体支持特定断言的程度。个人主义理论，也称为主观主义理论，旨在将概率作为个人的信念程度，尽管它施加了约束，如可加性吨H和pr这b一种b一世一世一世吨一世和s这F一种pr这p这s一世吨一世这n一种nd这F一世吨sd和n一世一种一世米你s吨一种dd吨这1.，在任何信念系统或信念系数上，使它们表现得像经典概率。这些认知理论的概率不仅限于随机过程，还涉及来自任何来源的不确定性。还有其他解释；最近的一个将概率视为设备或设置显示特定结果的物理倾向。
统计代写|统计推断作业代写STATISTICAL INFERENCE代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。
